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Triple seasonal methods for short-term electricity demand forecasting

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Cited by:

  1. Edgar Segovia & Vladimir Vukovic & Tommaso Bragatto, 2021. "Comparison of Baseline Load Forecasting Methodologies for Active and Reactive Power Demand," Energies, MDPI, vol. 14(22), pages 1-14, November.
  2. Florian Ziel & Rick Steinert & Sven Husmann, 2015. "Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets," Papers 1501.00818, arXiv.org, revised Dec 2015.
  3. Ayman A. Amin & Saeed A. Alghamdi, 2023. "Bayesian Identification Procedure for Triple Seasonal Autoregressive Models," Mathematics, MDPI, vol. 11(18), pages 1-13, September.
  4. Trull, Oscar & García-Díaz, J. Carlos & Troncoso, Alicia, 2021. "One-day-ahead electricity demand forecasting in holidays using discrete-interval moving seasonalities," Energy, Elsevier, vol. 231(C).
  5. Gökay Yörük & Ugur Bac & Fatma Yerlikaya-Özkurt & Kamil Demirberk Ünlü, 2023. "Strategic Electricity Production Planning of Turkey via Mixed Integer Programming Based on Time Series Forecasting," Mathematics, MDPI, vol. 11(8), pages 1-20, April.
  6. Yang, Dazhi & Sharma, Vishal & Ye, Zhen & Lim, Lihong Idris & Zhao, Lu & Aryaputera, Aloysius W., 2015. "Forecasting of global horizontal irradiance by exponential smoothing, using decompositions," Energy, Elsevier, vol. 81(C), pages 111-119.
  7. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
  8. Reisen, Valdério A. & Zamprogno, Bartolomeu & Palma, Wilfredo & Arteche, Josu, 2014. "A semiparametric approach to estimate two seasonal fractional parameters in the SARFIMA model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 98(C), pages 1-17.
  9. Janusz Sowinski, 2021. "The Impact of the Selection of Exogenous Variables in the ANFIS Model on the Results of the Daily Load Forecast in the Power Company," Energies, MDPI, vol. 14(2), pages 1-18, January.
  10. Xiao, Xun & Mo, Huadong & Zhang, Yinan & Shan, Guangcun, 2022. "Meta-ANN – A dynamic artificial neural network refined by meta-learning for Short-Term Load Forecasting," Energy, Elsevier, vol. 246(C).
  11. Bessec, Marie & Fouquau, Julien, 2018. "Short-run electricity load forecasting with combinations of stationary wavelet transforms," European Journal of Operational Research, Elsevier, vol. 264(1), pages 149-164.
  12. Zhu, Suling & Wang, Jianzhou & Zhao, Weigang & Wang, Jujie, 2011. "A seasonal hybrid procedure for electricity demand forecasting in China," Applied Energy, Elsevier, vol. 88(11), pages 3807-3815.
  13. Moreno, Manuel & Novales, Alfonso & Platania, Federico, 2019. "Long-term swings and seasonality in energy markets," European Journal of Operational Research, Elsevier, vol. 279(3), pages 1011-1023.
  14. 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.
  15. Marie Bessec & Julien Fouquau & Sophie Meritet, 2016. "Forecasting electricity spot prices using time-series models with a double temporal segmentation," Applied Economics, Taylor & Francis Journals, vol. 48(5), pages 361-378, January.
  16. Jun-Hyeok Kim & Byung-Sung Lee & Chul-Hwan Kim, 2020. "A Study on the Development of Machine-Learning Based Load Transfer Detection Algorithm for Distribution Planning," Energies, MDPI, vol. 13(17), pages 1-12, August.
  17. Jasiński, Tomasz, 2022. "A new approach to modeling cycles with summer and winter demand peaks as input variables for deep neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
  18. Behm, Svenia & Haupt, Harry, 2020. "Predictability of hourly nitrogen dioxide concentration," Ecological Modelling, Elsevier, vol. 428(C).
  19. Jinseok Kim & Hyungseop Hong & Ki-Il Kim, 2018. "Adaptive Optimized Pattern Extracting Algorithm for Forecasting Maximum Electrical Load Duration Using Random Sampling and Cumulative Slope Index," Energies, MDPI, vol. 11(7), pages 1-23, July.
  20. Nurcan Kilinc-Ata, 2018. "Assessing the Future of Renewable Energy Consumption for United Kingdom, Turkey and Nigeria," Foresight and STI Governance (Foresight-Russia till No. 3/2015), National Research University Higher School of Economics, vol. 12(4), pages 62-77.
  21. Aftab Ahmed Almani & Xueshan Han, 2023. "Real-Time Pricing-Enabled Demand Response Using Long Short-Time Memory Deep Learning," Energies, MDPI, vol. 16(5), pages 1-13, March.
  22. Mauro Bernardi & Francesco Lisi, 2020. "Point and Interval Forecasting of Zonal Electricity Prices and Demand Using Heteroscedastic Models: The IPEX Case," Energies, MDPI, vol. 13(23), pages 1-34, November.
  23. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
  24. repec:dau:papers:123456789/13532 is not listed on IDEAS
  25. Bennett, Christopher J. & Stewart, Rodney A. & Lu, Jun Wei, 2014. "Forecasting low voltage distribution network demand profiles using a pattern recognition based expert system," Energy, Elsevier, vol. 67(C), pages 200-212.
  26. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
  27. Lee, Juyong & Cho, Youngsang, 2022. "National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?," Energy, Elsevier, vol. 239(PD).
  28. Umut Ugurlu & Ilkay Oksuz & Oktay Tas, 2018. "Electricity Price Forecasting Using Recurrent Neural Networks," Energies, MDPI, vol. 11(5), pages 1-23, May.
  29. Winita Sulandari & Yudho Yudhanto & Paulo Canas Rodrigues, 2022. "The Use of Singular Spectrum Analysis and K-Means Clustering-Based Bootstrap to Improve Multistep Ahead Load Forecasting," Energies, MDPI, vol. 15(16), pages 1-22, August.
  30. AL-Musaylh, Mohanad S. & Deo, Ravinesh C. & Adamowski, Jan F. & Li, Yan, 2019. "Short-term electricity demand forecasting using machine learning methods enriched with ground-based climate and ECMWF Reanalysis atmospheric predictors in southeast Queensland, Australia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
  31. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.
  32. Goutam Dutta & Krishnendranath Mitra, 2017. "A literature review on dynamic pricing of electricity," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(10), pages 1131-1145, October.
  33. Alobaidi, Mohammad H. & Chebana, Fateh & Meguid, Mohamed A., 2018. "Robust ensemble learning framework for day-ahead forecasting of household based energy consumption," Applied Energy, Elsevier, vol. 212(C), pages 997-1012.
  34. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
  35. Antoniadis, Anestis & Brossat, Xavier & Cugliari, Jairo & Poggi, Jean-Michel, 2016. "A prediction interval for a function-valued forecast model: Application to load forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 939-947.
  36. repec:qut:auncer:wp103 is not listed on IDEAS
  37. Silva, Hendrigo Batista da & Santiago, Leonardo P., 2018. "On the trade-off between real-time pricing and the social acceptability costs of demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1513-1521.
  38. Zhineng Hu & Jing Ma & Liangwei Yang & Liming Yao & Meng Pang, 2019. "Monthly electricity demand forecasting using empirical mode decomposition-based state space model," Energy & Environment, , vol. 30(7), pages 1236-1254, November.
  39. Yukseltan, Ergun & Yucekaya, Ahmet & Bilge, Ayse Humeyra, 2017. "Forecasting electricity demand for Turkey: Modeling periodic variations and demand segregation," Applied Energy, Elsevier, vol. 193(C), pages 287-296.
  40. Rendon-Sanchez, Juan F. & de Menezes, Lilian M., 2019. "Structural combination of seasonal exponential smoothing forecasts applied to load forecasting," European Journal of Operational Research, Elsevier, vol. 275(3), pages 916-924.
  41. Kostas Hatalis & Chengbo Zhao & Parv Venkitasubramaniam & Larry Snyder & Shalinee Kishore & Rick S. Blum, 2020. "Modeling and Detection of Future Cyber-Enabled DSM Data Attacks," Energies, MDPI, vol. 13(17), pages 1-27, August.
  42. Saâdaoui, Foued & Ben Jabeur, Sami, 2023. "Analyzing the influence of geopolitical risks on European power prices using a multiresolution causal neural network," Energy Economics, Elsevier, vol. 124(C).
  43. Clements, A.E. & Hurn, A.S. & Li, Z., 2016. "Forecasting day-ahead electricity load using a multiple equation time series approach," European Journal of Operational Research, Elsevier, vol. 251(2), pages 522-530.
  44. Short, Michael & Crosbie, Tracey & Dawood, Muneeb & Dawood, Nashwan, 2017. "Load forecasting and dispatch optimisation for decentralised co-generation plant with dual energy storage," Applied Energy, Elsevier, vol. 186(P3), pages 304-320.
  45. Heung-gu Son & Yunsun Kim & Sahm Kim, 2020. "Time Series Clustering of Electricity Demand for Industrial Areas on Smart Grid," Energies, MDPI, vol. 13(9), pages 1-14, May.
  46. 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.
  47. 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.
  48. Dutta, Goutam & Mitra, Krishnendranath, 2015. "Dynamic Pricing of Electricity: A Survey of Related Research," IIMA Working Papers WP2015-08-03, Indian Institute of Management Ahmedabad, Research and Publication Department.
  49. Cho, Haeran & Goude, Yannig & Brossat, Xavier & Yao, Qiwei, 2013. "Modeling and forecasting daily electricity load curves: a hybrid approach," LSE Research Online Documents on Economics 49634, London School of Economics and Political Science, LSE Library.
  50. Brenda López Cabrera & Franziska Schulz, 2017. "Forecasting Generalized Quantiles of Electricity Demand: A Functional Data Approach," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 127-136, January.
  51. So, Mike K.P. & Chung, Ray S.W., 2014. "Dynamic seasonality in time series," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 212-226.
  52. David Wozabal & Christoph Graf & David Hirschmann, 2016. "The effect of intermittent renewables on the electricity price variance," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 38(3), pages 687-709, July.
  53. Christopher Bennett & Rodney A. Stewart & Junwei Lu, 2014. "Autoregressive with Exogenous Variables and Neural Network Short-Term Load Forecast Models for Residential Low Voltage Distribution Networks," Energies, MDPI, vol. 7(5), pages 1-23, April.
  54. 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.
  55. Arora, Siddharth & Taylor, James W., 2016. "Forecasting electricity smart meter data using conditional kernel density estimation," Omega, Elsevier, vol. 59(PA), pages 47-59.
  56. Faheem Jan & Ismail Shah & Sajid Ali, 2022. "Short-Term Electricity Prices Forecasting Using Functional Time Series Analysis," Energies, MDPI, vol. 15(9), pages 1-15, May.
  57. Chethana Dharmawardane & Ville Sillanpää & Jan Holmström, 2021. "High-frequency forecasting for grocery point-of-sales: intervention in practice and theoretical implications for operational design," Operations Management Research, Springer, vol. 14(1), pages 38-60, June.
  58. Wang, Yuanyuan & Wang, Jianzhou & Zhao, Ge & Dong, Yao, 2012. "Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China," Energy Policy, Elsevier, vol. 48(C), pages 284-294.
  59. Kim, Myung Suk, 2013. "Modeling special-day effects for forecasting intraday electricity demand," European Journal of Operational Research, Elsevier, vol. 230(1), pages 170-180.
  60. Agnieszka Mazurek-Czarnecka & Ksymena Rosiek & Marcin Salamaga & Krzysztof Wąsowicz & Renata Żaba-Nieroda, 2022. "Study on Support Mechanisms for Renewable Energy Sources in Poland," Energies, MDPI, vol. 15(12), pages 1-38, June.
  61. 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.
  62. Apostolos Ampountolas, 2021. "Modeling and Forecasting Daily Hotel Demand: A Comparison Based on SARIMAX, Neural Networks, and GARCH Models," Forecasting, MDPI, vol. 3(3), pages 1-16, August.
  63. Cristina Rottondi & Markus Duchon & Dagmar Koss & Andrei Palamarciuc & Alessandro Pití & Giacomo Verticale & Bernhard Schätz, 2015. "An Energy Management Service for the Smart Office," Energies, MDPI, vol. 8(10), pages 1-18, October.
  64. Jose Juan Caceres-Hernandez & Gloria Martin-Rodriguez & Jonay Hernandez-Martin, 2022. "A proposal for measuring and comparing seasonal variations in hourly economic time series," Empirical Economics, Springer, vol. 62(4), pages 1995-2021, April.
  65. Do, Hung Xuan & Nepal, Rabindra & Pham, Son Duy & Jamasb, Tooraj, 2023. "Electricity Market Crisis in Europe and Cross Border Price Effects: A Quantile Return Connectedness Analysis," Working Papers 8-2023, Copenhagen Business School, Department of Economics.
  66. Moral-Carcedo, Julián & Pérez-García, Julián, 2019. "Time of day effects of temperature and daylight on short term electricity load," Energy, Elsevier, vol. 174(C), pages 169-183.
  67. Yunsun Kim & Sahm Kim, 2021. "Electricity Load and Internet Traffic Forecasting Using Vector Autoregressive Models," Mathematics, MDPI, vol. 9(18), pages 1-15, September.
  68. Weide Li & Demeng Kong & Jinran Wu, 2017. "A Novel Hybrid Model Based on Extreme Learning Machine, k-Nearest Neighbor Regression and Wavelet Denoising Applied to Short-Term Electric Load Forecasting," Energies, MDPI, vol. 10(5), pages 1-16, May.
  69. Arora, Siddharth & Taylor, James W., 2018. "Rule-based autoregressive moving average models for forecasting load on special days: A case study for France," European Journal of Operational Research, Elsevier, vol. 266(1), pages 259-268.
  70. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
  71. Ahmed Ismail & Mustafa Baysal, 2023. "Dynamic Pricing Based on Demand Response Using Actor–Critic Agent Reinforcement Learning," Energies, MDPI, vol. 16(14), pages 1-19, July.
  72. Óscar Trull & J. Carlos García-Díaz & Alicia Troncoso, 2019. "Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter," Energies, MDPI, vol. 12(6), pages 1-16, March.
  73. Oscar Trull & Juan Carlos García-Díaz & Alicia Troncoso, 2020. "Initialization Methods for Multiple Seasonal Holt–Winters Forecasting Models," Mathematics, MDPI, vol. 8(2), pages 1-16, February.
  74. Tang, Tao & Jiang, Weiheng & Zhang, Hui & Nie, Jiangtian & Xiong, Zehui & Wu, Xiaogang & Feng, Wenjiang, 2022. "GM(1,1) based improved seasonal index model for monthly electricity consumption forecasting," Energy, Elsevier, vol. 252(C).
  75. Oscar Trull & Angel Peiró-Signes & J. Carlos García-Díaz, 2019. "Electricity Forecasting Improvement in a Destination Using Tourism Indicators," Sustainability, MDPI, vol. 11(13), pages 1-16, July.
  76. Oscar Trull & J. Carlos Garc'ia-D'iaz & Angel Peir'o-Signes, 2024. "mshw, a forecasting library to predict short-term electricity demand based on multiple seasonal Holt-Winters," Papers 2402.10982, arXiv.org.
  77. 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.
  78. Xing, Yazhou & Zhang, Su & Wen, Peng & Shao, Limin & Rouyendegh, Babak Daneshvar, 2020. "Load prediction in short-term implementing the multivariate quantile regression," Energy, Elsevier, vol. 196(C).
  79. Habeebur Rahman & Iniyan Selvarasan & Jahitha Begum A, 2018. "Short-Term Forecasting of Total Energy Consumption for India-A Black Box Based Approach," Energies, MDPI, vol. 11(12), pages 1-21, December.
  80. 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.
  81. Bordignon, Silvano & Bunn, Derek W. & Lisi, Francesco & Nan, Fany, 2013. "Combining day-ahead forecasts for British electricity prices," Energy Economics, Elsevier, vol. 35(C), pages 88-103.
  82. Vu, D.H. & Muttaqi, K.M. & Agalgaonkar, A.P. & Bouzerdoum, A., 2017. "Short-term electricity demand forecasting using autoregressive based time varying model incorporating representative data adjustment," Applied Energy, Elsevier, vol. 205(C), pages 790-801.
  83. Hasnain Iftikhar & Josue E. Turpo-Chaparro & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2023. "Day-Ahead Electricity Demand Forecasting Using a Novel Decomposition Combination Method," Energies, MDPI, vol. 16(18), pages 1-22, September.
  84. Malo Huard & Rémy Garnier & Gilles Stoltz, 2020. "Hierarchical robust aggregation of sales forecasts at aggregated levels in e-commerce, based on exponential smoothing and Holt's linear trend method," Working Papers hal-02794320, HAL.
  85. Moral-Carcedo, Julián & Pérez-García, Julián, 2017. "Integrating long-term economic scenarios into peak load forecasting: An application to Spain," Energy, Elsevier, vol. 140(P1), pages 682-695.
  86. Feng, Yonghan & Ryan, Sarah M., 2016. "Day-ahead hourly electricity load modeling by functional regression," Applied Energy, Elsevier, vol. 170(C), pages 455-465.
  87. 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.
  88. Souhaib Ben Taieb & Raphael Huser & Rob J. Hyndman & Marc G. Genton, 2015. "Probabilistic time series forecasting with boosted additive models: an application to smart meter data," Monash Econometrics and Business Statistics Working Papers 12/15, Monash University, Department of Econometrics and Business Statistics.
  89. Ziel, Florian & Steinert, Rick & Husmann, Sven, 2015. "Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets," Energy Economics, Elsevier, vol. 51(C), pages 430-444.
  90. He, Yaoyao & Qin, Yang & Wang, Shuo & Wang, Xu & Wang, Chao, 2019. "Electricity consumption probability density forecasting method based on LASSO-Quantile Regression Neural Network," Applied Energy, Elsevier, vol. 233, pages 565-575.
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