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Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm—A case study of papermaking process

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  1. Yang, Dongchuan & Guo, Ju-e & Li, Yanzhao & Sun, Shaolong & Wang, Shouyang, 2023. "Short-term load forecasting with an improved dynamic decomposition-reconstruction-ensemble approach," Energy, Elsevier, vol. 263(PA).
  2. Guo‐Feng Fan & Yan‐Hui Guo & Jia‐Mei Zheng & Wei‐Chiang Hong, 2020. "A generalized regression model based on hybrid empirical mode decomposition and support vector regression with back‐propagation neural network for mid‐short‐term load forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 737-756, August.
  3. Hossein Moayedi & Amir Mosavi, 2021. "Electrical Power Prediction through a Combination of Multilayer Perceptron with Water Cycle Ant Lion and Satin Bowerbird Searching Optimizers," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
  4. Tianlang Xiong & Zhishuo Ma & Zhuangzhuang Li & Jiangqianyi Dai, 2022. "The analysis of influence mechanism for internet financial fraud identification and user behavior based on machine learning approaches," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 996-1007, December.
  5. Hu, Yusha & Li, Jigeng & Hong, Mengna & Ren, Jingzheng & Man, Yi, 2022. "Industrial artificial intelligence based energy management system: Integrated framework for electricity load forecasting and fault prediction," Energy, Elsevier, vol. 244(PB).
  6. Talaat, M. & Farahat, M.A. & Mansour, Noura & Hatata, A.Y., 2020. "Load forecasting based on grasshopper optimization and a multilayer feed-forward neural network using regressive approach," Energy, Elsevier, vol. 196(C).
  7. Shichao Huang & Jing Zhang & Yu He & Xiaofan Fu & Luqin Fan & Gang Yao & Yongjun Wen, 2022. "Short-Term Load Forecasting Based on the CEEMDAN-Sample Entropy-BPNN-Transformer," Energies, MDPI, vol. 15(10), pages 1-14, May.
  8. Mustafa Saglam & Xiaojing Lv & Catalina Spataru & Omer Ali Karaman, 2024. "Instantaneous Electricity Peak Load Forecasting Using Optimization and Machine Learning," Energies, MDPI, vol. 17(4), pages 1-22, February.
  9. Malekizadeh, M. & Karami, H. & Karimi, M. & Moshari, A. & Sanjari, M.J., 2020. "Short-term load forecast using ensemble neuro-fuzzy model," Energy, Elsevier, vol. 196(C).
  10. Zhen, Hao & Niu, Dongxiao & Wang, Keke & Shi, Yucheng & Ji, Zhengsen & Xu, Xiaomin, 2021. "Photovoltaic power forecasting based on GA improved Bi-LSTM in microgrid without meteorological information," Energy, Elsevier, vol. 231(C).
  11. Yizhong Chen & Guiwen Liu & Taozhi Zhuang, 2022. "Evaluating the Comprehensive Benefit of Urban Renewal Projects on the Area Scale: An Integrated Method," IJERPH, MDPI, vol. 20(1), pages 1-20, December.
  12. Zhang, Dongxue & Wang, Shuai & Liang, Yuqiu & Du, Zhiyuan, 2023. "A novel combined model for probabilistic load forecasting based on deep learning and improved optimizer," Energy, Elsevier, vol. 264(C).
  13. Giuntini, Lorenzo & Lamioni, Rachele & Linari, Luca & Saccomano, Pietro & Mainardi, Davide & Tognotti, Leonardo & Galletti, Chiara, 2022. "Decarbonization of a tissue paper plant: Advanced numerical simulations to assess the replacement of fossil fuels with a biomass-derived syngas," Renewable Energy, Elsevier, vol. 198(C), pages 884-893.
  14. Hu, Yusha & Man, Yi, 2022. "Two-stage energy scheduling optimization model for complex industrial process and its industrial verification," Renewable Energy, Elsevier, vol. 193(C), pages 879-894.
  15. Hu, Yusha & Man, Yi, 2023. "Energy consumption and carbon emissions forecasting for industrial processes: Status, challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
  16. Kaiyan Wang & Haodong Du & Jiao Wang & Rong Jia & Zhenyu Zong, 2023. "An Ensemble Deep Learning Model for Provincial Load Forecasting Based on Reduced Dimensional Clustering and Decomposition Strategies," Mathematics, MDPI, vol. 11(12), pages 1-20, June.
  17. Wang, Kang & Wang, Jianzhou & Zeng, Bo & Lu, Haiyan, 2022. "An integrated power load point-interval forecasting system based on information entropy and multi-objective optimization," Applied Energy, Elsevier, vol. 314(C).
  18. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
  19. Lai, Changzhi & Wang, Yu & Fan, Kai & Cai, Qilin & Ye, Qing & Pang, Haoqiang & Wu, Xi, 2022. "An improved forecasting model of short-term electric load of papermaking enterprises for production line optimization," Energy, Elsevier, vol. 245(C).
  20. Wang, Jianzhou & Gao, Jialu & Wei, Danxiang, 2022. "Electric load prediction based on a novel combined interval forecasting system," Applied Energy, Elsevier, vol. 322(C).
  21. Su, Qingyu & Chen, Cong & Huang, Xin & Li, Jian, 2022. "Interval TrendRank method for grid node importance assessment considering new energy," Applied Energy, Elsevier, vol. 324(C).
  22. Wang, Jianzhou & Xing, Qianyi & Zeng, Bo & Zhao, Weigang, 2022. "An ensemble forecasting system for short-term power load based on multi-objective optimizer and fuzzy granulation," Applied Energy, Elsevier, vol. 327(C).
  23. Li, Chen, 2020. "Designing a short-term load forecasting model in the urban smart grid system," Applied Energy, Elsevier, vol. 266(C).
  24. Wang, Guoyang & Awad, Omar I. & Liu, Shiyu & Shuai, Shijin & Wang, Zhiming, 2020. "NOx emissions prediction based on mutual information and back propagation neural network using correlation quantitative analysis," Energy, Elsevier, vol. 198(C).
  25. Zhang, Lihong & Wang, Jun & Wang, Bin, 2020. "Energy market prediction with novel long short-term memory network: Case study of energy futures index volatility," Energy, Elsevier, vol. 211(C).
  26. Yang, Dongchuan & Guo, Ju-e & Sun, Shaolong & Han, Jing & Wang, Shouyang, 2022. "An interval decomposition-ensemble approach with data-characteristic-driven reconstruction for short-term load forecasting," Applied Energy, Elsevier, vol. 306(PA).
  27. Kazemzadeh, Mohammad-Rasool & Amjadian, Ali & Amraee, Turaj, 2020. "A hybrid data mining driven algorithm for long term electric peak load and energy demand forecasting," Energy, Elsevier, vol. 204(C).
  28. Rafati, Amir & Joorabian, Mahmood & Mashhour, Elaheh, 2020. "An efficient hour-ahead electrical load forecasting method based on innovative features," Energy, Elsevier, vol. 201(C).
  29. Tavassoli-Hojati, Z. & Ghaderi, S.F. & Iranmanesh, H. & Hilber, P. & Shayesteh, E., 2020. "A self-partitioning local neuro fuzzy model for short-term load forecasting in smart grids," Energy, Elsevier, vol. 199(C).
  30. Akhter, Muhammad Naveed & Mekhilef, Saad & Mokhlis, Hazlie & Ali, Raza & Usama, Muhammad & Muhammad, Munir Azam & Khairuddin, Anis Salwa Mohd, 2022. "A hybrid deep learning method for an hour ahead power output forecasting of three different photovoltaic systems," Applied Energy, Elsevier, vol. 307(C).
  31. Qi Jiang & Yuxin Cheng & Haozhe Le & Chunquan Li & Peter X. Liu, 2022. "A Stacking Learning Model Based on Multiple Similar Days for Short-Term Load Forecasting," Mathematics, MDPI, vol. 10(14), pages 1-20, July.
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