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An adaptive hybrid model for short term electricity price forecasting

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  1. 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).
  2. He, Xinbo & Wang, Yong & Zhang, Yuyang & Ma, Xin & Wu, Wenqing & Zhang, Lei, 2022. "A novel structure adaptive new information priority discrete grey prediction model and its application in renewable energy generation forecasting," Applied Energy, Elsevier, vol. 325(C).
  3. Meng, Anbo & Wang, Peng & Zhai, Guangsong & Zeng, Cong & Chen, Shun & Yang, Xiaoyi & Yin, Hao, 2022. "Electricity price forecasting with high penetration of renewable energy using attention-based LSTM network trained by crisscross optimization," Energy, Elsevier, vol. 254(PA).
  4. Chengqing, Yu & Guangxi, Yan & Chengming, Yu & Yu, Zhang & Xiwei, Mi, 2023. "A multi-factor driven spatiotemporal wind power prediction model based on ensemble deep graph attention reinforcement learning networks," Energy, Elsevier, vol. 263(PE).
  5. Xiong, Xiaoping & Qing, Guohua, 2023. "A hybrid day-ahead electricity price forecasting framework based on time series," Energy, Elsevier, vol. 264(C).
  6. Yin, Linfei & Qiu, Yao, 2022. "Neural network dynamic differential control for long-term price guidance mechanism of flexible energy service providers," Energy, Elsevier, vol. 255(C).
  7. 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.
  8. He Jiang & Yao Dong & Jianzhou Wang, 2024. "Electricity price forecasting using quantile regression averaging with nonconvex regularization," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1859-1879, September.
  9. Laiqing Yan & Zutai Yan & Zhenwen Li & Ning Ma & Ran Li & Jian Qin, 2023. "Electricity Market Price Prediction Based on Quadratic Hybrid Decomposition and THPO Algorithm," Energies, MDPI, vol. 16(13), pages 1-18, July.
  10. Heydari, Azim & Majidi Nezhad, Meysam & Pirshayan, Elmira & Astiaso Garcia, Davide & Keynia, Farshid & De Santoli, Livio, 2020. "Short-term electricity price and load forecasting in isolated power grids based on composite neural network and gravitational search optimization algorithm," Applied Energy, Elsevier, vol. 277(C).
  11. Daniel Manfre Jaimes & Manuel Zamudio López & Hamidreza Zareipour & Mike Quashie, 2023. "A Hybrid Model for Multi-Day-Ahead Electricity Price Forecasting considering Price Spikes," Forecasting, MDPI, vol. 5(3), pages 1-23, July.
  12. Sergio Cantillo-Luna & Ricardo Moreno-Chuquen & Jesus Lopez-Sotelo & David Celeita, 2023. "An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture," Energies, MDPI, vol. 16(19), pages 1-24, September.
  13. Ghimire, Sujan & Nguyen-Huy, Thong & Deo, Ravinesh C. & Casillas-Pérez, David & Masrur Ahmed, A.A. & Salcedo-Sanz, Sancho, 2025. "Novel deep hybrid model for electricity price prediction based on dual decomposition," Applied Energy, Elsevier, vol. 395(C).
  14. Wu, Binrong & Wang, Lin & Zeng, Yu-Rong, 2022. "Interpretable wind speed prediction with multivariate time series and temporal fusion transformers," Energy, Elsevier, vol. 252(C).
  15. Lu Peng & Sheng‐Xiang Lv & Lin Wang, 2024. "Explainable machine learning techniques based on attention gate recurrent unit and local interpretable model‐agnostic explanations for multivariate wind speed forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2064-2087, September.
  16. 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.
  17. Li, Jianyu & Xu, Xinyin & Karimi, Hamid Reza & Wu, Zhengtian, 2025. "Optimization of electric vehicle charging strategies in residential integrated energy systems: A SARIMA model approach for dynamic electricity prices," Energy, Elsevier, vol. 330(C).
  18. Chai, Shanglei & Li, Qiang & Abedin, Mohammad Zoynul & Lucey, Brian M., 2024. "Forecasting electricity prices from the state-of-the-art modeling technology and the price determinant perspectives," Research in International Business and Finance, Elsevier, vol. 67(PA).
  19. Acikgoz, Hakan & Budak, Umit & Korkmaz, Deniz & Yildiz, Ceyhun, 2021. "WSFNet: An efficient wind speed forecasting model using channel attention-based densely connected convolutional neural network," Energy, Elsevier, vol. 233(C).
  20. Jialin Liu & Chen Gong & Suhua Chen & Nanrun Zhou, 2023. "Multi-Step-Ahead Wind Speed Forecast Method Based on Outlier Correction, Optimized Decomposition, and DLinear Model," Mathematics, MDPI, vol. 11(12), pages 1-26, June.
  21. Li, Dan & Jiang, Fuxin & Chen, Min & Qian, Tao, 2022. "Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks," Energy, Elsevier, vol. 238(PC).
  22. Jiang, Ping & Liu, Zhenkun & Niu, Xinsong & Zhang, Lifang, 2021. "A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting," Energy, Elsevier, vol. 217(C).
  23. Wen, Shizhao & Wang, Hongzeng & Qian, Jinhua & Men, Xuanyu, 2023. "A novel combined model based on echo state network optimized by whale optimization algorithm for blast furnace gas prediction," Energy, Elsevier, vol. 279(C).
  24. Liu, Luyao & Bai, Feifei & Su, Chenyu & Ma, Cuiping & Yan, Ruifeng & Li, Hailong & Sun, Qie & Wennersten, Ronald, 2022. "Forecasting the occurrence of extreme electricity prices using a multivariate logistic regression model," Energy, Elsevier, vol. 247(C).
  25. Krishna Prakash N. & Jai Govind Singh, 2023. "Electricity price forecasting using hybrid deep learned networks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1750-1771, November.
  26. Lu, Renzhi & Bai, Ruichang & Ding, Yuemin & Wei, Min & Jiang, Junhui & Sun, Mingyang & Xiao, Feng & Zhang, Hai-Tao, 2021. "A hybrid deep learning-based online energy management scheme for industrial microgrid," Applied Energy, Elsevier, vol. 304(C).
  27. Wang, Xuguang & Ren, Huan & Zhai, Junhai & Xing, Hongjie & Su, Jie, 2022. "Adaptive support segment based short-term wind speed forecasting," Energy, Elsevier, vol. 249(C).
  28. Askr, Heba & Basha, Sameh H. & Abdelnapi, Noha MM. & Elgeldawi, Enas & Darwish, Ashraf & Hassanien, Aboul Ella, 2025. "Artificial intelligence for sustainable green hydrogen production: A systematic literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 224(C).
  29. Garcia, Alexandre Schwinden & Alves, Frederick Fagundes & Pimentel Filgueiras, João Marcello, 2024. "Tariff flags and electricity consumption response in Brazil," Utilities Policy, Elsevier, vol. 88(C).
  30. Hong, Ying-Yi & Satriani, Thursy Rienda Aulia, 2020. "Day-ahead spatiotemporal wind speed forecasting using robust design-based deep learning neural network," Energy, Elsevier, vol. 209(C).
  31. Stefano Frizzo Stefenon & Laio Oriel Seman & Viviana Cocco Mariani & Leandro dos Santos Coelho, 2023. "Aggregating Prophet and Seasonal Trend Decomposition for Time Series Forecasting of Italian Electricity Spot Prices," Energies, MDPI, vol. 16(3), pages 1-18, January.
  32. Zheng, Jingwei & Wang, Jianzhou, 2024. "Short-term wind speed forecasting based on recurrent neural networks and Levy crystal structure algorithm," Energy, Elsevier, vol. 293(C).
  33. Zhou, Yilin & Wang, Jianzhou & Lu, Haiyan & Zhao, Weigang, 2022. "Short-term wind power prediction optimized by multi-objective dragonfly algorithm based on variational mode decomposition," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
  34. Zhichao Zhang & Xue Li & Yanling Zhao & Zhaogong Zhang & Bin Li, 2025. "Prediction Accuracy of Stackelberg Game Model of Electricity Price in Smart Grid Power Market Environment," Energies, MDPI, vol. 18(3), pages 1-16, January.
  35. Nie, Ying & Li, Ping & Wang, Jianzhou & Zhang, Lifang, 2024. "A novel multivariate electrical price bi-forecasting system based on deep learning, a multi-input multi-output structure and an operator combination mechanism," Applied Energy, Elsevier, vol. 366(C).
  36. Naval, Natalia & Yusta, Jose M., 2025. "Assessment of the impact of electricity market prices on pumped hydro storage operation with renewable generation," Energy, Elsevier, vol. 336(C).
  37. Heidarpanah, Mohammadreza & Hooshyaripor, Farhad & Fazeli, Meysam, 2023. "Daily electricity price forecasting using artificial intelligence models in the Iranian electricity market," Energy, Elsevier, vol. 263(PE).
  38. Wang, Xuguang & Li, Xiao & Su, Jie, 2023. "Distribution drift-adaptive short-term wind speed forecasting," Energy, Elsevier, vol. 273(C).
  39. Yang, Rui & Liu, Hui & Nikitas, Nikolaos & Duan, Zhu & Li, Yanfei & Li, Ye, 2022. "Short-term wind speed forecasting using deep reinforcement learning with improved multiple error correction approach," Energy, Elsevier, vol. 239(PB).
  40. Li, Yanting & Wu, Zhenyu & Su, Yan, 2023. "Adaptive short-term wind power forecasting with concept drifts," Renewable Energy, Elsevier, vol. 217(C).
  41. Li, Yanhui & Sun, Kaixuan & Yao, Qi & Wang, Lin, 2024. "A dual-optimization wind speed forecasting model based on deep learning and improved dung beetle optimization algorithm," Energy, Elsevier, vol. 286(C).
  42. AL-Alimi, Dalal & AlRassas, Ayman Mutahar & Al-qaness, Mohammed A.A. & Cai, Zhihua & Aseeri, Ahmad O. & Abd Elaziz, Mohamed & Ewees, Ahmed A., 2023. "TLIA: Time-series forecasting model using long short-term memory integrated with artificial neural networks for volatile energy markets," Applied Energy, Elsevier, vol. 343(C).
  43. Shao, Zhen & Yang, Yudie & Zheng, Qingru & Zhou, Kaile & Liu, Chen & Yang, Shanlin, 2022. "A pattern classification methodology for interval forecasts of short-term electricity prices based on hybrid deep neural networks: A comparative analysis," Applied Energy, Elsevier, vol. 327(C).
  44. Yuanke Cu & Kaishu Wang & Lechen Zhang & Zixuan Liu & Yixuan Liu & Li Mo, 2025. "A Time Series Decomposition-Based Interpretable Electricity Price Forecasting Method," Energies, MDPI, vol. 18(3), pages 1-19, January.
  45. Hasnain Iftikhar & Josue E. Turpo-Chaparro & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2023. "Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition—Combination Technique," Energies, MDPI, vol. 16(18), pages 1-23, September.
  46. Zhao, Xinyu & Bai, Mingliang & Yang, Xusheng & Liu, Jinfu & Yu, Daren & Chang, Juntao, 2021. "Short-term probabilistic predictions of wind multi-parameter based on one-dimensional convolutional neural network with attention mechanism and multivariate copula distribution estimation," Energy, Elsevier, vol. 234(C).
  47. Tahmasebifar, Reza & Moghaddam, Mohsen Parsa & Sheikh-El-Eslami, Mohammad Kazem & Kheirollahi, Reza, 2020. "A new hybrid model for point and probabilistic forecasting of wind power," Energy, Elsevier, vol. 211(C).
  48. Wu, Zheng & Zhang, Yue & Dong, Ze, 2023. "Prediction of NOx emission concentration from coal-fired power plant based on joint knowledge and data driven," Energy, Elsevier, vol. 271(C).
  49. Li, Jinchao & Wu, Qianqian & Tian, Yu & Fan, Liguo, 2021. "Monthly Henry Hub natural gas spot prices forecasting using variational mode decomposition and deep belief network," Energy, Elsevier, vol. 227(C).
  50. Dorel Mihai Paraschiv & Narciz Balasoiu & Souhir Ben-Amor & Raul Cristian Bag, 2023. "Hybridising Neurofuzzy Model the Seasonal Autoregressive Models for Electricity Price Forecasting on Germany’s Spot Market," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 25(63), pages 463-463, April.
  51. 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).
  52. Feng, Lingbing & Qi, Jiajun & Lucey, Brian, 2024. "Enhancing cryptocurrency market volatility forecasting with daily dynamic tuning strategy," International Review of Financial Analysis, Elsevier, vol. 94(C).
  53. Karol Pilot & Alicja Ganczarek-Gamrot & Krzysztof Kania, 2024. "Dealing with Anomalies in Day-Ahead Market Prediction Using Machine Learning Hybrid Model," Energies, MDPI, vol. 17(17), pages 1-20, September.
  54. Demir, Sumeyra & Mincev, Krystof & Kok, Koen & Paterakis, Nikolaos G., 2021. "Data augmentation for time series regression: Applying transformations, autoencoders and adversarial networks to electricity price forecasting," Applied Energy, Elsevier, vol. 304(C).
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  58. Yang, Wendong & Sun, Shaolong & Hao, Yan & Wang, Shouyang, 2022. "A novel machine learning-based electricity price forecasting model based on optimal model selection strategy," Energy, Elsevier, vol. 238(PC).
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