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

作　　者：徐浩[1] 刘青红 任正 张爽 XU Hao;LIU Qinghong;REN Zheng;ZHANG Shuang(Nanjing Nari-Relays Electric Co.,Ltd.,Nanjing 210000,China;Power Science Research Institute,State Grid Inner Mongolia Eastern Electric Power Co.,Ltd.,Hohhot 010000,China)

机构地区：[1]南京南瑞继保电气有限公司,南京210000 [2]国网内蒙古东部电力有限公司电力科学研究院,呼和浩特010000

出　　处：《电力需求侧管理》2024年第4期94-99,共6页Power Demand Side Management

基　　金：内蒙古自治区科技重大专项(2021ZD0039)。

摘　　要：当前电力负荷预测模型在数据复杂性高、数据稀缺、模型泛化和动态社会经济因素适应性方面存在局限,影响了其在复杂电网规划中的应用。为满足电网或者大型风、光、火、储、网、荷能源基地项目的规划调度需求,提出了一种融合技术,将灰色预测、空间负荷密度预测和变分自编码器与深度因果卷积神经网络相结合,以实现中长期负荷预测。通过引入有序加权平均微分算子,融合不同预测方法,提升结果的准确性。实验结果表明,本方法相较于传统方法展现更高的准确性和鲁棒性,特别是在进行电力负荷远景预测时,所提方法能够有效提升预测的可靠性和适用性。该技术有效克服传统方法固有的数据复杂性、数据稀缺性和模型泛化问题,同时适应社会经济条件的动态变化。该方法为电网、大型源网荷储多能互补类项目的规划和发展提供有力的决策支持。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 

分 类 号：TM715[电气工程—电力系统及自动化]