中国电机工程学报2024，Vol.44Issue(5)：1777-1788,中插9,13.DOI:10.13334/j.0258-8013.pcsee.223470
考虑最小平均包络熵负荷分解的最优Bagging集成超短期多元负荷预测
Optimal Bagging Ensemble Ultra Short Term Multi-energy Load Forecasting Considering Least Average Envelope Entropy Load Decomposition
摘要
Abstract
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.关键词
多元负荷预测/综合能源系统/集成学习/海洋捕食者算法/包络熵Key words
multi-energy load forecasting/integrated energy system/ensemble learning/marine predators algorithm/envelope entropy分类
信息技术与安全科学引用本文复制引用
姜飞,林政阳,王文烨,王小明,奚振乾,郭祺..考虑最小平均包络熵负荷分解的最优Bagging集成超短期多元负荷预测[J].中国电机工程学报,2024,44(5):1777-1788,中插9,13.基金项目
湖南省自然科学基金项目(2021JJ30715) （2021JJ30715）
湖南省教育厅资助科研项目(20B029). Project Supported by Natural Science Foundation of Hunan Province(2021JJ30715) （20B029）
Project Supported by Research Project Funded by Hunan Provincial Department of Education(20B029). （20B029）
