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Electric load forecasting methods: Tools for decision making

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  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. Jihoon Moon & Yongsung Kim & Minjae Son & Eenjun Hwang, 2018. "Hybrid Short-Term Load Forecasting Scheme Using Random Forest and Multilayer Perceptron," Energies, MDPI, vol. 11(12), pages 1-20, November.
  3. Panapakidis, Ioannis P. & Dagoumas, Athanasios S., 2016. "Day-ahead electricity price forecasting via the application of artificial neural network based models," Applied Energy, Elsevier, vol. 172(C), pages 132-151.
  4. Voulis, Nina & Warnier, Martijn & Brazier, Frances M.T., 2017. "Impact of service sector loads on renewable resource integration," Applied Energy, Elsevier, vol. 205(C), pages 1311-1326.
  5. Marin Cerjan & Ana Petričić & Marko Delimar, 2019. "HIRA Model for Short-Term Electricity Price Forecasting," Energies, MDPI, vol. 12(3), pages 1-32, February.
  6. Yongquan Dong & Zichen Zhang & Wei-Chiang Hong, 2018. "A Hybrid Seasonal Mechanism with a Chaotic Cuckoo Search Algorithm with a Support Vector Regression Model for Electric Load Forecasting," Energies, MDPI, vol. 11(4), pages 1-21, April.
  7. Cabral, Joilson de Assis & Legey, Luiz Fernando Loureiro & Freitas Cabral, Maria Viviana de, 2017. "Electricity consumption forecasting in Brazil: A spatial econometrics approach," Energy, Elsevier, vol. 126(C), pages 124-131.
  8. Hossein Iranmanesh & Majid Abdollahzade & Arash Miranian, 2011. "Mid-Term Energy Demand Forecasting by Hybrid Neuro-Fuzzy Models," Energies, MDPI, vol. 5(1), pages 1-21, December.
  9. Zhang Yue & Arash Farnoosh, 2018. "Analysing the Dynamic Impact of Electricity Futures on Revenue and Risks of Renewable Energy in China," Working Papers hal-03188814, HAL.
  10. Wang, Yong & Li, Lin, 2016. "Critical peak electricity pricing for sustainable manufacturing: Modeling and case studies," Applied Energy, Elsevier, vol. 175(C), pages 40-53.
  11. Peplinski, McKenna & Mayes, Stepp & Sanders, Kelly T., 2025. "Revealing spatial and temporal patterns of residential cooling in Southern California through combined estimates of AC ownership and use," Applied Energy, Elsevier, vol. 377(PC).
  12. Min-Liang Huang, 2016. "Hybridization of Chaotic Quantum Particle Swarm Optimization with SVR in Electric Demand Forecasting," Energies, MDPI, vol. 9(6), pages 1, May.
  13. 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.
  14. Safiullah, Hameed, 2011. "Evaluation of Grid Level Impacts of Electric Vehicles," MPRA Paper 58517, University Library of Munich, Germany.
  15. Alexandros Menelaos Tzortzis & Sotiris Pelekis & Evangelos Spiliotis & Evangelos Karakolis & Spiros Mouzakitis & John Psarras & Dimitris Askounis, 2023. "Transfer Learning for Day-Ahead Load Forecasting: A Case Study on European National Electricity Demand Time Series," Mathematics, MDPI, vol. 12(1), pages 1-24, December.
  16. Sun, Jian & Liu, Gang & Sun, Boyang & Xiao, Gang, 2021. "Light-stacking strengthened fusion based building energy consumption prediction framework via variable weight feature selection," Applied Energy, Elsevier, vol. 303(C).
  17. Hildebrandt, Benjamin & Hurink, Johann & Manitz, Michael, 2024. "Local energy management: A base model for the optimization of virtual economic units," Energy Economics, Elsevier, vol. 129(C).
  18. Tanrisever, Fehmi & Derinkuyu, Kursad & Heeren, Michael, 2013. "Forecasting electricity infeed for distribution system networks: An analysis of the Dutch case," Energy, Elsevier, vol. 58(C), pages 247-257.
  19. Pavel V. Matrenin & Vadim Z. Manusov & Alexandra I. Khalyasmaa & Dmitry V. Antonenkov & Stanislav A. Eroshenko & Denis N. Butusov, 2020. "Improving Accuracy and Generalization Performance of Small-Size Recurrent Neural Networks Applied to Short-Term Load Forecasting," Mathematics, MDPI, vol. 8(12), pages 1-17, December.
  20. 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.
  21. Le Cam, M. & Daoud, A. & Zmeureanu, R., 2016. "Forecasting electric demand of supply fan using data mining techniques," Energy, Elsevier, vol. 101(C), pages 541-557.
  22. Kamal Chapagain & Somsak Kittipiyakul, 2018. "Performance Analysis of Short-Term Electricity Demand with Atmospheric Variables," Energies, MDPI, vol. 11(4), pages 1-34, April.
  23. Yongtong Shao & Tao Xiong & Minghao Li & Dermot Hayes & Wendong Zhang & Wei Xie, 2021. "China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1082-1098, May.
  24. Kiguchi, Y. & Weeks, M. & Arakawa, R., 2021. "Predicting winners and losers under time-of-use tariffs using smart meter data," Energy, Elsevier, vol. 236(C).
  25. Jens Hönen & Johann L. Hurink & Bert Zwart, 2023. "A classification scheme for local energy trading," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 85-118, March.
  26. Jessica Walther & Matthias Weigold, 2021. "A Systematic Review on Predicting and Forecasting the Electrical Energy Consumption in the Manufacturing Industry," Energies, MDPI, vol. 14(4), pages 1-24, February.
  27. Hasnain Iftikhar & Nadeela Bibi & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2023. "Multiple Novel Decomposition Techniques for Time Series Forecasting: Application to Monthly Forecasting of Electricity Consumption in Pakistan," Energies, MDPI, vol. 16(6), pages 1-17, March.
  28. 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.
  29. Cheng-Wen Lee & Bing-Yi Lin, 2016. "Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting," Energies, MDPI, vol. 9(11), pages 1, October.
  30. Rodolfo Gordillo-Orquera & Luis Miguel Lopez-Ramos & Sergio Muñoz-Romero & Paz Iglesias-Casarrubios & Diego Arcos-Avilés & Antonio G. Marques & José Luis Rojo-Álvarez, 2018. "Analyzing and Forecasting Electrical Load Consumption in Healthcare Buildings," Energies, MDPI, vol. 11(3), pages 1-18, February.
  31. Lebotsa, Moshoko Emily & Sigauke, Caston & Bere, Alphonce & Fildes, Robert & Boylan, John E., 2018. "Short term electricity demand forecasting using partially linear additive quantile regression with an application to the unit commitment problem," Applied Energy, Elsevier, vol. 222(C), pages 104-118.
  32. Xiaoxin Zhu & Yanyan Wang & David Regan & Baiqing Sun, 2020. "A Quantitative Study on Crucial Food Supplies after the 2011 Tohoku Earthquake Based on Time Series Analysis," IJERPH, MDPI, vol. 17(19), pages 1-13, September.
  33. Ewa Chodakowska & Joanicjusz Nazarko & Łukasz Nazarko, 2021. "ARIMA Models in Electrical Load Forecasting and Their Robustness to Noise," Energies, MDPI, vol. 14(23), pages 1-22, November.
  34. Nasir Ayub & Muhammad Irfan & Muhammad Awais & Usman Ali & Tariq Ali & Mohammed Hamdi & Abdullah Alghamdi & Fazal Muhammad, 2020. "Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler," Energies, MDPI, vol. 13(19), pages 1-21, October.
  35. Roshanak Nateghi & Sayanti Mukherjee, 2017. "A multi-paradigm framework to assess the impacts of climate change on end-use energy demand," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-23, November.
  36. Zhou, Kaile & Fu, Chao & Yang, Shanlin, 2016. "Big data driven smart energy management: From big data to big insights," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 215-225.
  37. Apadula, Francesco & Bassini, Alessandra & Elli, Alberto & Scapin, Simone, 2012. "Relationships between meteorological variables and monthly electricity demand," Applied Energy, Elsevier, vol. 98(C), pages 346-356.
  38. Nystrup, Peter & Lindström, Erik & Møller, Jan K. & Madsen, Henrik, 2021. "Dimensionality reduction in forecasting with temporal hierarchies," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1127-1146.
  39. Y, Kiguchi & Y, Heo & M, Weeks & R, Choudhary, 2019. "Predicting intra-day load profiles under time-of-use tariffs using smart meter data," Energy, Elsevier, vol. 173(C), pages 959-970.
  40. Cheng-Wen Lee & Bing-Yi Lin, 2017. "Applications of the Chaotic Quantum Genetic Algorithm with Support Vector Regression in Load Forecasting," Energies, MDPI, vol. 10(11), pages 1, November.
  41. Christian Pape, 2017. "The impact of intraday markets on the market value of flexibility–Decomposing effects on profile and the imbalance costs," EWL Working Papers 1711, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Dec 2017.
  42. Adeshina Y. Alani & Isaac O. Osunmakinde, 2017. "Short-Term Multiple Forecasting of Electric Energy Loads for Sustainable Demand Planning in Smart Grids for Smart Homes," Sustainability, MDPI, vol. 9(11), pages 1-27, October.
  43. Pape, Christian, 2018. "The impact of intraday markets on the market value of flexibility — Decomposing effects on profile and the imbalance costs," Energy Economics, Elsevier, vol. 76(C), pages 186-201.
  44. Arpita Samanta Santra & Cheng-Chin Taso & Pei-Chann Chang, 2017. "An artificial immune network based novel approach to predict short term load forecasting," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 3(3), pages 79-88.
  45. Hickey, Emily & Loomis, David G. & Mohammadi, Hassan, 2012. "Forecasting hourly electricity prices using ARMAX–GARCH models: An application to MISO hubs," Energy Economics, Elsevier, vol. 34(1), pages 307-315.
  46. Kück, Mirko & Freitag, Michael, 2021. "Forecasting of customer demands for production planning by local k-nearest neighbor models," International Journal of Production Economics, Elsevier, vol. 231(C).
  47. Semmelmann, Leo & Hertel, Matthias & Kircher, Kevin J. & Mikut, Ralf & Hagenmeyer, Veit & Weinhardt, Christof, 2024. "The impact of heat pumps on day-ahead energy community load forecasting," Applied Energy, Elsevier, vol. 368(C).
  48. Kaur, Amanpreet & Nonnenmacher, Lukas & Coimbra, Carlos F.M., 2016. "Net load forecasting for high renewable energy penetration grids," Energy, Elsevier, vol. 114(C), pages 1073-1084.
  49. Sumit Saroha & Marta Zurek-Mortka & Jerzy Ryszard Szymanski & Vineet Shekher & Pardeep Singla, 2021. "Forecasting of Market Clearing Volume Using Wavelet Packet-Based Neural Networks with Tracking Signals," Energies, MDPI, vol. 14(19), pages 1-21, September.
  50. Saini, Priyesh & Parida, S.K., 2024. "A novel probabilistic gradient boosting model with multi-approach feature selection and iterative seasonal trend decomposition for short-term load forecasting," Energy, Elsevier, vol. 294(C).
  51. Zhao, Weigang & Wang, Jianzhou & Lu, Haiyan, 2014. "Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model," Omega, Elsevier, vol. 45(C), pages 80-91.
  52. Lujano-Rojas, Juan M. & Monteiro, Cláudio & Dufo-López, Rodolfo & Bernal-Agustín, José L., 2012. "Optimum residential load management strategy for real time pricing (RTP) demand response programs," Energy Policy, Elsevier, vol. 45(C), pages 671-679.
  53. Marcin Olkiewicz & Anna Olkiewicz & Radosław Wolniak & Adam Wyszomirski, 2021. "Effects of Pro-Ecological Investments on an Example of the Heating Industry—Case Study," Energies, MDPI, vol. 14(18), pages 1-24, September.
  54. Dordonnat, Virginie & Koopman, Siem Jan & Ooms, Marius, 2012. "Dynamic factors in periodic time-varying regressions with an application to hourly electricity load modelling," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3134-3152.
  55. Ömer Özgür Bozkurt & Göksel Biricik & Ziya Cihan Tayşi, 2017. "Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-24, April.
  56. Luo, Jian & Hong, Tao & Gao, Zheming & Fang, Shu-Cherng, 2023. "A robust support vector regression model for electric load forecasting," International Journal of Forecasting, Elsevier, vol. 39(2), pages 1005-1020.
  57. Samuel Atuahene & Yukun Bao & Patricia Semwaah Gyan & Yao Yevenyo Ziggah, 2019. "Accurate Forecast Improvement Approach for Short Term Load Forecasting Using Hybrid Filter-Wrap Feature Selection," International Journal of Management Science and Business Administration, Inovatus Services Ltd., vol. 5(2), pages 37-49, January.
  58. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
  59. Angelopoulos, Dimitrios & Siskos, Yannis & Psarras, John, 2019. "Disaggregating time series on multiple criteria for robust forecasting: The case of long-term electricity demand in Greece," European Journal of Operational Research, Elsevier, vol. 275(1), pages 252-265.
  60. AlShelahi, Abdullah & Wang, Jingxing & You, Mingdi & Byon, Eunshin & Saigal, Romesh, 2020. "Data-driven prediction for volatile processes based on real option theories," International Journal of Production Economics, Elsevier, vol. 226(C).
  61. Leonard Burg & Gonca Gürses-Tran & Reinhard Madlener & Antonello Monti, 2021. "Comparative Analysis of Load Forecasting Models for Varying Time Horizons and Load Aggregation Levels," Energies, MDPI, vol. 14(21), pages 1-16, November.
  62. Hu, Zhongyi & Bao, Yukun & Chiong, Raymond & Xiong, Tao, 2015. "Mid-term interval load forecasting using multi-output support vector regression with a memetic algorithm for feature selection," Energy, Elsevier, vol. 84(C), pages 419-431.
  63. Mestekemper, Thomas & Kauermann, Göran & Smith, Michael S., 2013. "A comparison of periodic autoregressive and dynamic factor models in intraday energy demand forecasting," International Journal of Forecasting, Elsevier, vol. 29(1), pages 1-12.
  64. Möst, Dominik & Keles, Dogan, 2010. "A survey of stochastic modelling approaches for liberalised electricity markets," European Journal of Operational Research, Elsevier, vol. 207(2), pages 543-556, December.
  65. Hafize Nurgul Durmus Senyapar & Ahmet Aksoz, 2024. "Empowering Sustainability: A Consumer-Centric Analysis Based on Advanced Electricity Consumption Predictions," Sustainability, MDPI, vol. 16(7), pages 1-23, April.
  66. Troy Malatesta & Jessica K. Breadsell, 2022. "Identifying Home System of Practices for Energy Use with K-Means Clustering Techniques," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
  67. Fan, Cheng & Xiao, Fu & Wang, Shengwei, 2014. "Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques," Applied Energy, Elsevier, vol. 127(C), pages 1-10.
  68. Luca Massidda & Marino Marrocu, 2017. "Decoupling Weather Influence from User Habits for an Optimal Electric Load Forecast System," Energies, MDPI, vol. 10(12), pages 1, December.
  69. Li, Han & Johra, Hicham & de Andrade Pereira, Flavia & Hong, Tianzhen & Le Dréau, Jérôme & Maturo, Anthony & Wei, Mingjun & Liu, Yapan & Saberi-Derakhtenjani, Ali & Nagy, Zoltan & Marszal-Pomianowska,, 2023. "Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives," Applied Energy, Elsevier, vol. 343(C).
  70. Ghulam Hafeez & Khurram Saleem Alimgeer & Zahid Wadud & Zeeshan Shafiq & Mohammad Usman Ali Khan & Imran Khan & Farrukh Aslam Khan & Abdelouahid Derhab, 2020. "A Novel Accurate and Fast Converging Deep Learning-Based Model for Electrical Energy Consumption Forecasting in a Smart Grid," Energies, MDPI, vol. 13(9), pages 1-25, May.
  71. Jaime Buitrago & Shihab Asfour, 2017. "Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Vector Inputs," Energies, MDPI, vol. 10(1), pages 1, January.
  72. Moustris, K. & Kavadias, K.A. & Zafirakis, D. & Kaldellis, J.K., 2020. "Medium, short and very short-term prognosis of load demand for the Greek Island of Tilos using artificial neural networks and human thermal comfort-discomfort biometeorological data," Renewable Energy, Elsevier, vol. 147(P1), pages 100-109.
  73. 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.
  74. Arpita Samanta Santra & Jun-Lin Lin, 2019. "Integrating Long Short-Term Memory and Genetic Algorithm for Short-Term Load Forecasting," Energies, MDPI, vol. 12(11), pages 1-11, May.
  75. Silva, Felipe L.C. & Souza, Reinaldo C. & Cyrino Oliveira, Fernando L. & Lourenco, Plutarcho M. & Calili, Rodrigo F., 2018. "A bottom-up methodology for long term electricity consumption forecasting of an industrial sector - Application to pulp and paper sector in Brazil," Energy, Elsevier, vol. 144(C), pages 1107-1118.
  76. Mahmoud Shaban & Mohammed F. Alsharekh, 2022. "Design of a Smart Distribution Panelboard Using IoT Connectivity and Machine Learning Techniques," Energies, MDPI, vol. 15(10), pages 1-17, May.
  77. Pinheiro, Marco G. & Madeira, Sara C. & Francisco, Alexandre P., 2023. "Short-term electricity load forecasting—A systematic approach from system level to secondary substations," Applied Energy, Elsevier, vol. 332(C).
  78. Raza, Muhammad Qamar & Khosravi, Abbas, 2015. "A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1352-1372.
  79. Safiullah, Hameed, 2011. "Evaluation of Grid Level Impacts of Electric Vehicles," MPRA Paper 59175, University Library of Munich, Germany.
  80. Sun, Bixuan & Eryilmaz, Derya & Konidena, Rao, 2018. "Transparency in Long-Term Electric Demand Forecast: A Perspective on Regional Load Forecasting," 2018 Annual Meeting, August 5-7, Washington, D.C. 274396, Agricultural and Applied Economics Association.
  81. Juan A. Dominguez-Jimenez & Javier E. Campillo & Oscar Danilo Montoya & Enrique Delahoz & Jesus C. Hernández, 2020. "Seasonality Effect Analysis and Recognition of Charging Behaviors of Electric Vehicles: A Data Science Approach," Sustainability, MDPI, vol. 12(18), pages 1-18, September.
  82. Xiao, Tong & Xu, Peng & He, Ruikai & Sha, Huajing, 2022. "Status quo and opportunities for building energy prediction in limited data Context—Overview from a competition," Applied Energy, Elsevier, vol. 305(C).
  83. Sanstad, Alan H. & McMenamin, Stuart & Sukenik, Andrew & Barbose, Galen L. & Goldman, Charles A., 2014. "Modeling an aggressive energy-efficiency scenario in long-range load forecasting for electric power transmission planning," Applied Energy, Elsevier, vol. 128(C), pages 265-276.
  84. Cao, Qing & Ewing, Bradley T. & Thompson, Mark A., 2012. "Forecasting wind speed with recurrent neural networks," European Journal of Operational Research, Elsevier, vol. 221(1), pages 148-154.
  85. María Del Carmen Ruiz-Abellón & Antonio Gabaldón & Antonio Guillamón, 2018. "Load Forecasting for a Campus University Using Ensemble Methods Based on Regression Trees," Energies, MDPI, vol. 11(8), pages 1-22, August.
  86. 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.
  87. Yuanyuan Zhou & Min Zhou & Qing Xia & Wei-Chiang Hong, 2019. "Construction of EMD-SVR-QGA Model for Electricity Consumption: Case of University Dormitory," Mathematics, MDPI, vol. 7(12), pages 1-23, December.
  88. Chapaloglou, Spyridon & Nesiadis, Athanasios & Iliadis, Petros & Atsonios, Konstantinos & Nikolopoulos, Nikos & Grammelis, Panagiotis & Yiakopoulos, Christos & Antoniadis, Ioannis & Kakaras, Emmanuel, 2019. "Smart energy management algorithm for load smoothing and peak shaving based on load forecasting of an island’s power system," Applied Energy, Elsevier, vol. 238(C), pages 627-642.
  89. García-Ascanio, Carolina & Maté, Carlos, 2010. "Electric power demand forecasting using interval time series: A comparison between VAR and iMLP," Energy Policy, Elsevier, vol. 38(2), pages 715-725, February.
  90. Zhang, Yue & Farnoosh, Arash, 2019. "Analyzing the dynamic impact of electricity futures on revenue and risk of renewable energy in China," Energy Policy, Elsevier, vol. 132(C), pages 678-690.
  91. George P. Papaioannou & Christos Dikaiakos & Anargyros Dramountanis & Panagiotis G. Papaioannou, 2016. "Analysis and Modeling for Short- to Medium-Term Load Forecasting Using a Hybrid Manifold Learning Principal Component Model and Comparison with Classical Statistical Models (SARIMAX, Exponential Smoot," Energies, MDPI, vol. 9(8), pages 1, August.
  92. Moriarty, John & Palczewski, Jan, 2017. "Real option valuation for reserve capacity," European Journal of Operational Research, Elsevier, vol. 257(1), pages 251-260.
  93. Karamaziotis, Panagiotis I. & Raptis, Achilleas & Nikolopoulos, Konstantinos & Litsiou, Konstantia & Assimakopoulos, Vassilis, 2020. "An empirical investigation of water consumption forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(2), pages 588-606.
  94. Szabó, Dávid Zoltán & Duck, Peter & Johnson, Paul, 2020. "Optimal trading of imbalance options for power systems using an energy storage device," European Journal of Operational Research, Elsevier, vol. 285(1), pages 3-22.
  95. Brusaferri, Alessandro & Matteucci, Matteo & Spinelli, Stefano & Vitali, Andrea, 2022. "Probabilistic electric load forecasting through Bayesian Mixture Density Networks," Applied Energy, Elsevier, vol. 309(C).
  96. Chung, Won Hee & Gu, Yeong Hyeon & Yoo, Seong Joon, 2022. "District heater load forecasting based on machine learning and parallel CNN-LSTM attention," Energy, Elsevier, vol. 246(C).
  97. Ashfaq Ahmad & Nadeem Javaid & Abdul Mateen & Muhammad Awais & Zahoor Ali Khan, 2019. "Short-Term Load Forecasting in Smart Grids: An Intelligent Modular Approach," Energies, MDPI, vol. 12(1), pages 1-21, January.
  98. Ding, Jia & Wang, Maolin & Ping, Zuowei & Fu, Dongfei & Vassiliadis, Vassilios S., 2020. "An integrated method based on relevance vector machine for short-term load forecasting," European Journal of Operational Research, Elsevier, vol. 287(2), pages 497-510.
  99. Alireza Pourdaryaei & Mohammad Mohammadi & Mazaher Karimi & Hazlie Mokhlis & Hazlee A. Illias & Seyed Hamidreza Aghay Kaboli & Shameem Ahmad, 2021. "Recent Development in Electricity Price Forecasting Based on Computational Intelligence Techniques in Deregulated Power Market," Energies, MDPI, vol. 14(19), pages 1-28, September.
  100. 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.
  101. Ismail Shah & Hasnain Iftikhar & Sajid Ali, 2020. "Modeling and Forecasting Medium-Term Electricity Consumption Using Component Estimation Technique," Forecasting, MDPI, vol. 2(2), pages 1-17, May.
  102. 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.
  103. 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.
  104. 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.
  105. María Carmen Ruiz-Abellón & Luis Alfredo Fernández-Jiménez & Antonio Guillamón & Alberto Falces & Ana García-Garre & Antonio Gabaldón, 2019. "Integration of Demand Response and Short-Term Forecasting for the Management of Prosumers’ Demand and Generation," Energies, MDPI, vol. 13(1), pages 1-31, December.
  106. Graff, Mario & Peña, Rafael & Medina, Aurelio & Escalante, Hugo Jair, 2014. "Wind speed forecasting using a portfolio of forecasters," Renewable Energy, Elsevier, vol. 68(C), pages 550-559.
  107. Ping-Huan Kuo & Chiou-Jye Huang, 2018. "A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting," Energies, MDPI, vol. 11(1), pages 1-13, January.
  108. Sajawal ur Rehman Khan & Israa Adil Hayder & Muhammad Asif Habib & Mudassar Ahmad & Syed Muhammad Mohsin & Farrukh Aslam Khan & Kainat Mustafa, 2022. "Enhanced Machine-Learning Techniques for Medium-Term and Short-Term Electric-Load Forecasting in Smart Grids," Energies, MDPI, vol. 16(1), pages 1-16, December.
  109. Debbie Dupuis, Geneviève Gauthier, and Fréderic Godin, 2016. "Short-term Hedging for an Electricity Retailer," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
  110. Sigauke, C. & Chikobvu, D., 2011. "Prediction of daily peak electricity demand in South Africa using volatility forecasting models," Energy Economics, Elsevier, vol. 33(5), pages 882-888, September.
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