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Modeling and Forecasting Hourly Electricity Demand by SARIMAX with Interactions

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  1. 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).
  2. Yixiang Ma & Lean Yu & Guoxing Zhang, 2022. "A Hybrid Short-Term Load Forecasting Model Based on a Multi-Trait-Driven Methodology and Secondary Decomposition," Energies, MDPI, vol. 15(16), pages 1-20, August.
  3. Roberto Baviera & Pietro Manzoni, 2022. "Tree-Based Learning in RNNs for Power Consumption Forecasting," Papers 2209.01378, arXiv.org.
  4. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
  5. Lemos-Vinasco, Julian & Bacher, Peder & Møller, Jan Kloppenborg, 2021. "Probabilistic load forecasting considering temporal correlation: Online models for the prediction of households’ electrical load," Applied Energy, Elsevier, vol. 303(C).
  6. Wu, Wen-Ze & Pang, Haodan & Zheng, Chengli & Xie, Wanli & Liu, Chong, 2021. "Predictive analysis of quarterly electricity consumption via a novel seasonal fractional nonhomogeneous discrete grey model: A case of Hubei in China," Energy, Elsevier, vol. 229(C).
  7. Zhang, Meng & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2022. "A novel flexible grey multivariable model and its application in forecasting energy consumption in China," Energy, Elsevier, vol. 239(PE).
  8. Zhang, Ning & Li, Zhiying & Zou, Xun & Quiring, Steven M., 2019. "Comparison of three short-term load forecast models in Southern California," Energy, Elsevier, vol. 189(C).
  9. Lee, Juyong & Cho, Youngsang, 2022. "National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?," Energy, Elsevier, vol. 239(PD).
  10. 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.
  11. Visser, L.R. & Kootte, M.E. & Ferreira, A.C. & Sicurani, O. & Pauwels, E.J. & Vuik, C. & Van Sark, W.G.J.H.M. & AlSkaif, T.A., 2022. "An operational bidding framework for aggregated electric vehicles on the electricity spot market," Applied Energy, Elsevier, vol. 308(C).
  12. Weijie Zhou & Huihui Tao & Jiaxin Chang & Huimin Jiang & Li Chen, 2023. "Forecasting Chinese Electricity Consumption Based on Grey Seasonal Model with New Information Priority," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
  13. Julio Barzola-Monteses & Mónica Mite-León & Mayken Espinoza-Andaluz & Juan Gómez-Romero & Waldo Fajardo, 2019. "Time Series Analysis for Predicting Hydroelectric Power Production: The Ecuador Case," Sustainability, MDPI, vol. 11(23), pages 1-19, November.
  14. Irene Nandutu & Marcellin Atemkeng & Nokubonga Mgqatsa & Sakayo Toadoum Sari & Patrice Okouma & Rockefeller Rockefeller & Theophilus Ansah-Narh & Jean Louis Ebongue Kedieng Fendji & Franklin Tchakount, 2022. "Error Correction Based Deep Neural Networks for Modeling and Predicting South African Wildlife–Vehicle Collision Data," Mathematics, MDPI, vol. 10(21), pages 1-31, October.
  15. Juyong Lee & Youngsang Cho, 2021. "National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?," Papers 2107.06174, arXiv.org.
  16. Zeng, Bo & He, Chengxiang & Mao, Cuiwei & Wu, You, 2023. "Forecasting China's hydropower generation capacity using a novel grey combination optimization model," Energy, Elsevier, vol. 262(PA).
  17. Yu, Binbin & Li, Jianjing & Liu, Che & Sun, Bo, 2022. "A novel short-term electrical load forecasting framework with intelligent feature engineering," Applied Energy, Elsevier, vol. 327(C).
  18. Chang, Zihan & Zhang, Yang & Chen, Wenbo, 2019. "Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform," Energy, Elsevier, vol. 187(C).
  19. Xiong, Xin & Hu, Xi & Guo, Huan, 2021. "A hybrid optimized grey seasonal variation index model improved by whale optimization algorithm for forecasting the residential electricity consumption," Energy, Elsevier, vol. 234(C).
  20. 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.
  21. Yunsun Kim & Sahm Kim, 2021. "Electricity Load and Internet Traffic Forecasting Using Vector Autoregressive Models," Mathematics, MDPI, vol. 9(18), pages 1-15, September.
  22. Xie, Wanli & Wu, Wen-Ze & Liu, Chong & Zhao, Jingjie, 2020. "Forecasting annual electricity consumption in China by employing a conformable fractional grey model in opposite direction," Energy, Elsevier, vol. 202(C).
  23. 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.
  24. Jasiński, Tomasz, 2020. "Use of new variables based on air temperature for forecasting day-ahead spot electricity prices using deep neural networks: A new approach," Energy, Elsevier, vol. 213(C).
  25. Zhu, Xiaoyue & Dang, Yaoguo & Ding, Song, 2020. "Using a self-adaptive grey fractional weighted model to forecast Jiangsu’s electricity consumption in China," Energy, Elsevier, vol. 190(C).
  26. 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.
  27. Mengkun Liang & Renjing Guo & Hongyu Li & Jiaqi Wu & Xiangdong Sun, 2023. "T-LGBKS: An Interpretable Machine Learning Framework for Electricity Consumption Forecasting," Energies, MDPI, vol. 16(11), pages 1-27, May.
  28. Li, Fengyun & Zheng, Haofeng & Li, Xingmei & Yang, Fei, 2021. "Day-ahead city natural gas load forecasting based on decomposition-fusion technique and diversified ensemble learning model," Applied Energy, Elsevier, vol. 303(C).
  29. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2021. "Point and interval forecasting of electricity supply via pruned ensembles," Energy, Elsevier, vol. 232(C).
  30. Rafati, Amir & Joorabian, Mahmood & Mashhour, Elaheh, 2020. "An efficient hour-ahead electrical load forecasting method based on innovative features," Energy, Elsevier, vol. 201(C).
  31. Haider, Syed Altan & Sajid, Muhammad & Sajid, Hassan & Uddin, Emad & Ayaz, Yasar, 2022. "Deep learning and statistical methods for short- and long-term solar irradiance forecasting for Islamabad," Renewable Energy, Elsevier, vol. 198(C), pages 51-60.
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