Optimal Bagging Ensemble Ultra Short Term Multi-energy Load Forecasting Considering Least Average Envelope Entropy Load Decomposition

被引：0
|
作者：
Jiang F. [1 ]
Lin Z. [1 ]
Wang W. [1 ]
Wang X. [2 ]
Xi Z. [2 ]
Guo Q. [3 ]
机构：
[1] School of Electrical and Information Engineering, Changsha University of Science & Technology, Hunan Province, Changsha
[2] State Grid Anhui Electric Power Co., Ltd., Anhui Province, Hefei
[3] National Engineering Research Center of Energy Conversion and Control, Hunan University, Hunan Province, Changsha
来源：
Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2024年 / 44卷 / 05期
关键词：
ensemble learning; envelope entropy; integrated energy system; marine predators algorithm; multi-energy load forecasting;
D O I：
10.13334/j.0258-8013.pcsee.223470
中图分类号：
学科分类号：
摘要：
Multi-energy load forecasting technology is the key cornerstone to ensure the supply and demand balance and stable operation of integrated energy system (IES). However, IES load with strong randomness and volatility aggravates the difficulty of accurate ultra short term multi-energy load forecast. Therefore, the optimal Bagging ensemble ultra short term multi-energy load forecasting method considering least average envelope entropy load decomposition is proposed. The parameters optimization model of variational mode decomposition based on least average envelope entropy is constructed, and the multi-energy load of IES is decomposed into the set of intrinsic mode functions; the strong correlation characteristic of calendar, weather and load of multi-energy load forecasting are filtered based on the uniform information coefficient method. Combined with the IMFs set of load, calendar rules, meteorological environment and load data, the Bagging ensemble ultra short term multi-energy load forecasting model is constructed, the ensemble strategy optimization model is constructed based on the mean absolute percentage error and R-square, and then the optimal ensemble strategy and final forecast results are also obtained. Simulation verification is carried out with IES of Arizona State University Tempe Campus as the object. The results show that the mean absolute percentage error of the proposed method in electric, heat and cooling load forecasting is 1.948 6%, 2.058 5% and 2.5331%, respectively, which has higher accuracy than other forecast methods. ©2024 Chin.Soc.for Elec.Eng.
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页码：1777 / 1788
页数：11
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共 26 条
  • [1] ZHANG Zhigang, KANG Chongqing, Challenges and prospects for constructing the new-type power system towards a carbon neutrality future[J], Proceedings of the CSEE, 42, 8, pp. 2806-2818, (2022)
  • [2] DENG Jie, JIANG Fei, WANG Wenye, Low-carbon optimized operation of integrated energy system considering electric-heat flexible load and hydrogen energy refined modeling[J], Power System Technology, 46, 5, pp. 1692-1702, (2022)
  • [3] Ting YANG, ZHAO Liyuan, WANG Chengshan, Review on application of artificial intelligence in power system and integrated energy system[J], Automation of Electric Power Systems, 43, 1, pp. 2-14, (2019)
  • [4] ZHU Jizhong, DONG Hanjiang, LI Shenglin, Review of data-driven load forecasting for integrated energy system[J], Proceedings of the CSEE, 41, 23, pp. 7905-7923, (2021)
  • [5] ESEYE A T，, LEHTONEN M．, Short-term forecasting of heat demand of buildings for efficient and optimal energy management based on integrated machine learning models[J], IEEE Transactions on industrial informatics, 16, 12, pp. 7743-7755, (2020)
  • [6] Shi QIAO, WANG Lei, ZHANG Pengchao, Shortterm load forecasting by long-and short-term temporal networks with attention based on modal decomposition [J], Power System Technology, 46, 10, pp. 3940-3951, (2022)
  • [7] CHEN Jinpeng, HU Zhijian, CHEN Weinan, Load prediction of integrated energy system based on combination of quadratic modal decomposition and deep bidirectional long short-term memory and multiple linear regression[J], Automation of Electric Power Systems, 45, 13, pp. 85-94, (2021)
  • [8] LI Wenwu, ZHANG Pengyu, SHI Qiang, Correction prediction of integrated energy system load based on aggregated mixed mode decomposition and TCN [J], Power System Technology, 46, 9, pp. 3345-3353, (2022)
  • [9] YE Jianhua, CAO Jing, YANG Li, Ultra short-term load forecasting of user level integrated energy system based on variational mode decomposition and multi-model fusion[J], Power System Technology, 46, 7, pp. 2610-2618, (2022)
  • [10] TANG Xianlun, CHEN Hongxu, XIONG Deyi, Short-term power load forecasting based on extreme gradient boosting and temporal convolutional network [J], High Voltage Engineering, 48, 8, pp. 3059-3067, (2022)
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