电力需求侧管理2024，Vol.26Issue(4)：94-99,6.DOI:10.3969/j.issn.1009-1831.2024.04.015
基于融合技术的中长期电力负荷预测方法
Mid-long term electricity load forecasting method based on fusion techniques
摘要
Abstract
Current electrical load forecasting models are constrained by high data complexity,data scarcity,limited generalization,and in-sufficient adaptability to dynamic socio-economic factors,impeding their utility in sophisticated grid planning.To meet the planning and scheduling requirements of power grids or large-scale wind,solar,thermal,storage,grid,and load energy base projects,an integrated tech-nology has been proposed.Grey forecasting,spatial load density forecasting,variational autoencoders,and deep causal convolutional neu-ral networks are combined for medium to long-term load forecasting.The introduction of an ordered weighted averaging differential opera-tor amalgamates various predictive techniques,thereby refining accuracy.The experimental results demonstrate that the proposed method exhibits higher accuracy and robustness compared to traditional methods,particularly in the context of long-term electric load forecasting,effectively enhancing the reliability and applicability of the predictions.This technology effectively overcomes issues of data complexity,data scarcity and model generalization inherent in conventional methods,while adjusting to socio-economic dynamics.It provides substan-tial decision-making support for the planning and evolution of power networks and large-scale integrated energy projects.关键词
中长期电力负荷预测/深度因果卷积神经网络/变分自编码器/灰色预测/空间负荷密度预测/融合技术Key words
mid-long term power load forecasting/deep causal convolutional neural network/variational auto-encoder/grey forecasting/spatial load density prediction/fusion techniques分类
信息技术与安全科学引用本文复制引用
徐浩,刘青红,任正,张爽..基于融合技术的中长期电力负荷预测方法[J].电力需求侧管理,2024,26(4):94-99,6.基金项目
内蒙古自治区科技重大专项(2021ZD0039) （2021ZD0039）
