Ariel Liebman

Personal Details

This person is deceased (Date: 09 Nov 2023)

First Name:	Ariel
Middle Name:
Last Name:	Liebman
Suffix:
RePEc Short-ID:	pli790
Terminal Degree:	1996 (from RePEc Genealogy)

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. Foster, John & Wagner, Liam & Liebman, Ariel, 2015. "Modelling the Electricity and Natural Gas Sectors for the Future Grid: Developing Co-Optimisation Platforms for Market Redesign," MPRA Paper 70114, University Library of Munich, Germany.

   Cited by:

   1. Anna Flessa & Dimitris Fragkiadakis & Eleftheria Zisarou & Panagiotis Fragkos, 2023. "Developing an Integrated Energy–Economy Model Framework for Islands," Energies, MDPI, vol. 16(3), pages 1-32, January.
   2. Foster, John & Wagner, Liam & Liebman, Ariel, 2017. "Economic and investment models for future grids: Final Report Project 3," MPRA Paper 78866, University Library of Munich, Germany.

2. John Foster & Liam Wagner & Phil Wild & Junhua Zhao & Lucas Skoofa & Craig Froome & Ariel Liebman, 2011. "Market and Economic Modelling of the Intelligent Grid: End of Year Report 2010," Energy Economics and Management Group Working Papers 10, School of Economics, University of Queensland, Australia.

   Cited by:

   1. Bell, William Paul & Wild, Phillip & Foster, John, 2013. "The transformative effect of unscheduled generation by solar PV and wind generation on net electricity demand," MPRA Paper 46065, University Library of Munich, Germany.
   2. John Foster & William Paul Bell & Craig Froome & Phil Wild & Liam Wagner & Deepak Sharma & Suwin Sandu & Suchi Misra & Ravindra Bagia, 2012. "Institutional adaptability to redress electricity infrastructure vulnerability due to climate change," Energy Economics and Management Group Working Papers 7-2012, School of Economics, University of Queensland, Australia.
   3. Bell, William Paul, 2012. "The impact of climate change on generation and transmission in the Australian national electricity market," MPRA Paper 38111, University Library of Munich, Germany, revised 29 Feb 2012.
   4. Foster, John & Wagner, Liam & Liebman, Ariel, 2017. "Economic and investment models for future grids: Final Report Project 3," MPRA Paper 78866, University Library of Munich, Germany.

3. John Foster & Liam Wagner & Ariel Liebman, 2011. "Market and Economic Modelling of the Intelligent Grid: 1st Interim Report 2009," Energy Economics and Management Group Working Papers 08, School of Economics, University of Queensland, Australia.

   Cited by:

   1. Foster, John & Wagner, Liam & Liebman, Ariel, 2017. "Economic and investment models for future grids: Final Report Project 3," MPRA Paper 78866, University Library of Munich, Germany.

4. Xun Zhou & Zhao Yang Dong & Ariel Liebman & Geoff James, 2009. "Australian electricity market power analysis under potential emission trading scheme," Energy Economics and Management Group Working Papers 1-2009, School of Economics, University of Queensland, Australia.

   Cited by:

   1. Radzi, N.H. & Bansal, R.C. & Dong, Z.Y., 2015. "New Australian NEM transmission use of system charging methodologies for integrating renewable generation to existing grid," Renewable Energy, Elsevier, vol. 76(C), pages 72-81.
   2. Radzi, N.H. & Bansal, R.C. & Dong, Z.Y. & Hassan, M.Y. & Wong, K.P., 2013. "An efficient distribution factors enhanced transmission pricing method for Australian NEM transmission charging scheme," Renewable Energy, Elsevier, vol. 53(C), pages 319-328.
   3. Apergis, Nicholas & Lau, Marco Chi Keung, 2015. "Structural breaks and electricity prices: Further evidence on the role of climate policy uncertainties in the Australian electricity market," Energy Economics, Elsevier, vol. 52(PA), pages 176-182.

5. Xun Zhou & Zhao Yang Dong & Ariel Liebman & Geoff James, 2008. "Potential Impact of Emission Trading Schemes on the Australian National Electricity Market," Energy Economics and Management Group Working Papers 1-2008, School of Economics, University of Queensland, Australia.

   Cited by:

   1. Radzi, N.H. & Bansal, R.C. & Dong, Z.Y., 2015. "New Australian NEM transmission use of system charging methodologies for integrating renewable generation to existing grid," Renewable Energy, Elsevier, vol. 76(C), pages 72-81.
   2. Liu, Liwei & Sun, Xiaoru & Chen, Chuxiang & Zhao, Erdong, 2016. "How will auctioning impact on the carbon emission abatement cost of electric power generation sector in China?," Applied Energy, Elsevier, vol. 168(C), pages 594-609.
   3. Imran, Kashif & Hassan, Tehzeebul & Aslam, Muhammad Farooq & Ngan, Hon-Wing & Ahmad, Intesar, 2009. "Simulation analysis of emissions trading impact on a non-utility power plant," Energy Policy, Elsevier, vol. 37(12), pages 5694-5703, December.

Articles

1. Mohsen Khorasany & Donald Azuatalam & Robert Glasgow & Ariel Liebman & Reza Razzaghi, 2020. "Transactive Energy Market for Energy Management in Microgrids: The Monash Microgrid Case Study," Energies, MDPI, vol. 13(8), pages 1-23, April.

   Cited by:

   1. Dudjak, Viktorija & Neves, Diana & Alskaif, Tarek & Khadem, Shafi & Pena-Bello, Alejandro & Saggese, Pietro & Bowler, Benjamin & Andoni, Merlinda & Bertolini, Marina & Zhou, Yue & Lormeteau, Blanche &, 2021. "Impact of local energy markets integration in power systems layer: A comprehensive review," Applied Energy, Elsevier, vol. 301(C).
   2. Tushar, Wayes & Yuen, Chau & Saha, Tapan K. & Morstyn, Thomas & Chapman, Archie C. & Alam, M. Jan E. & Hanif, Sarmad & Poor, H. Vincent, 2021. "Peer-to-peer energy systems for connected communities: A review of recent advances and emerging challenges," Applied Energy, Elsevier, vol. 282(PA).
   3. Mario Tovar & Miguel Robles & Felipe Rashid, 2020. "PV Power Prediction, Using CNN-LSTM Hybrid Neural Network Model. Case of Study: Temixco-Morelos, México," Energies, MDPI, vol. 13(24), pages 1-15, December.
   4. Muhammad Mateen Afzal Awan & Muhammad Yaqoob Javed & Aamer Bilal Asghar & Krzysztof Ejsmont & Zia-ur-Rehman, 2022. "Economic Integration of Renewable and Conventional Power Sources—A Case Study," Energies, MDPI, vol. 15(6), pages 1-20, March.
   5. Partha Pratim Dey & Dulal Chandra Das & Abdul Latif & S. M. Suhail Hussain & Taha Selim Ustun, 2020. "Active Power Management of Virtual Power Plant under Penetration of Central Receiver Solar Thermal-Wind Using Butterfly Optimization Technique," Sustainability, MDPI, vol. 12(17), pages 1-16, August.
   6. Sarah Ouédraogo & Ghjuvan Antone Faggianelli & Guillaume Pigelet & Jean Laurent Duchaud & Gilles Notton, 2020. "Application of Optimal Energy Management Strategies for a Building Powered by PV/Battery System in Corsica Island," Energies, MDPI, vol. 13(17), pages 1-20, September.
   7. Kaan Ozgun, 2020. "Towards a Sustainability Assessment Model for Urban Public Space Renewable Energy Infrastructure," Energies, MDPI, vol. 13(13), pages 1-19, July.
   8. Bogdan-Constantin Neagu & Ovidiu Ivanov & Gheorghe Grigoras & Mihai Gavrilas & Dumitru-Marcel Istrate, 2020. "New Market Model with Social and Commercial Tiers for Improved Prosumer Trading in Microgrids," Sustainability, MDPI, vol. 12(18), pages 1-43, September.
   9. Nizami, Sohrab & Tushar, Wayes & Hossain, M.J. & Yuen, Chau & Saha, Tapan & Poor, H. Vincent, 2022. "Transactive energy for low voltage residential networks: A review," Applied Energy, Elsevier, vol. 323(C).
   10. Rodrigues, Stefane Dias & Garcia, Vinicius Jacques, 2023. "Transactive energy in microgrid communities: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
   11. Rafael Ninno Muniz & Stéfano Frizzo Stefenon & William Gouvêa Buratto & Ademir Nied & Luiz Henrique Meyer & Erlon Cristian Finardi & Ricardo Marino Kühl & José Alberto Silva de Sá & Brigida Ramati Per, 2020. "Tools for Measuring Energy Sustainability: A Comparative Review," Energies, MDPI, vol. 13(9), pages 1-27, May.
   12. Darren Sharp & Rob Raven, 2021. "Urban Planning by Experiment at Precinct Scale: Embracing Complexity, Ambiguity, and Multiplicity," Urban Planning, Cogitatio Press, vol. 6(1), pages 195-207.
   13. Isaías Gomes & Rui Melicio & Victor M. F. Mendes, 2021. "Assessing the Value of Demand Response in Microgrids," Sustainability, MDPI, vol. 13(11), pages 1-16, May.
   14. Pedro Faria & Zita Vale, 2022. "Realistic Load Modeling for Efficient Consumption Management Using Real-Time Simulation and Power Hardware-in-the-Loop," Energies, MDPI, vol. 16(1), pages 1-15, December.
   15. Younes Zahraoui & Ibrahim Alhamrouni & Saad Mekhilef & M. Reyasudin Basir Khan & Mehdi Seyedmahmoudian & Alex Stojcevski & Ben Horan, 2021. "Energy Management System in Microgrids: A Comprehensive Review," Sustainability, MDPI, vol. 13(19), pages 1-33, September.
   16. Younes Zahraoui & Tarmo Korõtko & Argo Rosin & Hannes Agabus, 2023. "Market Mechanisms and Trading in Microgrid Local Electricity Markets: A Comprehensive Review," Energies, MDPI, vol. 16(5), pages 1-52, February.
   17. Fábio Retorta & João Aguiar & Igor Rezende & José Villar & Bernardo Silva, 2020. "Local Market for TSO and DSO Reactive Power Provision Using DSO Grid Resources," Energies, MDPI, vol. 13(13), pages 1-19, July.
   18. Rumpa Dasgupta & Amin Sakzad & Carsten Rudolph, 2021. "Cyber Attacks in Transactive Energy Market-Based Microgrid Systems," Energies, MDPI, vol. 14(4), pages 1-17, February.
   19. Alizadeh, Ali & Kamwa, Innocent & Moeini, Ali & Mohseni-Bonab, Seyed Masoud, 2023. "Energy management in microgrids using transactive energy control concept under high penetration of Renewables; A survey and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 176(C).

2. Lusis, Peter & Khalilpour, Kaveh Rajab & Andrew, Lachlan & Liebman, Ariel, 2017. "Short-term residential load forecasting: Impact of calendar effects and forecast granularity," Applied Energy, Elsevier, vol. 205(C), pages 654-669.

   Cited by:

   1. Fan, Guo-Feng & Peng, Li-Ling & Hong, Wei-Chiang, 2018. "Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model," Applied Energy, Elsevier, vol. 224(C), pages 13-33.
   2. Fang Xu & Meng Tian & Jie Yang & Guohu Xu, 2020. "Does Environmental Inspection Led by the Central Government Improve the Air Quality in China? The Moderating Role of Public Engagement," Sustainability, MDPI, vol. 12(8), pages 1-27, April.
   3. Fermín Rodríguez & Fernando Martín & Luis Fontán & Ainhoa Galarza, 2020. "Very Short-Term Load Forecaster Based on a Neural Network Technique for Smart Grid Control," Energies, MDPI, vol. 13(19), pages 1-19, October.
   4. Barman, Mayur & Dev Choudhury, Nalin Behari, 2019. "Season specific approach for short-term load forecasting based on hybrid FA-SVM and similarity concept," Energy, Elsevier, vol. 174(C), pages 886-896.
   5. Luca Massidda & Marino Marrocu, 2018. "Smart Meter Forecasting from One Minute to One Year Horizons," Energies, MDPI, vol. 11(12), pages 1-16, December.
   6. Shepero, Mahmoud & van der Meer, Dennis & Munkhammar, Joakim & Widén, Joakim, 2018. "Residential probabilistic load forecasting: A method using Gaussian process designed for electric load data," Applied Energy, Elsevier, vol. 218(C), pages 159-172.
   7. Filipe Rodrigues & Carlos Cardeira & João M. F. Calado & Rui Melicio, 2023. "Short-Term Load Forecasting of Electricity Demand for the Residential Sector Based on Modelling Techniques: A Systematic Review," Energies, MDPI, vol. 16(10), pages 1-26, May.
   8. Burleyson, Casey D. & Rahman, Aowabin & Rice, Jennie S. & Smith, Amanda D. & Voisin, Nathalie, 2021. "Multiscale effects masked the impact of the COVID-19 pandemic on electricity demand in the United States," Applied Energy, Elsevier, vol. 304(C).
   9. Gang Chen & Qingchang Hu & Jin Wang & Xu Wang & Yuyu Zhu, 2023. "Machine-Learning-Based Electric Power Forecasting," Sustainability, MDPI, vol. 15(14), pages 1-21, July.
   10. Wenhao Chen & Guangjie Han & Hongbo Zhu & Lyuchao Liao, 2022. "Short-Term Load Forecasting with an Ensemble Model Using Densely Residual Block and Bi-LSTM Based on the Attention Mechanism," Sustainability, MDPI, vol. 14(24), pages 1-16, December.
   11. Pan, Keda & Chen, Zhaohua & Lai, Chun Sing & Xie, Changhong & Wang, Dongxiao & Li, Xuecong & Zhao, Zhuoli & Tong, Ning & Lai, Loi Lei, 2022. "An unsupervised data-driven approach for behind-the-meter photovoltaic power generation disaggregation," Applied Energy, Elsevier, vol. 309(C).
   12. Zhong, Hai & Wang, Jiajun & Jia, Hongjie & Mu, Yunfei & Lv, Shilei, 2019. "Vector field-based support vector regression for building energy consumption prediction," Applied Energy, Elsevier, vol. 242(C), pages 403-414.
   13. Kamal Chapagain & Somsak Kittipiyakul, 2018. "Performance Analysis of Short-Term Electricity Demand with Atmospheric Variables," Energies, MDPI, vol. 11(4), pages 1-34, April.
   14. Khoshrou, Abdolrahman & Pauwels, Eric J., 2019. "Short-term scenario-based probabilistic load forecasting: A data-driven approach," Applied Energy, Elsevier, vol. 238(C), pages 1258-1268.
   15. Fan, Cheng & Wang, Jiayuan & Gang, Wenjie & Li, Shenghan, 2019. "Assessment of deep recurrent neural network-based strategies for short-term building energy predictions," Applied Energy, Elsevier, vol. 236(C), pages 700-710.
   16. Sekhar, Charan & Dahiya, Ratna, 2023. "Robust framework based on hybrid deep learning approach for short term load forecasting of building electricity demand," Energy, Elsevier, vol. 268(C).
   17. Ping Ma & Shuhui Cui & Mingshuai Chen & Shengzhe Zhou & Kai Wang, 2023. "Review of Family-Level Short-Term Load Forecasting and Its Application in Household Energy Management System," Energies, MDPI, vol. 16(15), pages 1-17, August.
   18. Zang, Haixiang & Xu, Ruiqi & Cheng, Lilin & Ding, Tao & Liu, Ling & Wei, Zhinong & Sun, Guoqiang, 2021. "Residential load forecasting based on LSTM fusing self-attention mechanism with pooling," Energy, Elsevier, vol. 229(C).
   19. Singh, Priyanka & Dwivedi, Pragya, 2018. "Integration of new evolutionary approach with artificial neural network for solving short term load forecast problem," Applied Energy, Elsevier, vol. 217(C), pages 537-549.
   20. Hasan Erteza Gelani & Faizan Dastgeer & Mashood Nasir & Sidra Khan & Josep M. Guerrero, 2021. "AC vs. DC Distribution Efficiency: Are We on the Right Path?," Energies, MDPI, vol. 14(13), pages 1-26, July.
   21. Wang, Qiang & Song, Xiaoxing & Li, Rongrong, 2018. "A novel hybridization of nonlinear grey model and linear ARIMA residual correction for forecasting U.S. shale oil production," Energy, Elsevier, vol. 165(PB), pages 1320-1331.
   22. Gde Dharma Nugraha & Ardiansyah Musa & Jaiyoung Cho & Kishik Park & Deokjai Choi, 2018. "Lambda-Based Data Processing Architecture for Two-Level Load Forecasting in Residential Buildings," Energies, MDPI, vol. 11(4), pages 1-20, March.
   23. Zhang, Jinliang & Wei, Yi-Ming & Li, Dezhi & Tan, Zhongfu & Zhou, Jianhua, 2018. "Short term electricity load forecasting using a hybrid model," Energy, Elsevier, vol. 158(C), pages 774-781.
   24. Mehdi Seyedmahmoudian & Elmira Jamei & Gokul Sidarth Thirunavukkarasu & Tey Kok Soon & Michael Mortimer & Ben Horan & Alex Stojcevski & Saad Mekhilef, 2018. "Short-Term Forecasting of the Output Power of a Building-Integrated Photovoltaic System Using a Metaheuristic Approach," Energies, MDPI, vol. 11(5), pages 1-23, May.
   25. Zhang, Liang & Wen, Jin & Li, Yanfei & Chen, Jianli & Ye, Yunyang & Fu, Yangyang & Livingood, William, 2021. "A review of machine learning in building load prediction," Applied Energy, Elsevier, vol. 285(C).
   26. Zheng, Zhuang & Sun, Zhankun & Pan, Jia & Luo, Xiaowei, 2021. "An integrated smart home energy management model based on a pyramid taxonomy for residential houses with photovoltaic-battery systems," Applied Energy, Elsevier, vol. 298(C).
   27. Jihoon Moon & Junhong Kim & Pilsung Kang & Eenjun Hwang, 2020. "Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods," Energies, MDPI, vol. 13(4), pages 1-37, February.
   28. 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.
   29. Bhattacharjee, Vikram & Khan, Irfan, 2018. "A non-linear convex cost model for economic dispatch in microgrids," Applied Energy, Elsevier, vol. 222(C), pages 637-648.
   30. Ahajjam, Mohamed Aymane & Bonilla Licea, Daniel & Ghogho, Mounir & Kobbane, Abdellatif, 2022. "Experimental investigation of variational mode decomposition and deep learning for short-term multi-horizon residential electric load forecasting," Applied Energy, Elsevier, vol. 326(C).
   31. Wang, Jianzhou & Gao, Jialu & Wei, Danxiang, 2022. "Electric load prediction based on a novel combined interval forecasting system," Applied Energy, Elsevier, vol. 322(C).
   32. Michael Wood & Emanuele Ogliari & Alfredo Nespoli & Travis Simpkins & Sonia Leva, 2023. "Day Ahead Electric Load Forecast: A Comprehensive LSTM-EMD Methodology and Several Diverse Case Studies," Forecasting, MDPI, vol. 5(1), pages 1-18, March.
   33. Zheng, Peijun & Zhou, Heng & Liu, Jiang & Nakanishi, Yosuke, 2023. "Interpretable building energy consumption forecasting using spectral clustering algorithm and temporal fusion transformers architecture," Applied Energy, Elsevier, vol. 349(C).
   34. Alipour, Panteha & Mukherjee, Sayanti & Nateghi, Roshanak, 2019. "Assessing climate sensitivity of peak electricity load for resilient power systems planning and operation: A study applied to the Texas region," Energy, Elsevier, vol. 185(C), pages 1143-1153.
   35. Iuri C. Figueiró & Alzenira R. Abaide & Nelson K. Neto & Leonardo N. F. Silva & Laura L. C. Santos, 2023. "Bottom-Up Short-Term Load Forecasting Considering Macro-Region and Weighting by Meteorological Region," Energies, MDPI, vol. 16(19), pages 1-21, September.
   36. Zhang, Jinliang & Siya, Wang & Zhongfu, Tan & Anli, Sun, 2023. "An improved hybrid model for short term power load prediction," Energy, Elsevier, vol. 268(C).
   37. Dadkhah, Mojtaba & Jahangoshai Rezaee, Mustafa & Zare Chavoshi, Ahmad, 2018. "Short-term power output forecasting of hourly operation in power plant based on climate factors and effects of wind direction and wind speed," Energy, Elsevier, vol. 148(C), pages 775-788.
   38. Francesco Mancini & Gianluigi Lo Basso & Livio de Santoli, 2019. "Energy Use in Residential Buildings: Impact of Building Automation Control Systems on Energy Performance and Flexibility," Energies, MDPI, vol. 12(15), pages 1-21, July.
   39. Seyedeh Narjes Fallah & Mehdi Ganjkhani & Shahaboddin Shamshirband & Kwok-wing Chau, 2019. "Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview," Energies, MDPI, vol. 12(3), pages 1-21, January.
   40. Laib, Oussama & Khadir, Mohamed Tarek & Mihaylova, Lyudmila, 2019. "Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks," Energy, Elsevier, vol. 177(C), pages 530-542.
   41. Yukseltan, E. & Kok, A. & Yucekaya, A. & Bilge, A. & Aktunc, E. Agca & Hekimoglu, M., 2022. "The impact of the COVID-19 pandemic and behavioral restrictions on electricity consumption and the daily demand curve in Turkey," Utilities Policy, Elsevier, vol. 76(C).
   42. Ali K k & Erg n Y kseltan & Mustafa Hekimo lu & Esra Agca Aktunc & Ahmet Y cekaya & Ay e Bilge, 2022. "Forecasting Hourly Electricity Demand Under COVID-19 Restrictions," International Journal of Energy Economics and Policy, Econjournals, vol. 12(1), pages 73-85.
   43. Fazlipour, Zahra & Mashhour, Elaheh & Joorabian, Mahmood, 2022. "A deep model for short-term load forecasting applying a stacked autoencoder based on LSTM supported by a multi-stage attention mechanism," Applied Energy, Elsevier, vol. 327(C).
   44. Richter, Lucas & Lehna, Malte & Marchand, Sophie & Scholz, Christoph & Dreher, Alexander & Klaiber, Stefan & Lenk, Steve, 2022. "Artificial Intelligence for Electricity Supply Chain automation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
   45. Lee, Eunjung & Lee, Kyungeun & Lee, Hyoseop & Kim, Euncheol & Rhee, Wonjong, 2019. "Defining virtual control group to improve customer baseline load calculation of residential demand response," Applied Energy, Elsevier, vol. 250(C), pages 946-958.
   46. Park, June Young & Yang, Xiya & Miller, Clayton & Arjunan, Pandarasamy & Nagy, Zoltan, 2019. "Apples or oranges? Identification of fundamental load shape profiles for benchmarking buildings using a large and diverse dataset," Applied Energy, Elsevier, vol. 236(C), pages 1280-1295.
   47. Hussain, I. & Ali, S.M. & Khan, B. & Ullah, Z. & Mehmood, C.A. & Jawad, M. & Farid, U. & Haider, A., 2019. "Stochastic Wind Energy Management Model within smart grid framework: A joint Bi-directional Service Level Agreement (SLA) between smart grid and Wind Energy District Prosumers," Renewable Energy, Elsevier, vol. 134(C), pages 1017-1033.
   48. Li, Kun & Cursio, Joseph D. & Jiang, Mengfei & Liang, Xi, 2019. "The significance of calendar effects in the electricity market," Applied Energy, Elsevier, vol. 235(C), pages 487-494.
   49. Kamal Chapagain & Somsak Kittipiyakul & Pisut Kulthanavit, 2020. "Short-Term Electricity Demand Forecasting: Impact Analysis of Temperature for Thailand," Energies, MDPI, vol. 13(10), pages 1-29, May.
   50. Guefano, Serge & Tamba, Jean Gaston & Azong, Tchitile Emmanuel Wilfried & Monkam, Louis, 2021. "Forecast of electricity consumption in the Cameroonian residential sector by Grey and vector autoregressive models," Energy, Elsevier, vol. 214(C).
   51. Tulin Guzel & Hakan Cinar & Mehmet Nabi Cenet & Kamil Doruk Oguz & Ahmet Yucekaya & Mustafa Hekimoglu, 2023. "A Framework to Forecast Electricity Consumption of Meters using Automated Ranking and Data Preprocessing," International Journal of Energy Economics and Policy, Econjournals, vol. 13(5), pages 179-193, September.
   52. Khan, Waqas & Liao, Juo Yu & Walker, Shalika & Zeiler, Wim, 2022. "Impact assessment of varied data granularities from commercial buildings on exploration and learning mechanism," Applied Energy, Elsevier, vol. 319(C).
   53. Hyojoo Son & Changwan Kim, 2020. "A Deep Learning Approach to Forecasting Monthly Demand for Residential–Sector Electricity," Sustainability, MDPI, vol. 12(8), pages 1-16, April.
   54. Pekka Koponen & Jussi Ikäheimo & Juha Koskela & Christina Brester & Harri Niska, 2020. "Assessing and Comparing Short Term Load Forecasting Performance," Energies, MDPI, vol. 13(8), pages 1-17, April.
   55. Guo, Yabin & Wang, Jiangyu & Chen, Huanxin & Li, Guannan & Liu, Jiangyan & Xu, Chengliang & Huang, Ronggeng & Huang, Yao, 2018. "Machine learning-based thermal response time ahead energy demand prediction for building heating systems," Applied Energy, Elsevier, vol. 221(C), pages 16-27.
   56. Liu, Xin & Zhang, Zijun & Song, Zhe, 2020. "A comparative study of the data-driven day-ahead hourly provincial load forecasting methods: From classical data mining to deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
   57. Diogo M. F. Izidio & Paulo S. G. de Mattos Neto & Luciano Barbosa & João F. L. de Oliveira & Manoel Henrique da Nóbrega Marinho & Guilherme Ferretti Rissi, 2021. "Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters," Energies, MDPI, vol. 14(7), pages 1-19, March.
   58. Wang, Yi & Gan, Dahua & Sun, Mingyang & Zhang, Ning & Lu, Zongxiang & Kang, Chongqing, 2019. "Probabilistic individual load forecasting using pinball loss guided LSTM," Applied Energy, Elsevier, vol. 235(C), pages 10-20.
   59. Javier, Prince Joseph Erneszer A. & Liponhay, Marissa P. & Dajac, Carlo Vincienzo G. & Monterola, Christopher P., 2022. "Causal network inference in a dam system and its implications on feature selection for machine learning forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
   60. Marta Moure-Garrido & Celeste Campo & Carlos Garcia-Rubio, 2022. "Entropy-Based Anomaly Detection in Household Electricity Consumption," Energies, MDPI, vol. 15(5), pages 1-21, March.
   61. Alfredo Nespoli & Emanuele Ogliari & Silvia Pretto & Michele Gavazzeni & Sonia Vigani & Franco Paccanelli, 2021. "Electrical Load Forecast by Means of LSTM: The Impact of Data Quality," Forecasting, MDPI, vol. 3(1), pages 1-11, February.
   62. Fernández, Joaquín Delgado & Menci, Sergio Potenciano & Lee, Chul Min & Rieger, Alexander & Fridgen, Gilbert, 2022. "Privacy-preserving federated learning for residential short-term load forecasting," Applied Energy, Elsevier, vol. 326(C).
   63. Haben, Stephen & Arora, Siddharth & Giasemidis, Georgios & Voss, Marcus & Vukadinović Greetham, Danica, 2021. "Review of low voltage load forecasting: Methods, applications, and recommendations," Applied Energy, Elsevier, vol. 304(C).
   64. Zheng, Zhuang & Chen, Hainan & Luo, Xiaowei, 2019. "A Kalman filter-based bottom-up approach for household short-term load forecast," Applied Energy, Elsevier, vol. 250(C), pages 882-894.
   65. Sarah Hadri & Mehdi Najib & Mohamed Bakhouya & Youssef Fakhri & Mohamed El Arroussi, 2021. "Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings," Energies, MDPI, vol. 14(18), pages 1-17, September.
   66. Thomas Steens & Jan-Simon Telle & Benedikt Hanke & Karsten von Maydell & Carsten Agert & Gian-Luca Di Modica & Bernd Engel & Matthias Grottke, 2021. "A Forecast-Based Load Management Approach for Commercial Buildings Demonstrated on an Integration of BEV," Energies, MDPI, vol. 14(12), pages 1-25, June.
   67. Li, Lechen & Meinrenken, Christoph J. & Modi, Vijay & Culligan, Patricia J., 2021. "Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features," Applied Energy, Elsevier, vol. 287(C).
   68. Minseok Jang & Hyun Cheol Jeong & Taegon Kim & Dong Hee Suh & Sung-Kwan Joo, 2021. "Empirical Analysis of the Impact of COVID-19 Social Distancing on Residential Electricity Consumption Based on Demographic Characteristics and Load Shape," Energies, MDPI, vol. 14(22), pages 1-15, November.

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NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 4 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.

1. NEP-ENE: Energy Economics (4) 2011-12-19 2011-12-19 2016-04-04 2017-05-14
2. NEP-REG: Regulation (2) 2016-04-04 2017-05-14
3. NEP-CMP: Computational Economics (1) 2016-04-04

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