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Short term electricity price forecast based on environmentally adapted generalized neuron

Citations

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Cited by:

  1. Wang, Fei & Ge, Xinxin & Yang, Peng & Li, Kangping & Mi, Zengqiang & Siano, Pierluigi & Duić, Neven, 2020. "Day-ahead optimal bidding and scheduling strategies for DER aggregator considering responsive uncertainty under real-time pricing," Energy, Elsevier, vol. 213(C).
  2. Liyang Tang, 2020. "Application of Nonlinear Autoregressive with Exogenous Input (NARX) neural network in macroeconomic forecasting, national goal setting and global competitiveness assessment," Papers 2005.08735, arXiv.org.
  3. Lago, Jesus & Marcjasz, Grzegorz & De Schutter, Bart & Weron, Rafał, 2021. "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," Applied Energy, Elsevier, vol. 293(C).
  4. Saez, Yago & Mochon, Asuncion & Corona, Luis & Isasi, Pedro, 2019. "Integration in the European electricity market: A machine learning-based convergence analysis for the Central Western Europe region," Energy Policy, Elsevier, vol. 132(C), pages 549-566.
  5. 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.
  6. Otashu, Joannah I. & Baldea, Michael, 2018. "Grid-level “battery” operation of chemical processes and demand-side participation in short-term electricity markets," Applied Energy, Elsevier, vol. 220(C), pages 562-575.
  7. Xu, Mengjie & Li, Xiang & Li, Qianwen & Sun, Chuanwang, 2024. "LNBi-GRU model for coal price prediction and pattern recognition analysis," Applied Energy, Elsevier, vol. 365(C).
  8. Zhang, Jinliang & Tan, Zhongfu & Wei, Yiming, 2020. "An adaptive hybrid model for short term electricity price forecasting," Applied Energy, Elsevier, vol. 258(C).
  9. 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).
  10. Tian, Zhirui & Liu, Weican & Sun, Wenpu & Wu, Chenye, 2025. "From LMP to eLMP: An accurate transfer strategy for electricity price prediction based on learning ensemble," Energy, Elsevier, vol. 325(C).
  11. 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.
  12. Kamyar Sabri-Laghaie & Milad Eshkevari & Mahdi Fathi & Enrico Zio, 2019. "Redundancy allocation problem in a bridge system with dependent subsystems," Journal of Risk and Reliability, , vol. 233(4), pages 658-669, August.
  13. Xie, Li & Kong, Chun, 2024. "A fair grid connection cost-sharing model for electricity based on the random forest machine learning method," Utilities Policy, Elsevier, vol. 90(C).
  14. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Prediction and explanation of the formation of the Spanish day-ahead electricity price through machine learning regression," Applied Energy, Elsevier, vol. 239(C), pages 610-625.
  15. Qiao, Weibiao & Yang, Zhe, 2020. "Forecast the electricity price of U.S. using a wavelet transform-based hybrid model," Energy, Elsevier, vol. 193(C).
  16. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
  17. 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).
  18. Hakan Acaroğlu & Fausto Pedro García Márquez, 2021. "Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy," Energies, MDPI, vol. 14(22), pages 1-23, November.
  19. 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).
  20. Gabrielli, Paolo & Wüthrich, Moritz & Blume, Steffen & Sansavini, Giovanni, 2022. "Data-driven modeling for long-term electricity price forecasting," Energy, Elsevier, vol. 244(PB).
  21. Yin, Linfei & Qiu, Yao, 2022. "Long-term price guidance mechanism of flexible energy service providers based on stochastic differential methods," Energy, Elsevier, vol. 238(PB).
  22. Marcjasz, Grzegorz & Narajewski, Michał & Weron, Rafał & Ziel, Florian, 2023. "Distributional neural networks for electricity price forecasting," Energy Economics, Elsevier, vol. 125(C).
  23. Claudio Monteiro & Ignacio J. Ramirez-Rosado & L. Alfredo Fernandez-Jimenez, 2018. "Probabilistic Electricity Price Forecasting Models by Aggregation of Competitive Predictors," Energies, MDPI, vol. 11(5), pages 1-25, April.
  24. Fang Guo & Shangyun Deng & Weijia Zheng & An Wen & Jinfeng Du & Guangshan Huang & Ruiyang Wang, 2022. "Short-Term Electricity Price Forecasting Based on the Two-Layer VMD Decomposition Technique and SSA-LSTM," Energies, MDPI, vol. 15(22), pages 1-20, November.
  25. Peng, Lu & Liu, Shan & Liu, Rui & Wang, Lin, 2018. "Effective long short-term memory with differential evolution algorithm for electricity price prediction," Energy, Elsevier, vol. 162(C), pages 1301-1314.
  26. Li, Jin & Wen, Xin & Jia, Li & Cao, Ruochen & Zhang, Xiao & Hao, Yanrong & Gao, Chengxin & Cao, Rui, 2025. "A Transformer-based model fusing temporal dependence and variable correlation for short and medium-term electricity price forecasting," Energy, Elsevier, vol. 338(C).
  27. Wang, Jianzhou & Yang, Wendong & Du, Pei & Li, Yifan, 2018. "Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system," Energy, Elsevier, vol. 148(C), pages 59-78.
  28. Wang, Guotao & Lin, Zhenjia & Chen, Yuntian & Qiu, Rui & Zhang, Haoran & Yan, Jinyue, 2025. "Carbon-billed future for virtual power plants: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 217(C).
  29. Monjazeb, Mohammad Reza & Amiri, Hossein & Movahedi, Akram, 2024. "Wholesale electricity price forecasting by Quantile Regression and Kalman Filter method," Energy, Elsevier, vol. 290(C).
  30. Fraunholz, Christoph & Kraft, Emil & Keles, Dogan & Fichtner, Wolf, 2021. "Advanced price forecasting in agent-based electricity market simulation," Applied Energy, Elsevier, vol. 290(C).
  31. Muneer M. Alshater & Ilias Kampouris & Hazem Marashdeh & Osama F. Atayah & Hasanul Banna, 2025. "Early warning system to predict energy prices: the role of artificial intelligence and machine learning," Annals of Operations Research, Springer, vol. 345(2), pages 1297-1333, February.
  32. 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).
  33. 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.
  34. Kwak, Sanghyeok & Choi, Jaehong & Lee, Min Chul & Yoon, Youngbin, 2021. "Predicting instability frequency and amplitude using artificial neural network in a partially premixed combustor," Energy, Elsevier, vol. 230(C).
  35. Nahid Sultana & S. M. Zakir Hossain & Salma Hamad Almuhaini & Dilek Düştegör, 2022. "Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand," Energies, MDPI, vol. 15(9), pages 1-26, May.
  36. Singh, Priyanka & Dwivedi, Pragya & Kant, Vibhor, 2019. "A hybrid method based on neural network and improved environmental adaptation method using Controlled Gaussian Mutation with real parameter for short-term load forecasting," Energy, Elsevier, vol. 174(C), pages 460-477.
  37. Zhang, Guoqiang & Guo, Jifeng, 2020. "A novel ensemble method for residential electricity demand forecasting based on a novel sample simulation strategy," Energy, Elsevier, vol. 207(C).
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