电力建设2024，Vol.45Issue(12)：149-161,13.DOI:10.12204/j.issn.1000-7229.2024.12.012
基于二次分解重构与多任务学习的综合能源系统多元负荷短期预测
Multi-Energy Load Forecasting of Integrated Energy System based on Secondary Decomposition-Reconstruction and Multi-Task Learning
摘要
Abstract
In the field of integrated energy systems,the inherent variability and interconnected nature of various energy loads substantially amplify their unpredictability,posing challenges to enhancing forecast precision.This study introduces a hybrid forecasting model that utilizes quadratic decomposition reconstruction coupled with multitask learning to tackle this.Addressing the prevalent noise in load fluctuations,the model employs a quadratic decomposition strategy.The model leverages variational mode decomposition alongside an improved adaptive noise complete ensemble empirical mode decomposition,segmenting load data into distinct high,medium,and low-frequency bands.Subsequently,permutation entropy is utilized to identify low-and medium-frequency sequences,which more accurately mirror the dynamics of load changes.The model incorporates a multi-output least squares support vector regression algorithm to manage the intricate interdependencies of multiple energy loads.This algorithm excels in assimilating multi-output related data and,in conjunction with a bidirectional long short-term memory network founded on multitask learning,it forecasts the low-and medium-frequency components.Empirical simulations validate that the quadratic decomposition reconstruction approach significantly elevates the predictive accuracy of the model.Additionally,these simulations showcase the prospective benefits of employing multi-output least squares support vector regression for complex multi-element load forecasting.关键词
综合能源系统/多元负荷预测/分解重构/多任务学习/多输出最小二乘支持向量回归Key words
integrated energy systems/multi-energy load forecasting/decomposition-reconstruction/multi-task learning/multi-output least squares support vector regression分类
信息技术与安全科学引用本文复制引用
于润泽,窦震海,张志一,胡亚春,陈佳佳,尹文良..基于二次分解重构与多任务学习的综合能源系统多元负荷短期预测[J].电力建设,2024,45(12):149-161,13.基金项目
This work is supported by the National Natural Science Foundation of China(No.52005306)and the Natural Science Foundation of Shandong Province,China(No.ZR2020QE220). 国家自然科学基金项目(52005306) （No.52005306）
山东省自然科学基金项目(ZR2020QE220). （ZR2020QE220）
