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Katarzyna Maciejowska

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Katarzyna Maciejowska, 2022. "A portfolio management of a small RES utility with a Structural Vector Autoregressive model of German electricity markets," Papers 2205.00975, arXiv.org.

    Cited by:

    1. Weronika Nitka & Rafał Weron, 2023. "Combining predictive distributions of electricity prices. Does minimizing the CRPS lead to optimal decisions in day-ahead bidding?," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 33(3), pages 105-118.
    2. Joanna Janczura & Andrzej Puć, 2023. "ARX-GARCH Probabilistic Price Forecasts for Diversification of Trade in Electricity Markets—Variance Stabilizing Transformation and Financial Risk-Minimizing Portfolio Allocation," Energies, MDPI, vol. 16(2), pages 1-28, January.
    3. Grzegorz Marcjasz & Micha{l} Narajewski & Rafa{l} Weron & Florian Ziel, 2022. "Distributional neural networks for electricity price forecasting," Papers 2207.02832, arXiv.org, revised Dec 2022.

  2. Bartosz Uniejewski & Katarzyna Maciejowska, 2022. "LASSO Principal Component Averaging -- a fully automated approach for point forecast pooling," Papers 2207.04794, arXiv.org.

    Cited by:

    1. Bartosz Uniejewski, 2023. "Electricity price forecasting with Smoothing Quantile Regression Averaging: Quantifying economic benefits of probabilistic forecasts," Papers 2302.00411, arXiv.org, revised Jan 2024.
    2. Weronika Nitka & Rafał Weron, 2023. "Combining predictive distributions of electricity prices. Does minimizing the CRPS lead to optimal decisions in day-ahead bidding?," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 33(3), pages 105-118.
    3. Grzegorz Marcjasz & Micha{l} Narajewski & Rafa{l} Weron & Florian Ziel, 2022. "Distributional neural networks for electricity price forecasting," Papers 2207.02832, arXiv.org, revised Dec 2022.

  3. Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020. "PCA forecast averaging - predicting day-ahead and intraday electricity prices," WORking papers in Management Science (WORMS) WORMS/20/02, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.

    Cited by:

    1. Jonathan Berrisch & Florian Ziel, 2021. "CRPS Learning," Papers 2102.00968, arXiv.org, revised Nov 2021.
    2. Micha{l} Narajewski & Florian Ziel, 2021. "Optimal bidding in hourly and quarter-hourly electricity price auctions: trading large volumes of power with market impact and transaction costs," Papers 2104.14204, arXiv.org, revised Feb 2022.
    3. Bartosz Uniejewski & Katarzyna Maciejowska, 2022. "LASSO Principal Component Averaging -- a fully automated approach for point forecast pooling," Papers 2207.04794, arXiv.org.
    4. Jonathan Berrisch & Florian Ziel, 2023. "Multivariate Probabilistic CRPS Learning with an Application to Day-Ahead Electricity Prices," Papers 2303.10019, arXiv.org, revised Feb 2024.
    5. Weronika Nitka & Tomasz Serafin & Dimitrios Sotiros, 2021. "Forecasting Electricity Prices: Autoregressive Hybrid Nearest Neighbors (ARHNN) method," WORking papers in Management Science (WORMS) WORMS/21/06, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    6. Christopher Kath & Florian Ziel, 2020. "Optimal Order Execution in Intraday Markets: Minimizing Costs in Trade Trajectories," Papers 2009.07892, arXiv.org, revised Oct 2020.
    7. Uniejewski, Bartosz & Weron, Rafał, 2021. "Regularized quantile regression averaging for probabilistic electricity price forecasting," Energy Economics, Elsevier, vol. 95(C).
    8. Grzegorz Marcjasz & Micha{l} Narajewski & Rafa{l} Weron & Florian Ziel, 2022. "Distributional neural networks for electricity price forecasting," Papers 2207.02832, arXiv.org, revised Dec 2022.
    9. Uniejewski, Bartosz & Maciejowska, Katarzyna, 2023. "LASSO principal component averaging: A fully automated approach for point forecast pooling," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1839-1852.
    10. Silvia Golia & Luigi Grossi & Matteo Pelagatti, 2022. "Machine Learning Models and Intra-Daily Market Information for the Prediction of Italian Electricity Prices," Forecasting, MDPI, vol. 5(1), pages 1-21, December.
    11. Narajewski, Michał & Ziel, Florian, 2022. "Optimal bidding in hourly and quarter-hourly electricity price auctions: Trading large volumes of power with market impact and transaction costs," Energy Economics, Elsevier, vol. 110(C).
    12. Rainer Baule & Michael Naumann, 2021. "Volatility and Dispersion of Hourly Electricity Contracts on the German Continuous Intraday Market," Energies, MDPI, vol. 14(22), pages 1-24, November.
    13. Katarzyna Maciejowska & Bartosz Uniejewski & Rafa{l} Weron, 2022. "Forecasting Electricity Prices," Papers 2204.11735, arXiv.org.
    14. Philip Beran & Arne Vogler, 2021. "Multi-Day-Ahead Electricity Price Forecasting: A Comparison of fundamental, econometric and hybrid Models," EWL Working Papers 2102, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Oct 2021.

  4. Katarzyna Maciejowska, 2019. "Assessing the impact of renewable energy sources on the electricity price level and variability - a Quantile Regression approach," HSC Research Reports HSC/19/02, Hugo Steinhaus Center, Wroclaw University of Technology.

    Cited by:

    1. Janczura, Joanna & Wójcik, Edyta, 2022. "Dynamic short-term risk management strategies for the choice of electricity market based on probabilistic forecasts of profit and risk measures. The German and the Polish market case study," Energy Economics, Elsevier, vol. 110(C).
    2. Özen, Kadir & Yıldırım, Dilem, 2021. "Application of bagging in day-ahead electricity price forecasting and factor augmentation," Energy Economics, Elsevier, vol. 103(C).
    3. Pereira, Diogo Santos & Marques, António Cardoso, 2020. "How should price-responsive electricity tariffs evolve? An analysis of the German net demand case," Utilities Policy, Elsevier, vol. 66(C).
    4. Grzegorz Marcjasz & Bartosz Uniejewski & Rafał Weron, 2020. "Beating the Naïve—Combining LASSO with Naïve Intraday Electricity Price Forecasts," Energies, MDPI, vol. 13(7), pages 1-16, April.
    5. Katarzyna Maciejowska, 2022. "A portfolio management of a small RES utility with a Structural Vector Autoregressive model of German electricity markets," Papers 2205.00975, arXiv.org.
    6. Michał Narajewski, 2022. "Probabilistic Forecasting of German Electricity Imbalance Prices," Energies, MDPI, vol. 15(14), pages 1-17, July.
    7. Jesus Lago & Grzegorz Marcjasz & Bart De Schutter & Rafa{l} Weron, 2020. "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," Papers 2008.08004, arXiv.org, revised Dec 2020.
    8. Danlu Xu & Zhoubin Liu & Rui Shan & Haixiao Weng & Haoyu Zhang, 2023. "How a Grid Company Could Enter the Hydrogen Industry through a New Business Model: A Case Study in China," Sustainability, MDPI, vol. 15(5), pages 1-21, March.
    9. Andr s Oviedo-G mez & Sandra Milena Londo o-Hern ndez & Diego Fernando Manotas-Duque, 2021. "Electricity Price Fundamentals in Hydrothermal Power Generation Markets Using Machine Learning and Quantile Regression Analysis," International Journal of Energy Economics and Policy, Econjournals, vol. 11(5), pages 66-77.
    10. Bartosz Uniejewski & Katarzyna Maciejowska, 2022. "LASSO Principal Component Averaging -- a fully automated approach for point forecast pooling," Papers 2207.04794, arXiv.org.
    11. Tselika, Kyriaki, 2022. "The impact of variable renewables on the distribution of hourly electricity prices and their variability: A panel approach," Energy Economics, Elsevier, vol. 113(C).
    12. Matheus Henrique Dal Molin Ribeiro & Stéfano Frizzo Stefenon & José Donizetti de Lima & Ademir Nied & Viviana Cocco Mariani & Leandro dos Santos Coelho, 2020. "Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning," Energies, MDPI, vol. 13(19), pages 1-22, October.
    13. Neeraj Kumar & Madan Mohan Tripathi & Saket Gupta & Majed A. Alotaibi & Hasmat Malik & Asyraf Afthanorhan, 2023. "Study of Potential Impact of Wind Energy on Electricity Price Using Regression Techniques," Sustainability, MDPI, vol. 15(19), pages 1-17, October.
    14. Jakub Jurasz & Jerzy Mikulik & Paweł B. Dąbek & Mohammed Guezgouz & Bartosz Kaźmierczak, 2021. "Complementarity and ‘Resource Droughts’ of Solar and Wind Energy in Poland: An ERA5-Based Analysis," Energies, MDPI, vol. 14(4), pages 1-24, February.
    15. Jozef Barunik & Lubos Hanus, 2023. "Learning Probability Distributions of Day-Ahead Electricity Prices," Papers 2310.02867, arXiv.org, revised Oct 2023.
    16. Daniela Pereira Macedo & António Cardoso Marques & Olivier Damette, 2021. "The Merit-Order Effect on the Swedish bidding zone with the highest electricity flow in the Elspot market," Post-Print hal-03726391, HAL.
    17. Micha{l} Narajewski & Florian Ziel, 2020. "Ensemble Forecasting for Intraday Electricity Prices: Simulating Trajectories," Papers 2005.01365, arXiv.org, revised Aug 2020.
    18. Mwampashi, Muthe Mathias & Nikitopoulos, Christina Sklibosios & Rai, Alan & Konstandatos, Otto, 2022. "Large-scale and rooftop solar generation in the NEM: A tale of two renewables strategies," Energy Economics, Elsevier, vol. 115(C).
    19. Macedo, Daniela Pereira & Marques, António Cardoso & Damette, Olivier, 2022. "The role of electricity flows and renewable electricity production in the behaviour of electricity prices in Spain," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 885-900.
    20. Uniejewski, Bartosz & Weron, Rafał, 2021. "Regularized quantile regression averaging for probabilistic electricity price forecasting," Energy Economics, Elsevier, vol. 95(C).
    21. Hakan Acaroğlu & Fausto Pedro García Márquez, 2021. "Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy," Energies, MDPI, vol. 14(22), pages 1-23, November.
    22. Fang, Guochang & Tian, Lixin & Yang, Zili, 2020. "The construction of a comprehensive multidimensional energy index," Energy Economics, Elsevier, vol. 90(C).
    23. Joanna Janczura & Andrzej Puć, 2023. "ARX-GARCH Probabilistic Price Forecasts for Diversification of Trade in Electricity Markets—Variance Stabilizing Transformation and Financial Risk-Minimizing Portfolio Allocation," Energies, MDPI, vol. 16(2), pages 1-28, January.
    24. Bonaldo, Cinzia & Caporin, Massimiliano & Fontini, Fulvio, 2022. "The relationship between day-ahead and future prices in electricity markets: An empirical analysis on Italy, France, Germany, and Switzerland," Energy Economics, Elsevier, vol. 110(C).
    25. Micha{l} Narajewski, 2022. "Probabilistic forecasting of German electricity imbalance prices," Papers 2205.11439, arXiv.org.
    26. Petersen, Claire & Reguant, Mar & Segura, Lola, 2024. "Measuring the impact of wind power and intermittency," Energy Economics, Elsevier, vol. 129(C).
    27. Tian, Yuyu & Chang, Jianxia & Wang, Yimin & Wang, Xuebin & Zhao, Mingzhe & Meng, Xuejiao & Guo, Aijun, 2022. "A method of short-term risk and economic dispatch of the hydro-thermal-wind-PV hybrid system considering spinning reserve requirements," Applied Energy, Elsevier, vol. 328(C).
    28. Hunjra, Ahmed Imran & Azam, Muhammad & Bruna, Maria Giuseppina & Verhoeven, Peter & Al-Faryan, Mamdouh Abdulaziz Saleh, 2022. "Sustainable development: The impact of political risk, macroeconomic policy uncertainty and ethnic conflict," International Review of Financial Analysis, Elsevier, vol. 84(C).
    29. Maniatis, Georgios I. & Milonas, Nikolaos T., 2022. "The impact of wind and solar power generation on the level and volatility of wholesale electricity prices in Greece," Energy Policy, Elsevier, vol. 170(C).
    30. Grzegorz Marcjasz & Micha{l} Narajewski & Rafa{l} Weron & Florian Ziel, 2022. "Distributional neural networks for electricity price forecasting," Papers 2207.02832, arXiv.org, revised Dec 2022.
    31. Sirin, Selahattin Murat & Yilmaz, Berna N., 2020. "Variable renewable energy technologies in the Turkish electricity market: Quantile regression analysis of the merit-order effect," Energy Policy, Elsevier, vol. 144(C).
    32. Muthe Mathias Mwampashi & Christina Sklibosios Nikitopoulos & Otto Konstandatos & Alan Rai, 2020. "Wind Generation and the Dynamics of Electricity Prices in Australia," Research Paper Series 416, Quantitative Finance Research Centre, University of Technology, Sydney.
    33. Luo, Yusen & Lu, Zhengnan & Long, Xingle, 2020. "Heterogeneous effects of endogenous and foreign innovation on CO2 emissions stochastic convergence across China," Energy Economics, Elsevier, vol. 91(C).
    34. Huisman, Ronald & Stet, Cristian, 2022. "The dependence of quantile power prices on supply from renewables," Energy Economics, Elsevier, vol. 105(C).
    35. Christopher Kath & Weronika Nitka & Tomasz Serafin & Tomasz Weron & Przemysław Zaleski & Rafał Weron, 2020. "Balancing Generation from Renewable Energy Sources: Profitability of an Energy Trader," Energies, MDPI, vol. 13(1), pages 1-15, January.
    36. Peña, Juan Ignacio & Rodríguez, Rosa & Mayoral, Silvia, 2022. "Cannibalization, depredation, and market remuneration of power plants," Energy Policy, Elsevier, vol. 167(C).
    37. Venkatraman Indrajayanthan & Nalin Kant Mohanty & Rajvikram Madurai Elavarasan & Lucian Mihet-Popa, 2022. "Investigation on Current and Prospective Energy Transition Scenarios in Indian Landscape Using Integrated SWOT-MCDA Methodology," Sustainability, MDPI, vol. 14(9), pages 1-31, April.
    38. Christopher Kath & Weronika Nitka & Tomasz Serafin & Tomasz Weron & Przemyslaw Zaleski & Rafal Weron, 2019. "Balancing RES generation: Profitability of an energy trader," HSC Research Reports HSC/19/07, Hugo Steinhaus Center, Wroclaw University of Technology.
    39. Uniejewski, Bartosz & Maciejowska, Katarzyna, 2023. "LASSO principal component averaging: A fully automated approach for point forecast pooling," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1839-1852.
    40. Kraschewski, Tobias & Brauner, Tim & Heumann, Maximilian & Breitner, Michael H., 2023. "Disentangle the price dispersion of residential solar photovoltaic systems: Evidence from Germany," Energy Economics, Elsevier, vol. 121(C).
    41. Sirin, Selahattin Murat & Yilmaz, Berna N., 2021. "The impact of variable renewable energy technologies on electricity markets: An analysis of the Turkish balancing market," Energy Policy, Elsevier, vol. 151(C).
    42. Sakaguchi, Makishi & Fujii, Hidemichi, 2021. "The impact of variable renewable energy penetration on wholesale electricity prices in Japan," MPRA Paper 110554, University Library of Munich, Germany.
    43. Narajewski, Michał & Ziel, Florian, 2020. "Ensemble forecasting for intraday electricity prices: Simulating trajectories," Applied Energy, Elsevier, vol. 279(C).
    44. Katarzyna Maciejowska & Bartosz Uniejewski & Rafa{l} Weron, 2022. "Forecasting Electricity Prices," Papers 2204.11735, arXiv.org.

  5. Katarzyna Maciejowska & Weronika Nitka & Tomasz Weron, 2019. "Enhancing load, wind and solar generation forecasts in day-ahead forecasting of spot and intraday electricity prices," HSC Research Reports HSC/19/08, Hugo Steinhaus Center, Wroclaw University of Technology.

    Cited by:

    1. Christopher Kath & Weronika Nitka & Tomasz Serafin & Tomasz Weron & Przemysław Zaleski & Rafał Weron, 2020. "Balancing Generation from Renewable Energy Sources: Profitability of an Energy Trader," Energies, MDPI, vol. 13(1), pages 1-15, January.
    2. Christopher Kath & Weronika Nitka & Tomasz Serafin & Tomasz Weron & Przemyslaw Zaleski & Rafal Weron, 2019. "Balancing RES generation: Profitability of an energy trader," HSC Research Reports HSC/19/07, Hugo Steinhaus Center, Wroclaw University of Technology.

  6. Katarzyna Maciejowska & Rafal Weron, 2019. "Electricity price forecasting," HSC Research Reports HSC/19/01, Hugo Steinhaus Center, Wroclaw University of Technology.

    Cited by:

    1. Brusaferri, Alessandro & Matteucci, Matteo & Portolani, Pietro & Vitali, Andrea, 2019. "Bayesian deep learning based method for probabilistic forecast of day-ahead electricity prices," Applied Energy, Elsevier, vol. 250(C), pages 1158-1175.
    2. Janczura, Joanna & Wójcik, Edyta, 2022. "Dynamic short-term risk management strategies for the choice of electricity market based on probabilistic forecasts of profit and risk measures. The German and the Polish market case study," Energy Economics, Elsevier, vol. 110(C).
    3. Maryniak, Paweł & Trück, Stefan & Weron, Rafał, 2019. "Carbon pricing and electricity markets — The case of the Australian Clean Energy Bill," Energy Economics, Elsevier, vol. 79(C), pages 45-58.
    4. Özen, Kadir & Yıldırım, Dilem, 2021. "Application of bagging in day-ahead electricity price forecasting and factor augmentation," Energy Economics, Elsevier, vol. 103(C).
    5. Umut Ugurlu & Oktay Tas & Aycan Kaya & Ilkay Oksuz, 2018. "The Financial Effect of the Electricity Price Forecasts’ Inaccuracy on a Hydro-Based Generation Company," Energies, MDPI, vol. 11(8), pages 1-19, August.
    6. Lu, Xin & Qiu, Jing & Lei, Gang & Zhu, Jianguo, 2022. "Scenarios modelling for forecasting day-ahead electricity prices: Case studies in Australia," Applied Energy, Elsevier, vol. 308(C).
    7. Derek W. Bunn & Angelica Gianfreda & Stefan Kermer, 2018. "A Trading-Based Evaluation of Density Forecasts in a Real-Time Electricity Market," Energies, MDPI, vol. 11(10), pages 1-13, October.
    8. Croonenbroeck, Carsten & Stadtmann, Georg, 2019. "Renewable generation forecast studies – Review and good practice guidance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 312-322.
    9. Raimund M. Kovacevic, 2019. "Arbitrage conditions for electricity markets with production and storage," Computational Management Science, Springer, vol. 16(4), pages 671-696, October.
    10. Narajewski, Michał & Ziel, Florian, 2020. "Econometric modelling and forecasting of intraday electricity prices," Journal of Commodity Markets, Elsevier, vol. 19(C).
    11. Tim Janke & Florian Steinke, 2019. "Forecasting the Price Distribution of Continuous Intraday Electricity Trading," Energies, MDPI, vol. 12(22), pages 1-14, November.
    12. Grzegorz Marcjasz & Bartosz Uniejewski & Rafał Weron, 2020. "Beating the Naïve—Combining LASSO with Naïve Intraday Electricity Price Forecasts," Energies, MDPI, vol. 13(7), pages 1-16, April.
    13. Kannika Duangnate & James W. Mjelde, 2020. "Prequential forecasting in the presence of structure breaks in natural gas spot markets," Empirical Economics, Springer, vol. 59(5), pages 2363-2384, November.
    14. Xiaoming Xie & Meiping Li & Du Zhang, 2021. "A Multiscale Electricity Price Forecasting Model Based on Tensor Fusion and Deep Learning," Energies, MDPI, vol. 14(21), pages 1-14, November.
    15. Dumas, Jonathan & Wehenkel, Antoine & Lanaspeze, Damien & Cornélusse, Bertrand & Sutera, Antonio, 2022. "A deep generative model for probabilistic energy forecasting in power systems: normalizing flows," Applied Energy, Elsevier, vol. 305(C).
    16. Shao, Zhen & Zheng, Qingru & Yang, Shanlin & Gao, Fei & Cheng, Manli & Zhang, Qiang & Liu, Chen, 2020. "Modeling and forecasting the electricity clearing price: A novel BELM based pattern classification framework and a comparative analytic study on multi-layer BELM and LSTM," Energy Economics, Elsevier, vol. 86(C).
    17. Riccardo De Blasis & Giovanni Batista Masala & Filippo Petroni, 2021. "A Multivariate High-Order Markov Model for the Income Estimation of a Wind Farm," Energies, MDPI, vol. 14(2), pages 1-16, January.
    18. Arne Vogler & Florian Ziel, "undated". "On The Evaluation Of Binary Event Probability Predictions In Electricity Price Forecasting," EWL Working Papers 1911, University of Duisburg-Essen, Chair for Management Science and Energy Economics.
    19. Maciejowska, Katarzyna & Nitka, Weronika & Weron, Tomasz, 2021. "Enhancing load, wind and solar generation for day-ahead forecasting of electricity prices," Energy Economics, Elsevier, vol. 99(C).
    20. Zoran Gligorić & Svetlana Štrbac Savić & Aleksandra Grujić & Milanka Negovanović & Omer Musić, 2018. "Short-Term Electricity Price Forecasting Model Using Interval-Valued Autoregressive Process," Energies, MDPI, vol. 11(7), pages 1-17, July.
    21. Oliver Grothe & Fabian Kachele & Fabian Kruger, 2022. "From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecasting," Papers 2204.10154, arXiv.org.
    22. Katarzyna Maciejowska, 2022. "A portfolio management of a small RES utility with a Structural Vector Autoregressive model of German electricity markets," Papers 2205.00975, arXiv.org.
    23. Michał Narajewski, 2022. "Probabilistic Forecasting of German Electricity Imbalance Prices," Energies, MDPI, vol. 15(14), pages 1-17, July.
    24. Ekaterina Abramova & Derek Bunn, 2021. "Optimal Daily Trading of Battery Operations Using Arbitrage Spreads," Energies, MDPI, vol. 14(16), pages 1-23, August.
    25. Jesus Lago & Grzegorz Marcjasz & Bart De Schutter & Rafa{l} Weron, 2020. "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," Papers 2008.08004, arXiv.org, revised Dec 2020.
    26. Kin G. Olivares & Cristian Challu & Grzegorz Marcjasz & Rafal Weron & Artur Dubrawski, 2021. "Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx," WORking papers in Management Science (WORMS) WORMS/21/07, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    27. Lu, Renzhi & Bai, Ruichang & Huang, Yuan & Li, Yuting & Jiang, Junhui & Ding, Yuemin, 2021. "Data-driven real-time price-based demand response for industrial facilities energy management," Applied Energy, Elsevier, vol. 283(C).
    28. F. Cordoni, 2020. "A comparison of modern deep neural network architectures for energy spot price forecasting," Digital Finance, Springer, vol. 2(3), pages 189-210, December.
    29. Maciej Kostrzewski & Jadwiga Kostrzewska, 2021. "The Impact of Forecasting Jumps on Forecasting Electricity Prices," Energies, MDPI, vol. 14(2), pages 1-17, January.
    30. Stephen Haben & Julien Caudron & Jake Verma, 2021. "Probabilistic Day-Ahead Wholesale Price Forecast: A Case Study in Great Britain," Forecasting, MDPI, vol. 3(3), pages 1-37, August.
    31. Javier Pórtoles & Camino González & Javier M. Moguerza, 2018. "Electricity Price Forecasting with Dynamic Trees: A Benchmark Against the Random Forest Approach," Energies, MDPI, vol. 11(6), pages 1-21, June.
    32. Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco, 2023. "Forecasting electricity prices with expert, linear, and nonlinear models," International Journal of Forecasting, Elsevier, vol. 39(2), pages 570-586.
    33. Micha{l} Narajewski & Florian Ziel, 2021. "Optimal bidding in hourly and quarter-hourly electricity price auctions: trading large volumes of power with market impact and transaction costs," Papers 2104.14204, arXiv.org, revised Feb 2022.
    34. Bartosz Uniejewski & Katarzyna Maciejowska, 2022. "LASSO Principal Component Averaging -- a fully automated approach for point forecast pooling," Papers 2207.04794, arXiv.org.
    35. Raimund M. Kovacevic, 2019. "Valuation and pricing of electricity delivery contracts: the producer’s view," Annals of Operations Research, Springer, vol. 275(2), pages 421-460, April.
    36. Ismail Shah & Francesco Lisi, 2020. "Forecasting of electricity price through a functional prediction of sale and purchase curves," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 242-259, March.
    37. Uniejewski, Bartosz & Marcjasz, Grzegorz & Weron, Rafał, 2019. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting: Part II — Probabilistic forecasting," Energy Economics, Elsevier, vol. 79(C), pages 171-182.
    38. Roberto Baviera & Pietro Manzoni, 2022. "Tree-Based Learning in RNNs for Power Consumption Forecasting," Papers 2209.01378, arXiv.org.
    39. Mohamed Lotfi & Mohammad Javadi & Gerardo J. Osório & Cláudio Monteiro & João P. S. Catalão, 2020. "A Novel Ensemble Algorithm for Solar Power Forecasting Based on Kernel Density Estimation," Energies, MDPI, vol. 13(1), pages 1-19, January.
    40. Ismail Shah & Hasnain Iftikhar & Sajid Ali & Depeng Wang, 2019. "Short-Term Electricity Demand Forecasting Using Components Estimation Technique," Energies, MDPI, vol. 12(13), pages 1-17, July.
    41. Taylor, James W., 2021. "Evaluating quantile-bounded and expectile-bounded interval forecasts," International Journal of Forecasting, Elsevier, vol. 37(2), pages 800-811.
    42. Rodrigo A. de Marcos & Antonio Bello & Javier Reneses, 2019. "Short-Term Electricity Price Forecasting with a Composite Fundamental-Econometric Hybrid Methodology," Energies, MDPI, vol. 12(6), pages 1-15, March.
    43. John Boland & Adrian Grantham, 2018. "Nonparametric Conditional Heteroscedastic Hourly Probabilistic Forecasting of Solar Radiation," J, MDPI, vol. 1(1), pages 1-18, December.
    44. Muniain, Peru & Ziel, Florian, 2020. "Probabilistic forecasting in day-ahead electricity markets: Simulating peak and off-peak prices," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1193-1210.
    45. Antonello Rosato & Rodolfo Araneo & Amedeo Andreotti & Federico Succetti & Massimo Panella, 2021. "2-D Convolutional Deep Neural Network for the Multivariate Prediction of Photovoltaic Time Series," Energies, MDPI, vol. 14(9), pages 1-18, April.
    46. Zhang, Jinliang & Tan, Zhongfu & Wei, Yiming, 2020. "An adaptive hybrid model for short term electricity price forecasting," Applied Energy, Elsevier, vol. 258(C).
    47. Pedregal, Diego J. & Trapero, Juan R., 2021. "Adjusted combination of moving averages: A forecasting system for medium-term solar irradiance," Applied Energy, Elsevier, vol. 298(C).
    48. Sharifzadeh, Mahdi & Sikinioti-Lock, Alexandra & Shah, Nilay, 2019. "Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 513-538.
    49. Weronika Nitka & Tomasz Serafin & Dimitrios Sotiros, 2021. "Forecasting Electricity Prices: Autoregressive Hybrid Nearest Neighbors (ARHNN) method," WORking papers in Management Science (WORMS) WORMS/21/06, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    50. Carlo Fezzi & Luca Mosetti, 2018. "Size matters: Estimation sample length and electricity price forecasting accuracy," DEM Working Papers 2018/10, Department of Economics and Management.
    51. Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
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  7. Katarzyna Maciejowska & Arkadiusz Jedrzejewski & Anna Kowalska-Pyzalska & Rafal Weron, 2016. "Impact of social interactions on demand curves for innovative products," HSC Research Reports HSC/16/04, Hugo Steinhaus Center, Wroclaw University of Technology.

    Cited by:

    1. Paul R. Nail & Katarzyna Sznajd-Weron, 2016. "The diamond model of social response within an agent-based approach," HSC Research Reports HSC/16/02, Hugo Steinhaus Center, Wroclaw University of Technology.

  8. Katarzyna Maciejowska & Rafal Weron, 2015. "Short- and mid-term forecasting of baseload electricity prices in the UK: The impact of intra-day price relationships and market fundamentals," HSC Research Reports HSC/15/04, Hugo Steinhaus Center, Wroclaw University of Technology.

    Cited by:

    1. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    2. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2018. "Comparing the Forecasting Performances of Linear Models for Electricity Prices with High RES Penetration," Working Papers No 2/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    3. Rick Steinert & Florian Ziel, 2018. "Short- to Mid-term Day-Ahead Electricity Price Forecasting Using Futures," Papers 1801.10583, arXiv.org.
    4. Florian Ziel, 2015. "Forecasting Electricity Spot Prices using Lasso: On Capturing the Autoregressive Intraday Structure," Papers 1509.01966, arXiv.org, revised Jan 2016.

  9. Katarzyna Maciejowska & Jakub Nowotarski, 2015. "A hybrid model for GEFCom2014 probabilistic electricity price forecasting," HSC Research Reports HSC/15/06, Hugo Steinhaus Center, Wroclaw University of Technology.

    Cited by:

    1. Maryniak, Paweł & Trück, Stefan & Weron, Rafał, 2019. "Carbon pricing and electricity markets — The case of the Australian Clean Energy Bill," Energy Economics, Elsevier, vol. 79(C), pages 45-58.
    2. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2023. "Large Time‐Varying Volatility Models for Hourly Electricity Prices," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 545-573, June.
    3. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    4. Maciej Kostrzewski & Jadwiga Kostrzewska, 2021. "The Impact of Forecasting Jumps on Forecasting Electricity Prices," Energies, MDPI, vol. 14(2), pages 1-17, January.
    5. Stephen Haben & Julien Caudron & Jake Verma, 2021. "Probabilistic Day-Ahead Wholesale Price Forecast: A Case Study in Great Britain," Forecasting, MDPI, vol. 3(3), pages 1-37, August.
    6. Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco, 2023. "Forecasting electricity prices with expert, linear, and nonlinear models," International Journal of Forecasting, Elsevier, vol. 39(2), pages 570-586.
    7. Uniejewski, Bartosz & Marcjasz, Grzegorz & Weron, Rafał, 2019. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting: Part II — Probabilistic forecasting," Energy Economics, Elsevier, vol. 79(C), pages 171-182.
    8. Ekaterina Abramova & Derek Bunn, 2020. "Forecasting the Intra-Day Spread Densities of Electricity Prices," Energies, MDPI, vol. 13(3), pages 1-31, February.
    9. Muniain, Peru & Ziel, Florian, 2020. "Probabilistic forecasting in day-ahead electricity markets: Simulating peak and off-peak prices," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1193-1210.
    10. Jakub Nowotarski & Rafal Weron, 2016. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting," HSC Research Reports HSC/16/05, Hugo Steinhaus Center, Wroclaw University of Technology.
    11. Carlo Fezzi & Luca Mosetti, 2018. "Size matters: Estimation sample length and electricity price forecasting accuracy," DEM Working Papers 2018/10, Department of Economics and Management.
    12. Christian Pape & Oliver Woll & Christoph Weber, "undated". "Estimating the value of flexibility from real options: On the accuracy of hybrid electricity price models," EWL Working Papers 1804, University of Duisburg-Essen, Chair for Management Science and Energy Economics.
    13. Marcjasz, Grzegorz & Uniejewski, Bartosz & Weron, Rafał, 2020. "Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 466-479.
    14. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2018. "Comparing the Forecasting Performances of Linear Models for Electricity Prices with High RES Penetration," Working Papers No 2/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    15. Maciejowska, Katarzyna & Nowotarski, Jakub, 2016. "A hybrid model for GEFCom2014 probabilistic electricity price forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1051-1056.
    16. Avci, Ezgi & Ketter, Wolfgang & van Heck, Eric, 2018. "Managing electricity price modeling risk via ensemble forecasting: The case of Turkey," Energy Policy, Elsevier, vol. 123(C), pages 390-403.
    17. Jakub Nowotarski & Rafal Weron, 2016. "To combine or not to combine? Recent trends in electricity price forecasting," HSC Research Reports HSC/16/01, Hugo Steinhaus Center, Wroclaw University of Technology.
    18. Bartosz Uniejewski & Rafał Weron, 2018. "Efficient Forecasting of Electricity Spot Prices with Expert and LASSO Models," Energies, MDPI, vol. 11(8), pages 1-26, August.
    19. Kath, Christopher & Ziel, Florian, 2021. "Conformal prediction interval estimation and applications to day-ahead and intraday power markets," International Journal of Forecasting, Elsevier, vol. 37(2), pages 777-799.
    20. Bartosz Uniejewski, 2023. "Electricity price forecasting with Smoothing Quantile Regression Averaging: Quantifying economic benefits of probabilistic forecasts," Papers 2302.00411, arXiv.org, revised Jan 2024.
    21. Bartosz Uniejewski & Jakub Nowotarski & Rafał Weron, 2016. "Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 9(8), pages 1-22, August.
    22. Grzegorz Marcjasz & Tomasz Serafin & Rafal Weron, 2018. "Selection of calibration windows for day-ahead electricity price forecasting," HSC Research Reports HSC/18/06, Hugo Steinhaus Center, Wroclaw University of Technology.
    23. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    24. Marcjasz, Grzegorz & Uniejewski, Bartosz & Weron, Rafał, 2019. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1520-1532.
    25. Ekaterina Abramova & Derek Bunn, 2020. "Forecasting the Intra-Day Spread Densities of Electricity Prices," Papers 2002.10566, arXiv.org.
    26. Mira Watermeyer & Thomas Mobius & Oliver Grothe & Felix Musgens, 2023. "A hybrid model for day-ahead electricity price forecasting: Combining fundamental and stochastic modelling," Papers 2304.09336, arXiv.org.
    27. Uniejewski, Bartosz & Weron, Rafał, 2021. "Regularized quantile regression averaging for probabilistic electricity price forecasting," Energy Economics, Elsevier, vol. 95(C).
    28. Bartosz Uniejewski & Grzegorz Marcjasz & Rafal Weron, 2018. "Understanding intraday electricity markets: Variable selection and very short-term price forecasting using LASSO," HSC Research Reports HSC/18/07, Hugo Steinhaus Center, Wroclaw University of Technology.
    29. Lehna, Malte & Scheller, Fabian & Herwartz, Helmut, 2022. "Forecasting day-ahead electricity prices: A comparison of time series and neural network models taking external regressors into account," Energy Economics, Elsevier, vol. 106(C).
    30. Peru Muniain & Florian Ziel, 2018. "Probabilistic Forecasting in Day-Ahead Electricity Markets: Simulating Peak and Off-Peak Prices," Papers 1810.08418, arXiv.org, revised Dec 2019.
    31. Florian Ziel & Rafal Weron, 2016. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate models," HSC Research Reports HSC/16/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    32. Ziel, Florian & Steinert, Rick, 2018. "Probabilistic mid- and long-term electricity price forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 251-266.
    33. Kostrzewski, Maciej & Kostrzewska, Jadwiga, 2019. "Probabilistic electricity price forecasting with Bayesian stochastic volatility models," Energy Economics, Elsevier, vol. 80(C), pages 610-620.
    34. Kath, Christopher & Ziel, Florian, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Energy Economics, Elsevier, vol. 76(C), pages 411-423.
    35. Grzegorz Marcjasz & Micha{l} Narajewski & Rafa{l} Weron & Florian Ziel, 2022. "Distributional neural networks for electricity price forecasting," Papers 2207.02832, arXiv.org, revised Dec 2022.
    36. Claudio Monteiro & Ignacio J. Ramirez-Rosado & L. Alfredo Fernandez-Jimenez, 2018. "Probabilistic Electricity Price Forecasting Models by Aggregation of Competitive Predictors," Energies, MDPI, vol. 11(5), pages 1-25, April.
    37. Tomasz Serafin & Bartosz Uniejewski & Rafal Weron, 2019. "Averaging predictive distributions across calibration windows for day-ahead electricity price forecasting," WORking papers in Management Science (WORMS) WORMS/19/08, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, revised 06 Jul 2019.
    38. Serafin, Tomasz & Marcjasz, Grzegorz & Weron, Rafał, 2022. "Trading on short-term path forecasts of intraday electricity prices," Energy Economics, Elsevier, vol. 112(C).
    39. Emma Viviani & Luca Di Persio & Matthias Ehrhardt, 2021. "Energy Markets Forecasting. From Inferential Statistics to Machine Learning: The German Case," Energies, MDPI, vol. 14(2), pages 1-33, January.
    40. Ziel, Florian, 2019. "Quantile regression for the qualifying match of GEFCom2017 probabilistic load forecasting," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1400-1408.
    41. Yang, Dongchuan & Guo, Ju-e & Sun, Shaolong & Han, Jing & Wang, Shouyang, 2022. "An interval decomposition-ensemble approach with data-characteristic-driven reconstruction for short-term load forecasting," Applied Energy, Elsevier, vol. 306(PA).
    42. Ziel, Florian & Weron, Rafał, 2018. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks," Energy Economics, Elsevier, vol. 70(C), pages 396-420.
    43. Katarzyna Maciejowska & Bartosz Uniejewski & Rafa{l} Weron, 2022. "Forecasting Electricity Prices," Papers 2204.11735, arXiv.org.

  10. Katarzyna Maciejowska, 2014. "Fundamental and speculative shocks, what drives electricity prices?," HSC Research Reports HSC/14/05, Hugo Steinhaus Center, Wroclaw University of Technology.

    Cited by:

    1. Weron, Rafał & Zator, Michał, 2015. "A note on using the Hodrick–Prescott filter in electricity markets," Energy Economics, Elsevier, vol. 48(C), pages 1-6.
    2. Katarzyna Maciejowska, 2022. "A portfolio management of a small RES utility with a Structural Vector Autoregressive model of German electricity markets," Papers 2205.00975, arXiv.org.
    3. Rafal Weron & Michal Zator, 2013. "Revisiting the relationship between spot and futures prices in the Nord Pool electricity market," HSC Research Reports HSC/13/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    4. Jakub Nowotarski & Rafal Weron, 2016. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting," HSC Research Reports HSC/16/05, Hugo Steinhaus Center, Wroclaw University of Technology.
    5. Avci, Ezgi & Ketter, Wolfgang & van Heck, Eric, 2018. "Managing electricity price modeling risk via ensemble forecasting: The case of Turkey," Energy Policy, Elsevier, vol. 123(C), pages 390-403.
    6. Bartosz Uniejewski & Jakub Nowotarski & Rafał Weron, 2016. "Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 9(8), pages 1-22, August.
    7. Di Cosmo, Valeria & Malaguzzi Valeri, Laura, 2018. "Wind, storage, interconnection and the cost of electricity generation," Energy Economics, Elsevier, vol. 69(C), pages 1-18.
    8. Keles, Dogan & Dehler-Holland, Joris & Densing, Martin & Panos, Evangelos & Hack, Felix, 2020. "Cross-border effects in interconnected electricity markets - an analysis of the Swiss electricity prices," Energy Economics, Elsevier, vol. 90(C).
    9. Miguel Pinhão & Miguel Fonseca & Ricardo Covas, 2022. "Electricity Spot Price Forecast by Modelling Supply and Demand Curve," Mathematics, MDPI, vol. 10(12), pages 1-20, June.
    10. Rafal Weron, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," HSC Research Reports HSC/14/07, Hugo Steinhaus Center, Wroclaw University of Technology.
    11. Florian Ziel & Rafal Weron, 2016. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate models," HSC Research Reports HSC/16/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    12. Paschen, Marius, 2016. "Dynamic analysis of the German day-ahead electricity spot market," Energy Economics, Elsevier, vol. 59(C), pages 118-128.
    13. Alexandre Lucas & Konstantinos Pegios & Evangelos Kotsakis & Dan Clarke, 2020. "Price Forecasting for the Balancing Energy Market Using Machine-Learning Regression," Energies, MDPI, vol. 13(20), pages 1-16, October.
    14. Ziel, Florian & Weron, Rafał, 2018. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks," Energy Economics, Elsevier, vol. 70(C), pages 396-420.
    15. Valeria Di Cosmo & Laura Malaguzzi Valeri, 2016. "Wind, storage, interconnection and the cost of electricity," Working Papers 2016/30, Institut d'Economia de Barcelona (IEB).

  11. Katarzyna Maciejowska & Jakub Nowotarski & Rafal Weron, 2014. "Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging," HSC Research Reports HSC/14/09, Hugo Steinhaus Center, Wroclaw University of Technology.

    Cited by:

    1. Özen, Kadir & Yıldırım, Dilem, 2021. "Application of bagging in day-ahead electricity price forecasting and factor augmentation," Energy Economics, Elsevier, vol. 103(C).
    2. Vijay, Avinash & Fouquet, Nicolas & Staffell, Iain & Hawkes, Adam, 2017. "The value of electricity and reserve services in low carbon electricity systems," Applied Energy, Elsevier, vol. 201(C), pages 111-123.
    3. Derek W. Bunn & Angelica Gianfreda & Stefan Kermer, 2018. "A Trading-Based Evaluation of Density Forecasts in a Real-Time Electricity Market," Energies, MDPI, vol. 11(10), pages 1-13, October.
    4. Renato Fernandes & Isabel Soares, 2022. "Reviewing Explanatory Methodologies of Electricity Markets: An Application to the Iberian Market," Energies, MDPI, vol. 15(14), pages 1-17, July.
    5. Nowotarski, Jakub & Raviv, Eran & Trück, Stefan & Weron, Rafał, 2014. "An empirical comparison of alternative schemes for combining electricity spot price forecasts," Energy Economics, Elsevier, vol. 46(C), pages 395-412.
    6. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    7. Alexander, Carol & Han, Yang & Meng, Xiaochun, 2023. "Static and dynamic models for multivariate distribution forecasts: Proper scoring rule tests of factor-quantile versus multivariate GARCH models," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1078-1096.
    8. Maciejowska, Katarzyna, 2020. "Assessing the impact of renewable energy sources on the electricity price level and variability – A quantile regression approach," Energy Economics, Elsevier, vol. 85(C).
    9. Ulrich, Matthias & Jahnke, Hermann & Langrock, Roland & Pesch, Robert & Senge, Robin, 2022. "Classification-based model selection in retail demand forecasting," International Journal of Forecasting, Elsevier, vol. 38(1), pages 209-223.
    10. Michał Narajewski, 2022. "Probabilistic Forecasting of German Electricity Imbalance Prices," Energies, MDPI, vol. 15(14), pages 1-17, July.
    11. Shao, Zhen & Yang, ShanLin & Gao, Fei & Zhou, KaiLe & Lin, Peng, 2017. "A new electricity price prediction strategy using mutual information-based SVM-RFE classification," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 330-341.
    12. Sheybanivaziri, Samaneh & Le Dréau, Jérôme & Kazmi, Hussain, 2024. "Forecasting price spikes in day-ahead electricity markets: techniques, challenges, and the road ahead," Discussion Papers 2024/1, Norwegian School of Economics, Department of Business and Management Science.
    13. Д.О. Афанасьев1 & * & Е.А. Федорова2 & **, 2019. "Краткосрочное Прогнозирование Цены Электроэнергии На Российском Рынке С Использованием Класса Моделей Scarx," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 55(1), pages 68-84, январь.
    14. Yun Duan, 2022. "A Novel Interval Energy-Forecasting Method for Sustainable Building Management Based on Deep Learning," Sustainability, MDPI, vol. 14(14), pages 1-18, July.
    15. Stephen Haben & Julien Caudron & Jake Verma, 2021. "Probabilistic Day-Ahead Wholesale Price Forecast: A Case Study in Great Britain," Forecasting, MDPI, vol. 3(3), pages 1-37, August.
    16. Raiden Skala & Mohamed Ahmed T. A. Elgalhud & Katarina Grolinger & Syed Mir, 2023. "Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging," Energies, MDPI, vol. 16(10), pages 1-21, May.
    17. Mawuli Segnon & Chi Keung Lau & Bernd Wilfling & Rangan Gupta, 2017. "Are multifractal processes suited to forecasting electricity price volatility? Evidence from Australian intraday data," CQE Working Papers 6117, Center for Quantitative Economics (CQE), University of Muenster.
    18. Agustín A. Sánchez de la Nieta & Virginia González & Javier Contreras, 2016. "Portfolio Decision of Short-Term Electricity Forecasted Prices through Stochastic Programming," Energies, MDPI, vol. 9(12), pages 1-19, December.
    19. Uniejewski, Bartosz & Marcjasz, Grzegorz & Weron, Rafał, 2019. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting: Part II — Probabilistic forecasting," Energy Economics, Elsevier, vol. 79(C), pages 171-182.
    20. Jakub Nowotarski & Rafal Weron, 2016. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting," HSC Research Reports HSC/16/05, Hugo Steinhaus Center, Wroclaw University of Technology.
    21. Luigi Grossi & Fany Nan, 2017. "Forecasting electricity prices through robust nonlinear models," Working Papers 06/2017, University of Verona, Department of Economics.
    22. Grzegorz Marcjasz & Bartosz Uniejewski & Rafal Weron, 2017. "Importance of the long-term seasonal component in day-ahead electricity price forecasting revisited: Neural network models," HSC Research Reports HSC/17/03, Hugo Steinhaus Center, Wroclaw University of Technology.
    23. Carlo Fezzi & Luca Mosetti, 2018. "Size matters: Estimation sample length and electricity price forecasting accuracy," DEM Working Papers 2018/10, Department of Economics and Management.
    24. Marcjasz, Grzegorz & Uniejewski, Bartosz & Weron, Rafał, 2020. "Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 466-479.
    25. Jozef Barunik & Lubos Hanus, 2023. "Learning Probability Distributions of Day-Ahead Electricity Prices," Papers 2310.02867, arXiv.org, revised Oct 2023.
    26. Maciejowska, Katarzyna & Nowotarski, Jakub, 2016. "A hybrid model for GEFCom2014 probabilistic electricity price forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1051-1056.
    27. Avci, Ezgi & Ketter, Wolfgang & van Heck, Eric, 2018. "Managing electricity price modeling risk via ensemble forecasting: The case of Turkey," Energy Policy, Elsevier, vol. 123(C), pages 390-403.
    28. Jakub Nowotarski & Rafal Weron, 2016. "To combine or not to combine? Recent trends in electricity price forecasting," HSC Research Reports HSC/16/01, Hugo Steinhaus Center, Wroclaw University of Technology.
    29. Bartosz Uniejewski & Rafał Weron, 2018. "Efficient Forecasting of Electricity Spot Prices with Expert and LASSO Models," Energies, MDPI, vol. 11(8), pages 1-26, August.
    30. Kath, Christopher & Ziel, Florian, 2021. "Conformal prediction interval estimation and applications to day-ahead and intraday power markets," International Journal of Forecasting, Elsevier, vol. 37(2), pages 777-799.
    31. Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020. "PCA Forecast Averaging—Predicting Day-Ahead and Intraday Electricity Prices," Energies, MDPI, vol. 13(14), pages 1-19, July.
    32. Bartosz Uniejewski, 2023. "Electricity price forecasting with Smoothing Quantile Regression Averaging: Quantifying economic benefits of probabilistic forecasts," Papers 2302.00411, arXiv.org, revised Jan 2024.
    33. Bartosz Uniejewski & Jakub Nowotarski & Rafał Weron, 2016. "Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 9(8), pages 1-22, August.
    34. He, Yaoyao & Zheng, Yaya, 2018. "Short-term power load probability density forecasting based on Yeo-Johnson transformation quantile regression and Gaussian kernel function," Energy, Elsevier, vol. 154(C), pages 143-156.
    35. Xu, Bin & Lin, Boqiang, 2016. "A quantile regression analysis of China's provincial CO2 emissions: Where does the difference lie?," Energy Policy, Elsevier, vol. 98(C), pages 328-342.
    36. Grzegorz Marcjasz & Tomasz Serafin & Rafal Weron, 2018. "Selection of calibration windows for day-ahead electricity price forecasting," HSC Research Reports HSC/18/06, Hugo Steinhaus Center, Wroclaw University of Technology.
    37. Christopher Koch & Philipp Maskos, 2020. "Passive Balancing Through Intraday Trading: Whether Interactions Between Short-term Trading and Balancing Stabilize Germany s Electricity System," International Journal of Energy Economics and Policy, Econjournals, vol. 10(2), pages 101-112.
    38. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    39. Yang, Yandong & Li, Shufang & Li, Wenqi & Qu, Meijun, 2018. "Power load probability density forecasting using Gaussian process quantile regression," Applied Energy, Elsevier, vol. 213(C), pages 499-509.
    40. Marcjasz, Grzegorz & Uniejewski, Bartosz & Weron, Rafał, 2019. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1520-1532.
    41. Marta Poncela-Blanco & Pilar Poncela, 2021. "Improving Wind Power Forecasts: Combination through Multivariate Dimension Reduction Techniques," Energies, MDPI, vol. 14(5), pages 1-16, March.
    42. Katarzyna Maciejowska & Weronika Nitka & Tomasz Weron, 2019. "Day-Ahead vs. Intraday—Forecasting the Price Spread to Maximize Economic Benefits," Energies, MDPI, vol. 12(4), pages 1-15, February.
    43. Mira Watermeyer & Thomas Mobius & Oliver Grothe & Felix Musgens, 2023. "A hybrid model for day-ahead electricity price forecasting: Combining fundamental and stochastic modelling," Papers 2304.09336, arXiv.org.
    44. Jakub Nowotarski & Rafał Weron, 2015. "Computing electricity spot price prediction intervals using quantile regression and forecast averaging," Computational Statistics, Springer, vol. 30(3), pages 791-803, September.
    45. Uniejewski, Bartosz & Weron, Rafał, 2021. "Regularized quantile regression averaging for probabilistic electricity price forecasting," Energy Economics, Elsevier, vol. 95(C).
    46. Abeer Alshejari & Vassilis S. Kodogiannis & Stavros Leonidis, 2020. "Development of Neurofuzzy Architectures for Electricity Price Forecasting," Energies, MDPI, vol. 13(5), pages 1-24, March.
    47. Li, Wei & Paraschiv, Florentina, 2022. "Modelling the evolution of wind and solar power infeed forecasts," Journal of Commodity Markets, Elsevier, vol. 25(C).
    48. Andersson, Jonas & Sheybanivaziri, Samaneh, 2023. "Probabilistic forecasting of electricity prices using an augmented LMARX-model," Discussion Papers 2023/11, Norwegian School of Economics, Department of Business and Management Science.
    49. Bidong Liu & Jakub Nowotarski & Tao Hong & Rafal Weron, 2015. "Probabilistic load forecasting via Quantile Regression Averaging on sister forecasts," HSC Research Reports HSC/15/01, Hugo Steinhaus Center, Wroclaw University of Technology.
    50. Rafal Weron, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," HSC Research Reports HSC/14/07, Hugo Steinhaus Center, Wroclaw University of Technology.
    51. Florian Ziel & Rafal Weron, 2016. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate models," HSC Research Reports HSC/16/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    52. Ziel, Florian & Steinert, Rick, 2018. "Probabilistic mid- and long-term electricity price forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 251-266.
    53. Graziani, Carlo & Rosner, Robert & Adams, Jennifer M. & Machete, Reason L., 2021. "Probabilistic recalibration of forecasts," International Journal of Forecasting, Elsevier, vol. 37(1), pages 1-27.
    54. Ziel, Florian & Steinert, Rick, 2016. "Electricity price forecasting using sale and purchase curves: The X-Model," Energy Economics, Elsevier, vol. 59(C), pages 435-454.
    55. Luigi Grossi & Fany Nan, 2018. "The influence of renewables on electricity price forecasting: a robust approach," Working Papers 2018/10, Institut d'Economia de Barcelona (IEB).
    56. Micha{l} Narajewski, 2022. "Probabilistic forecasting of German electricity imbalance prices," Papers 2205.11439, arXiv.org.
    57. Kath, Christopher & Ziel, Florian, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Energy Economics, Elsevier, vol. 76(C), pages 411-423.
    58. Grzegorz Marcjasz & Micha{l} Narajewski & Rafa{l} Weron & Florian Ziel, 2022. "Distributional neural networks for electricity price forecasting," Papers 2207.02832, arXiv.org, revised Dec 2022.
    59. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    60. Simões, Paulo Fernando Mahaz & Souza, Reinaldo Castro & Calili, Rodrigo Flora & Pessanha, José Francisco Moreira, 2020. "Analysis and short-term predictions of non-technical loss of electric power based on mixed effects models," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    61. Tryggvi Jónsson & Pierre Pinson & Henrik Madsen & Henrik Aalborg Nielsen, 2014. "Predictive Densities for Day-Ahead Electricity Prices Using Time-Adaptive Quantile Regression," Energies, MDPI, vol. 7(9), pages 1-25, August.
    62. Gunnhildur H. Steinbakk & Alex Lenkoski & Ragnar Bang Huseby & Anders L{o}land & Tor Arne {O}ig{aa}rd, 2018. "Using published bid/ask curves to error dress spot electricity price forecasts," Papers 1812.02433, arXiv.org.
    63. Huisman, Ronald & Stet, Cristian, 2022. "The dependence of quantile power prices on supply from renewables," Energy Economics, Elsevier, vol. 105(C).
    64. Zhang, Wenjie & Quan, Hao & Srinivasan, Dipti, 2018. "Parallel and reliable probabilistic load forecasting via quantile regression forest and quantile determination," Energy, Elsevier, vol. 160(C), pages 810-819.
    65. Kadir Özen & Dilem Yıldırım, 2021. "Application of Bagging in Day-Ahead Electricity Price Forecasting and Factor Augmentation," ERC Working Papers 2101, ERC - Economic Research Center, Middle East Technical University, revised Apr 2021.
    66. Grossi, Luigi & Nan, Fany, 2019. "Robust forecasting of electricity prices: Simulations, models and the impact of renewable sources," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 305-318.
    67. Ulrich, Matthias & Jahnke, Hermann & Langrock, Roland & Pesch, Robert & Senge, Robin, 2021. "Distributional regression for demand forecasting in e-grocery," European Journal of Operational Research, Elsevier, vol. 294(3), pages 831-842.
    68. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    69. He, Yaoyao & Liu, Rui & Li, Haiyan & Wang, Shuo & Lu, Xiaofen, 2017. "Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory," Applied Energy, Elsevier, vol. 185(P1), pages 254-266.
    70. Antonio Bello & Derek Bunn & Javier Reneses & Antonio Muñoz, 2016. "Parametric Density Recalibration of a Fundamental Market Model to Forecast Electricity Prices," Energies, MDPI, vol. 9(11), pages 1-15, November.
    71. Florian Ziel, 2015. "Forecasting Electricity Spot Prices using Lasso: On Capturing the Autoregressive Intraday Structure," Papers 1509.01966, arXiv.org, revised Jan 2016.
    72. Ziel, Florian & Weron, Rafał, 2018. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks," Energy Economics, Elsevier, vol. 70(C), pages 396-420.
    73. Florian Ziel & Rick Steinert, 2017. "Probabilistic Mid- and Long-Term Electricity Price Forecasting," Papers 1703.10806, arXiv.org, revised May 2018.
    74. Joanna Janczura & Aleksandra Michalak, 2020. "Optimization of Electric Energy Sales Strategy Based on Probabilistic Forecasts," Energies, MDPI, vol. 13(5), pages 1-16, February.
    75. Mosquera-López, Stephanía & Uribe, Jorge M. & Manotas-Duque, Diego Fernando, 2017. "Nonlinear empirical pricing in electricity markets using fundamental weather factors," Energy, Elsevier, vol. 139(C), pages 594-605.
    76. Martina Assereto & Julie Byrne, 2020. "The Implications of Policy Uncertainty on Solar Photovoltaic Investment," Energies, MDPI, vol. 13(23), pages 1-20, November.
    77. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.

  12. Tao Hong & Katarzyna Maciejowska & Jakub Nowotarski & Rafal Weron, 2014. "Probabilistic load forecasting via Quantile Regression Averaging of independent expert forecasts," HSC Research Reports HSC/14/10, Hugo Steinhaus Center, Wroclaw University of Technology.

    Cited by:

    1. Niematallah Elamin & Mototsugu Fukushige, 2016. "A Quantile Regression Model for Electricity Peak Demand Forecasting: An Approach to Avoiding Power Blackouts," Discussion Papers in Economics and Business 16-22, Osaka University, Graduate School of Economics.
    2. Wang, Pu & Liu, Bidong & Hong, Tao, 2016. "Electric load forecasting with recency effect: A big data approach," International Journal of Forecasting, Elsevier, vol. 32(3), pages 585-597.
    3. Niematallah Elamin & Mototsugu Fukushige, 2018. "Quantile Regression Model for Peak Load Demand Forecasting with Approximation by Triangular Distribution to Avoid Blackouts," International Journal of Energy Economics and Policy, Econjournals, vol. 8(5), pages 119-124.

  13. Anna Kowalska-Pyzalska & Katarzyna Maciejowska & Katarzyna Sznajd-Weron & Rafal Weron, 2014. "Modeling consumer opinions towards dynamic pricing: An agent-based approach," HSC Research Reports HSC/14/06, Hugo Steinhaus Center, Wroclaw University of Technology.

    Cited by:

    1. Anna Kowalska-Pyzalska & Katarzyna Maciejowska & Katarzyna Sznajd-Weron & Karol Suszczynski & Rafal Weron, 2013. "Turning green: Agent-based modeling of the adoption of dynamic electricity tariffs," HSC Research Reports HSC/13/10, Hugo Steinhaus Center, Wroclaw University of Technology.
    2. Agnieszka Kowalska-Styczeń & Krzysztof Malarz, 2020. "Noise induced unanimity and disorder in opinion formation," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-22, July.

  14. Anna Kowalska-Pyzalska & Katarzyna Maciejowska & Katarzyna Sznajd-Weron & Rafal Weron, 2013. "Going green: Agent-based modeling of the diffusion of dynamic electricity tariffs," HSC Research Reports HSC/13/05, Hugo Steinhaus Center, Wroclaw University of Technology.

    Cited by:

    1. Anna Kowalska-Pyzalska & Katarzyna Maciejowska & Katarzyna Sznajd-Weron & Karol Suszczynski & Rafal Weron, 2013. "Turning green: Agent-based modeling of the adoption of dynamic electricity tariffs," HSC Research Reports HSC/13/10, Hugo Steinhaus Center, Wroclaw University of Technology.
    2. Piotr Przybyła & Katarzyna Sznajd-Weron & Rafał Weron, 2014. "Diffusion Of Innovation Within An Agent-Based Model: Spinsons, Independence And Advertising," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 17(01), pages 1-22.
    3. McCoy, Daire & Lyons, Sean, 2014. "The diffusion of electric vehicles: An agent-based microsimulation," MPRA Paper 54560, University Library of Munich, Germany.

  15. Anna Kowalska-Pyzalska & Katarzyna Maciejowska & Katarzyna Sznajd-Weron & Karol Suszczynski & Rafal Weron, 2013. "Turning green: Agent-based modeling of the adoption of dynamic electricity tariffs," HSC Research Reports HSC/13/10, Hugo Steinhaus Center, Wroclaw University of Technology.

    Cited by:

    1. T. Balint & F. Lamperti & Antoine Mandel & Mauro Napoletano & A. Roventini & A. Sapio, 2017. "Complexity and the Economics of Climate Change: A Survey and a Look Forward," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01906003, HAL.
    2. Katarzyna Maciejowska & Arkadiusz Jedrzejewski & Anna Kowalska-Pyzalska & Katarzyna Sznajd-Weron & Rafal Weron, 2015. "Two faces of word-of-mouth: Understanding the impact of social interactions on demand curves for innovative products," HSC Research Reports HSC/15/09, Hugo Steinhaus Center, Wroclaw University of Technology.
    3. Zhang, Cen & Schmöcker, Jan-Dirk & Kuwahara, Masahiro & Nakamura, Toshiyuki & Uno, Nobuhiro, 2020. "A diffusion model for estimating adoption patterns of a one-way carsharing system in its initial years," Transportation Research Part A: Policy and Practice, Elsevier, vol. 136(C), pages 135-150.
    4. Sahat Hutajulu & Wawan Dhewanto & Eko Agus Prasetio, 2021. "An Agent-Based Model for 5G Technology Diffusion in Urban Societies: Simulating Two Development Scenarios," Sustainability, MDPI, vol. 13(22), pages 1-20, November.
    5. Katarzyna Byrka & Arkadiusz Jedrzejewski & Katarzyna Sznajd-Weron & Rafal Weron, 2015. "Difficulty is critical: Psychological factors in modeling diffusion of green products and practices," HSC Research Reports HSC/15/10, Hugo Steinhaus Center, Wroclaw University of Technology.
    6. Yash Chawla & Anna Kowalska-Pyzalska, 2019. "Public Awareness and Consumer Acceptance of Smart Meters among Polish Social Media Users," Energies, MDPI, vol. 12(14), pages 1-27, July.
    7. Arias-Gaviria, Jessica & Carvajal-Quintero, Sandra Ximena & Arango-Aramburo, Santiago, 2019. "Understanding dynamics and policy for renewable energy diffusion in Colombia," Renewable Energy, Elsevier, vol. 139(C), pages 1111-1119.
    8. Katarzyna Sznajd-Weron & Janusz Szwabiński & Rafał Weron, 2014. "Is the Person-Situation Debate Important for Agent-Based Modeling and Vice-Versa?," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-7, November.
    9. Anna Kowalska-Pyzalska & Katarzyna Maciejowska & Katarzyna Sznajd-Weron & Rafal Weron, 2014. "Diffusion and adoption of dynamic electricity tariffs: An agent-based modeling approach," HSC Research Reports HSC/14/01, Hugo Steinhaus Center, Wroclaw University of Technology.
    10. Rafik Nafkha & Krzysztof Gajowniczek & Tomasz Ząbkowski, 2018. "Do Customers Choose Proper Tariff? Empirical Analysis Based on Polish Data Using Unsupervised Techniques," Energies, MDPI, vol. 11(3), pages 1-17, February.
    11. Anna Kowalska-Pyzalska, 2015. "Social acceptance of green energy and dynamic electricity tariffs - a short review," HSC Research Reports HSC/15/07, Hugo Steinhaus Center, Wroclaw University of Technology.
    12. Jakub Nowotarski & Rafal Weron, 2016. "To combine or not to combine? Recent trends in electricity price forecasting," HSC Research Reports HSC/16/01, Hugo Steinhaus Center, Wroclaw University of Technology.
    13. Alexopoulos, Thomas A., 2017. "The growing importance of natural gas as a predictor for retail electricity prices in US," Energy, Elsevier, vol. 137(C), pages 219-233.
    14. Scheller, Fabian & Johanning, Simon & Bruckner, Thomas, 2018. "IRPsim: A techno-socio-economic energy system model vision for business strategy assessment at municipal level," Contributions of the Institute for Infrastructure and Resources Management 02/2018, University of Leipzig, Institute for Infrastructure and Resources Management.
    15. Rafał Apriasz & Tyll Krueger & Grzegorz Marcjasz & Katarzyna Sznajd-Weron, 2016. "The Hunt Opinion Model—An Agent Based Approach to Recurring Fashion Cycles," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-19, November.
    16. Mittal, Anuj & Krejci, Caroline C. & Dorneich, Michael C., 2019. "An agent-based approach to designing residential renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 1008-1020.
    17. Miwa Nakai & Victor von Loessl & Heike Wetzel, 2022. "Preferences for dynamic electricity tariffs: A comparison of households in Germany and Japan," MAGKS Papers on Economics 202213, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    18. Hesselink, Laurens X.W. & Chappin, Emile J.L., 2019. "Adoption of energy efficient technologies by households – Barriers, policies and agent-based modelling studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 99(C), pages 29-41.
    19. Agnieszka Kowalska-Styczeń & Krzysztof Malarz, 2020. "Noise induced unanimity and disorder in opinion formation," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-22, July.
    20. Niamir, Leila & Filatova, Tatiana & Voinov, Alexey & Bressers, Hans, 2018. "Transition to low-carbon economy: Assessing cumulative impacts of individual behavioral changes," Energy Policy, Elsevier, vol. 118(C), pages 325-345.
    21. Weron, Tomasz & Kowalska-Pyzalska, Anna & Weron, Rafał, 2018. "The role of educational trainings in the diffusion of smart metering platforms: An agent-based modeling approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 591-600.
    22. Rafal Weron, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," HSC Research Reports HSC/14/07, Hugo Steinhaus Center, Wroclaw University of Technology.
    23. Kowalska-Pyzalska, Anna, 2018. "What makes consumers adopt to innovative energy services in the energy market? A review of incentives and barriers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3570-3581.
    24. Liang, Xin & Yu, Tao & Hong, Jingke & Shen, Geoffrey Qiping, 2019. "Making incentive policies more effective: An agent-based model for energy-efficiency retrofit in China," Energy Policy, Elsevier, vol. 126(C), pages 177-189.
    25. Arias-Gaviria, Jessica & Larsen, Erik R. & Arango-Aramburo, Santiago, 2018. "Understanding the future of Seawater Air Conditioning in the Caribbean: A simulation approach," Utilities Policy, Elsevier, vol. 53(C), pages 73-83.
    26. Alexandre Lucas & Konstantinos Pegios & Evangelos Kotsakis & Dan Clarke, 2020. "Price Forecasting for the Balancing Energy Market Using Machine-Learning Regression," Energies, MDPI, vol. 13(20), pages 1-16, October.
    27. Jędrzejewski, Arkadiusz & Sznajd-Weron, Katarzyna, 2018. "Impact of memory on opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 306-315.
    28. Hidayatno, Akhmad & Jafino, Bramka Arga & Setiawan, Andri D. & Purwanto, Widodo Wahyu, 2020. "When and why does transition fail? A model-based identification of adoption barriers and policy vulnerabilities for transition to natural gas vehicles," Energy Policy, Elsevier, vol. 138(C).
    29. Rolando Fuentes & Abhijit Sengupta, 2019. "Using Insurance to Manage Reliability in the Distributed Electricity Sector: Insights From an Agent-Based Model," Discussion Papers ks--2019-dp59, King Abdullah Petroleum Studies and Research Center.
    30. Yalcintas, Melek & Hagen, William T. & Kaya, Abidin, 2015. "Time-based electricity pricing for large-volume customers: A comparison of two buildings under tariff alternatives," Utilities Policy, Elsevier, vol. 37(C), pages 58-68.
    31. Marc Deissenroth & Martin Klein & Kristina Nienhaus & Matthias Reeg, 2017. "Assessing the Plurality of Actors and Policy Interactions: Agent-Based Modelling of Renewable Energy Market Integration," Complexity, Hindawi, vol. 2017, pages 1-24, December.
    32. Liu, Xueying & Madlener, Reinhard, 2019. "The Sky is the Limit: Assessing Aircraft Market Diffusion with Agent-Based Modeling," FCN Working Papers 16/2019, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    33. Anna Kowalska-Pyzalska, 2016. "What makes consumers adopt to innovative energy services in the energy market?," HSC Research Reports HSC/16/09, Hugo Steinhaus Center, Wroclaw University of Technology.
    34. David Huckebrink & Valentin Bertsch, 2021. "Integrating Behavioural Aspects in Energy System Modelling—A Review," Energies, MDPI, vol. 14(15), pages 1-26, July.
    35. Cl'emence Alasseur & Ivar Ekeland & Romuald Elie & Nicol'as Hern'andez Santib'a~nez & Dylan Possamai, 2017. "An adverse selection approach to power pricing," Papers 1706.01934, arXiv.org, revised Sep 2019.
    36. Anna Kowalska-Pyzalska & Katarzyna Maciejowska & Katarzyna Sznajd-Weron & Rafal Weron, 2014. "Modeling consumer opinions towards dynamic pricing: An agent-based approach," HSC Research Reports HSC/14/06, Hugo Steinhaus Center, Wroclaw University of Technology.
    37. Yash Chawla & Anna Kowalska-Pyzalska & Paulo Duarte Silveira, 2019. "Marketing and communications channels for diffusion of smart meters in Portugal," HSC Research Reports HSC/19/05, Hugo Steinhaus Center, Wroclaw University of Technology.
    38. Ringler, Philipp & Keles, Dogan & Fichtner, Wolf, 2016. "Agent-based modelling and simulation of smart electricity grids and markets – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 205-215.
    39. Scheller, Fabian & Johanning, Simon & Bruckner, Thomas, 2019. "A review of designing empirically grounded agent-based models of innovation diffusion: Development process, conceptual foundation and research agenda," Contributions of the Institute for Infrastructure and Resources Management 01/2019, University of Leipzig, Institute for Infrastructure and Resources Management.
    40. Freier, Julia & von Loessl, Victor, 2022. "Dynamic electricity tariffs: Designing reasonable pricing schemes for private households," Energy Economics, Elsevier, vol. 112(C).

  16. Katarzyna Maciejowska & Rafal Weron, 2013. "Forecasting of daily electricity spot prices by incorporating intra-day relationships: Evidence form the UK power market," HSC Research Reports HSC/13/01, Hugo Steinhaus Center, Wroclaw University of Technology, revised 15 Apr 2013.

    Cited by:

    1. Katarzyna Maciejowska & Rafal Weron, 2013. "Forecasting of daily electricity prices with factor models: Utilizing intra-day and inter-zone relationships," HSC Research Reports HSC/13/11, Hugo Steinhaus Center, Wroclaw University of Technology.
    2. Hendrik Kohrs & Hermann Mühlichen & Benjamin R. Auer & Frank Schuhmacher, 2019. "Pricing and risk of swing contracts in natural gas markets," Review of Derivatives Research, Springer, vol. 22(1), pages 77-167, April.
    3. Jakub Nowotarski & Rafał Weron, 2015. "Computing electricity spot price prediction intervals using quantile regression and forecast averaging," Computational Statistics, Springer, vol. 30(3), pages 791-803, September.
    4. Jakub Nowotarski & Rafal Weron, 2014. "Merging quantile regression with forecast averaging to obtain more accurate interval forecasts of Nord Pool spot prices," HSC Research Reports HSC/14/03, Hugo Steinhaus Center, Wroclaw University of Technology.
    5. Rafal Weron, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," HSC Research Reports HSC/14/07, Hugo Steinhaus Center, Wroclaw University of Technology.
    6. Katarzyna Maciejowska & Rafal Weron, 2015. "Short- and mid-term forecasting of baseload electricity prices in the UK: The impact of intra-day price relationships and market fundamentals," HSC Research Reports HSC/15/04, Hugo Steinhaus Center, Wroclaw University of Technology.
    7. Maciejowska, Katarzyna & Nowotarski, Jakub & Weron, Rafał, 2016. "Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging," International Journal of Forecasting, Elsevier, vol. 32(3), pages 957-965.
    8. Florian Ziel, 2015. "Forecasting Electricity Spot Prices using Lasso: On Capturing the Autoregressive Intraday Structure," Papers 1509.01966, arXiv.org, revised Jan 2016.
    9. Philip Beran & Arne Vogler, 2021. "Multi-Day-Ahead Electricity Price Forecasting: A Comparison of fundamental, econometric and hybrid Models," EWL Working Papers 2102, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Oct 2021.

  17. Katarzyna Maciejowska & Rafal Weron, 2013. "Forecasting of daily electricity prices with factor models: Utilizing intra-day and inter-zone relationships," HSC Research Reports HSC/13/11, Hugo Steinhaus Center, Wroclaw University of Technology.

    Cited by:

    1. Özen, Kadir & Yıldırım, Dilem, 2021. "Application of bagging in day-ahead electricity price forecasting and factor augmentation," Energy Economics, Elsevier, vol. 103(C).
    2. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    3. Alonso Fernández, Andrés Modesto & Bastos, Guadalupe & García-Martos, Carolina, 2017. "Electricity prices forecasting by averaging dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS 24028, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Andrés M. Alonso & Guadalupe Bastos & Carolina García-Martos, 2016. "Electricity Price Forecasting by Averaging Dynamic Factor Models," Energies, MDPI, vol. 9(8), pages 1-21, July.
    5. Caston Sigauke & Murendeni Maurel Nemukula & Daniel Maposa, 2018. "Probabilistic Hourly Load Forecasting Using Additive Quantile Regression Models," Energies, MDPI, vol. 11(9), pages 1-21, August.
    6. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2018. "Comparing the Forecasting Performances of Linear Models for Electricity Prices with High RES Penetration," Working Papers No 2/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    7. Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020. "PCA Forecast Averaging—Predicting Day-Ahead and Intraday Electricity Prices," Energies, MDPI, vol. 13(14), pages 1-19, July.
    8. Grzegorz Marcjasz & Tomasz Serafin & Rafal Weron, 2018. "Selection of calibration windows for day-ahead electricity price forecasting," HSC Research Reports HSC/18/06, Hugo Steinhaus Center, Wroclaw University of Technology.
    9. Ostap Okhrin & Stefan Trück, 2015. "Editorial to the special issue on Applicable semiparametrics of computational statistics," Computational Statistics, Springer, vol. 30(3), pages 641-646, September.
    10. Rafal Weron, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," HSC Research Reports HSC/14/07, Hugo Steinhaus Center, Wroclaw University of Technology.
    11. Florian Ziel & Rafal Weron, 2016. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate models," HSC Research Reports HSC/16/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    12. Kadir Özen & Dilem Yıldırım, 2021. "Application of Bagging in Day-Ahead Electricity Price Forecasting and Factor Augmentation," ERC Working Papers 2101, ERC - Economic Research Center, Middle East Technical University, revised Apr 2021.
    13. De Siano, Rita & Sapio, Alessandro, 2022. "Spatial merit order effects of renewables in the Italian power exchange," Energy Economics, Elsevier, vol. 108(C).
    14. Duván Humberto Cataño & Carlos Vladimir Rodríguez-Caballero & Daniel Peña, 2019. "Wavelet Estimation for Dynamic Factor Models with Time-Varying Loadings," CREATES Research Papers 2019-23, Department of Economics and Business Economics, Aarhus University.
    15. Fabrizio Leisen & Luca Rossini & Cristiano Villa, 2020. "Loss-based approach to two-piece location-scale distributions with applications to dependent data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 309-333, June.
    16. Ziel, Florian & Weron, Rafał, 2018. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks," Energy Economics, Elsevier, vol. 70(C), pages 396-420.
    17. Philip Beran & Arne Vogler, 2021. "Multi-Day-Ahead Electricity Price Forecasting: A Comparison of fundamental, econometric and hybrid Models," EWL Working Papers 2102, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Oct 2021.

  18. Katarzyna Maciejowska, 2010. "Estimation methods comparison of SVAR model with the mixture of two normal distributions - Monte Carlo analysis," Economics Working Papers ECO2010/27, European University Institute.

    Cited by:

    1. Guarda, Paolo & Rouabah, Abdelaziz & Theal, John, 2012. "An MVAR framework to capture extreme events in macro-prudential stress tests," Working Paper Series 1464, European Central Bank.
    2. Sun, Hang, 2016. "Crisis-Contingent Dynamics of Connectedness: An SVAR-Spatial-Network “Tripod” Model with Thresholds," Research Memorandum 032, Maastricht University, Graduate School of Business and Economics (GSBE).

  19. Katarzyna Maciejowska, 2010. "Common factors in nonstationary panel data with a deterministic trend - estimation and distribution theory," Economics Working Papers ECO2010/28, European University Institute.

    Cited by:

    1. Matteo Barigozzi & Lorenzo Trapani, 2018. "Determining the dimension of factor structures in non-stationary large datasets," Papers 1806.03647, arXiv.org.

  20. Markku Lanne & Helmut Luetkepohl & Katarzyna Maciejowska, 2009. "Structural Vector Autoregressions with Markov Switching," Economics Working Papers ECO2009/06, European University Institute.

    Cited by:

    1. Nautz, Dieter & Strohsal, Till & Netšunajev, Aleksei, 2019. "The Anchoring Of Inflation Expectations In The Short And In The Long Run," Macroeconomic Dynamics, Cambridge University Press, vol. 23(5), pages 1959-1977, July.
    2. Savi Virolainen, 2021. "Gaussian and Student's $t$ mixture vector autoregressive model with application to the asymmetric effects of monetary policy shocks in the Euro area," Papers 2109.13648, arXiv.org, revised Jun 2022.
    3. Lütkepohl, Helmut & Netšunajev, Aleksei, 2017. "Structural vector autoregressions with heteroskedasticity: A review of different volatility models," Econometrics and Statistics, Elsevier, vol. 1(C), pages 2-18.
    4. Mengheng Li & Ivan Mendieta-Munoz, 2019. "The multivariate simultaneous unobserved components model and identification via heteroskedasticity," Working Paper Series 2019/08, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    5. Podstawski, Maximilian & Velinov, Anton, 2018. "The state dependent impact of bank exposure on sovereign risk," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 88, pages 63-75.
    6. Jan Philipp Fritsche & Mathias Klein & Malte Rieth, 2020. "Government Spending Multipliers in (Un)certain Times," Discussion Papers of DIW Berlin 1901, DIW Berlin, German Institute for Economic Research.
    7. Helmut Lütkepohl & Aleksei Netsunajev, 2015. "Structural Vector Autoregressions with Heteroskedasticity: A Comparison of Different Volatility Models," Discussion Papers of DIW Berlin 1464, DIW Berlin, German Institute for Economic Research.
    8. Lütkepohl, Helmut & Woźniak, Tomasz, 2020. "Bayesian inference for structural vector autoregressions identified by Markov-switching heteroskedasticity," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    9. Klinger, Sabine & Weber, Enzo, 2016. "Detecting unemployment hysteresis: A simultaneous unobserved components model with Markov switching," Economics Letters, Elsevier, vol. 144(C), pages 115-118.
    10. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2016. "Structural analysis with Multivariate Autoregressive Index models," Journal of Econometrics, Elsevier, vol. 192(2), pages 332-348.
    11. Aleksei Netsunajev, 2013. "Reaction to technology shocks in Markov-switching structural VARs: identification via heteroskedasticity," Bank of Estonia Working Papers wp2012-6, Bank of Estonia, revised 03 Jan 2013.
    12. Wenjuan Chen & Anton Velinov, 2012. "Do Japanese Stock Prices Reflect Macro Fundamentals?," SFB 649 Discussion Papers SFB649DP2012-037, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    13. Winkelmann, Lars & Netsunajev, Aleksei, 2015. "International Transmissions of Inflation Expectations in a Markov Switching Structural VAR Model," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112900, Verein für Socialpolitik / German Economic Association.
    14. Sascha A. Keweloh, 2023. "Uncertain Prior Economic Knowledge and Statistically Identified Structural Vector Autoregressions," Papers 2303.13281, arXiv.org.
    15. Aleksei Netsunajev & Katharina Glass, 2016. "Uncertainty and Employment Dynamics in the Euro Area and the US," SFB 649 Discussion Papers SFB649DP2016-002, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
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    12. Thomas Mobius & Mira Watermeyer & Oliver Grothe & Felix Musgens, 2023. "Enhancing Energy System Models Using Better Load Forecasts," Papers 2302.11017, arXiv.org.
    13. Grzegorz Marcjasz & Micha{l} Narajewski & Rafa{l} Weron & Florian Ziel, 2022. "Distributional neural networks for electricity price forecasting," Papers 2207.02832, arXiv.org, revised Dec 2022.
    14. Jing Wan & Jiehui Huang & Zhiyuan Liao & Chunquan Li & Peter X. Liu, 2022. "A Multi-View Ensemble Width-Depth Neural Network for Short-Term Wind Power Forecasting," Mathematics, MDPI, vol. 10(11), pages 1-20, May.
    15. Serafin, Tomasz & Marcjasz, Grzegorz & Weron, Rafał, 2022. "Trading on short-term path forecasts of intraday electricity prices," Energy Economics, Elsevier, vol. 112(C).
    16. Grothe, Oliver & Kächele, Fabian & Krüger, Fabian, 2023. "From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecasting," Energy Economics, Elsevier, vol. 120(C).
    17. Silvia Golia & Luigi Grossi & Matteo Pelagatti, 2022. "Machine Learning Models and Intra-Daily Market Information for the Prediction of Italian Electricity Prices," Forecasting, MDPI, vol. 5(1), pages 1-21, December.
    18. Rainer Baule & Michael Naumann, 2021. "Volatility and Dispersion of Hourly Electricity Contracts on the German Continuous Intraday Market," Energies, MDPI, vol. 14(22), pages 1-24, November.
    19. Katarzyna Maciejowska & Bartosz Uniejewski & Rafa{l} Weron, 2022. "Forecasting Electricity Prices," Papers 2204.11735, arXiv.org.
    20. Rainer Baule & Michael Naumann, 2022. "Flexible Short-Term Electricity Certificates—An Analysis of Trading Strategies on the Continuous Intraday Market," Energies, MDPI, vol. 15(17), pages 1-28, August.

  2. Maciejowska, Katarzyna, 2020. "Assessing the impact of renewable energy sources on the electricity price level and variability – A quantile regression approach," Energy Economics, Elsevier, vol. 85(C).
    See citations under working paper version above.
  3. Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020. "PCA Forecast Averaging—Predicting Day-Ahead and Intraday Electricity Prices," Energies, MDPI, vol. 13(14), pages 1-19, July.
    See citations under working paper version above.
  4. Katarzyna Maciejowska & Weronika Nitka & Tomasz Weron, 2019. "Day-Ahead vs. Intraday—Forecasting the Price Spread to Maximize Economic Benefits," Energies, MDPI, vol. 12(4), pages 1-15, February.

    Cited by:

    1. Janczura, Joanna & Wójcik, Edyta, 2022. "Dynamic short-term risk management strategies for the choice of electricity market based on probabilistic forecasts of profit and risk measures. The German and the Polish market case study," Energy Economics, Elsevier, vol. 110(C).
    2. Tim Janke & Florian Steinke, 2019. "Forecasting the Price Distribution of Continuous Intraday Electricity Trading," Energies, MDPI, vol. 12(22), pages 1-14, November.
    3. Grzegorz Marcjasz & Bartosz Uniejewski & Rafał Weron, 2020. "Beating the Naïve—Combining LASSO with Naïve Intraday Electricity Price Forecasts," Energies, MDPI, vol. 13(7), pages 1-16, April.
    4. Ocker, Fabian & Jaenisch, Vincent, 2020. "The way towards European electricity intraday auctions – Status quo and future developments," Energy Policy, Elsevier, vol. 145(C).
    5. Maciejowska, Katarzyna & Nitka, Weronika & Weron, Tomasz, 2021. "Enhancing load, wind and solar generation for day-ahead forecasting of electricity prices," Energy Economics, Elsevier, vol. 99(C).
    6. Katarzyna Maciejowska, 2022. "A portfolio management of a small RES utility with a Structural Vector Autoregressive model of German electricity markets," Papers 2205.00975, arXiv.org.
    7. Michael Kostmann & Wolfgang K. Härdle, 2019. "Forecasting in Blockchain-Based Local Energy Markets," Energies, MDPI, vol. 12(14), pages 1-27, July.
    8. Thomas Kuppelwieser & David Wozabal, 2023. "Intraday power trading: toward an arms race in weather forecasting?," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 57-83, March.
    9. Bartosz Uniejewski & Katarzyna Maciejowska, 2022. "LASSO Principal Component Averaging -- a fully automated approach for point forecast pooling," Papers 2207.04794, arXiv.org.
    10. Weronika Nitka & Tomasz Serafin & Dimitrios Sotiros, 2021. "Forecasting Electricity Prices: Autoregressive Hybrid Nearest Neighbors (ARHNN) method," WORking papers in Management Science (WORMS) WORMS/21/06, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    11. Marcjasz, Grzegorz & Uniejewski, Bartosz & Weron, Rafał, 2020. "Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 466-479.
    12. Klie, Leo & Madlener, Reinhard, 2020. "Concentration Versus Diversification: A Spatial Deployment Approach to Improve the Economics of Wind Power," FCN Working Papers 2/2020, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN), revised May 2021.
    13. Dimitrios Kontogiannis & Dimitrios Bargiotas & Aspassia Daskalopulu & Athanasios Ioannis Arvanitidis & Lefteri H. Tsoukalas, 2022. "Error Compensation Enhanced Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 15(4), pages 1-21, February.
    14. Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020. "PCA Forecast Averaging—Predicting Day-Ahead and Intraday Electricity Prices," Energies, MDPI, vol. 13(14), pages 1-19, July.
    15. Christopher Kath & Florian Ziel, 2020. "Optimal Order Execution in Intraday Markets: Minimizing Costs in Trade Trajectories," Papers 2009.07892, arXiv.org, revised Oct 2020.
    16. Ivana Kiprijanovska & Simon Stankoski & Igor Ilievski & Slobodan Jovanovski & Matjaž Gams & Hristijan Gjoreski, 2020. "HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning," Energies, MDPI, vol. 13(10), pages 1-29, May.
    17. Micha{l} Narajewski & Florian Ziel, 2020. "Ensemble Forecasting for Intraday Electricity Prices: Simulating Trajectories," Papers 2005.01365, arXiv.org, revised Aug 2020.
    18. Hakan Acaroğlu & Fausto Pedro García Márquez, 2021. "Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy," Energies, MDPI, vol. 14(22), pages 1-23, November.
    19. Bartosz Uniejewski & Grzegorz Marcjasz & Rafal Weron, 2018. "Understanding intraday electricity markets: Variable selection and very short-term price forecasting using LASSO," HSC Research Reports HSC/18/07, Hugo Steinhaus Center, Wroclaw University of Technology.
    20. Joanna Janczura & Andrzej Puć, 2023. "ARX-GARCH Probabilistic Price Forecasts for Diversification of Trade in Electricity Markets—Variance Stabilizing Transformation and Financial Risk-Minimizing Portfolio Allocation," Energies, MDPI, vol. 16(2), pages 1-28, January.
    21. Li, Wei & Paraschiv, Florentina, 2022. "Modelling the evolution of wind and solar power infeed forecasts," Journal of Commodity Markets, Elsevier, vol. 25(C).
    22. Marcin Malec & Grzegorz Kinelski & Marzena Czarnecka, 2021. "The Impact of COVID-19 on Electricity Demand Profiles: A Case Study of Selected Business Clients in Poland," Energies, MDPI, vol. 14(17), pages 1-17, August.
    23. Russo, Marianna & Kraft, Emil & Bertsch, Valentin & Keles, Dogan, 2022. "Short-term risk management of electricity retailers under rising shares of decentralized solar generation," Energy Economics, Elsevier, vol. 109(C).
    24. Grzegorz Marcjasz & Micha{l} Narajewski & Rafa{l} Weron & Florian Ziel, 2022. "Distributional neural networks for electricity price forecasting," Papers 2207.02832, arXiv.org, revised Dec 2022.
    25. Katarzyna Maciejowska & Weronika Nitka & Tomasz Weron, 2019. "Enhancing load, wind and solar generation forecasts in day-ahead forecasting of spot and intraday electricity prices," HSC Research Reports HSC/19/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    26. Tomasz Serafin & Bartosz Uniejewski & Rafal Weron, 2019. "Averaging predictive distributions across calibration windows for day-ahead electricity price forecasting," WORking papers in Management Science (WORMS) WORMS/19/08, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, revised 06 Jul 2019.
    27. Christopher Kath & Weronika Nitka & Tomasz Serafin & Tomasz Weron & Przemysław Zaleski & Rafał Weron, 2020. "Balancing Generation from Renewable Energy Sources: Profitability of an Energy Trader," Energies, MDPI, vol. 13(1), pages 1-15, January.
    28. Antoine Ferré & Guillaume de Certaines & Jérôme Cazelles & Tancrède Cohet & Arash Farnoosh & Frédéric Lantz, 2021. "Short-term electricity price forecastingmodels comparative analysis : Machine Learning vs. Econometrics," Working Papers hal-03262208, HAL.
    29. Christopher Kath & Weronika Nitka & Tomasz Serafin & Tomasz Weron & Przemyslaw Zaleski & Rafal Weron, 2019. "Balancing RES generation: Profitability of an energy trader," HSC Research Reports HSC/19/07, Hugo Steinhaus Center, Wroclaw University of Technology.
    30. Uniejewski, Bartosz & Maciejowska, Katarzyna, 2023. "LASSO principal component averaging: A fully automated approach for point forecast pooling," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1839-1852.
    31. Su, Jun & Lie, T.T. & Zamora, Ramon, 2020. "A rolling horizon scheduling of aggregated electric vehicles charging under the electricity exchange market," Applied Energy, Elsevier, vol. 275(C).
    32. Jose R. Cedeño González & Juan J. Flores & Claudio R. Fuerte-Esquivel & Boris A. Moreno-Alcaide, 2020. "Nearest Neighbors Time Series Forecaster Based on Phase Space Reconstruction for Short-Term Load Forecasting," Energies, MDPI, vol. 13(20), pages 1-24, October.
    33. Ilkay Oksuz & Umut Ugurlu, 2019. "Neural Network Based Model Comparison for Intraday Electricity Price Forecasting," Energies, MDPI, vol. 12(23), pages 1-14, November.
    34. Al-Lawati, Razan A.H. & Crespo-Vazquez, Jose L. & Faiz, Tasnim Ibn & Fang, Xin & Noor-E-Alam, Md., 2021. "Two-stage stochastic optimization frameworks to aid in decision-making under uncertainty for variable resource generators participating in a sequential energy market," Applied Energy, Elsevier, vol. 292(C).
    35. Narajewski, Michał & Ziel, Florian, 2020. "Ensemble forecasting for intraday electricity prices: Simulating trajectories," Applied Energy, Elsevier, vol. 279(C).
    36. Joanna Janczura & Aleksandra Michalak, 2020. "Optimization of Electric Energy Sales Strategy Based on Probabilistic Forecasts," Energies, MDPI, vol. 13(5), pages 1-16, February.
    37. Katarzyna Maciejowska & Bartosz Uniejewski & Rafa{l} Weron, 2022. "Forecasting Electricity Prices," Papers 2204.11735, arXiv.org.
    38. Primož Mavsar & Klemen Sredenšek & Bojan Štumberger & Miralem Hadžiselimović & Sebastijan Seme, 2019. "Simplified Method for Analyzing the Availability of Rooftop Photovoltaic Potential," Energies, MDPI, vol. 12(22), pages 1-17, November.

  5. Maciejowska, Katarzyna & Nowotarski, Jakub & Weron, Rafał, 2016. "Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging," International Journal of Forecasting, Elsevier, vol. 32(3), pages 957-965.
    See citations under working paper version above.
  6. Maciejowska, Katarzyna & Nowotarski, Jakub, 2016. "A hybrid model for GEFCom2014 probabilistic electricity price forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1051-1056.
    See citations under working paper version above.
  7. Katarzyna Maciejowska & Rafał Weron, 2015. "Forecasting of daily electricity prices with factor models: utilizing intra-day and inter-zone relationships," Computational Statistics, Springer, vol. 30(3), pages 805-819, September.
    See citations under working paper version above.
  8. Kowalska-Pyzalska, Anna & Maciejowska, Katarzyna & Suszczyński, Karol & Sznajd-Weron, Katarzyna & Weron, Rafał, 2014. "Turning green: Agent-based modeling of the adoption of dynamic electricity tariffs," Energy Policy, Elsevier, vol. 72(C), pages 164-174.
    See citations under working paper version above.
  9. Lanne, Markku & Lütkepohl, Helmut & Maciejowska, Katarzyna, 2010. "Structural vector autoregressions with Markov switching," Journal of Economic Dynamics and Control, Elsevier, vol. 34(2), pages 121-131, February.
    See citations under working paper version above.
  10. Katarzyna Maciejowska, 2010. "Estimation Methods Comparison of SVAR Models with a Mixture of Two Normal Distributions," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 2(4), pages 279-314, September.

    Cited by:

    1. Guarda, Paolo & Rouabah, Abdelaziz & Theal, John, 2012. "An MVAR framework to capture extreme events in macro-prudential stress tests," Working Paper Series 1464, European Central Bank.
    2. Sun, Hang, 2016. "Crisis-Contingent Dynamics of Connectedness: An SVAR-Spatial-Network “Tripod” Model with Thresholds," Research Memorandum 032, Maastricht University, Graduate School of Business and Economics (GSBE).

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