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Received:December 28, 2021 Revised:January 28, 2022
Received:December 28, 2021 Revised:January 28, 2022
中文摘要: 为维持电网稳定, 各种负荷预测方法层出不穷, 但由于算法泛化能力、模型复杂度等自身特点不同, 使其对于负荷预测的适用性存在差异. 本文讨论了近5年短期电力负荷预测的国内外研究现状, 从实验数据集、数据预处理、预测算法、优化模型以及评估方法等多个维度对当前电力负荷预测研究现状进行整体概述, 同时总结各种预测算法的优缺点与适用性, 对短期电力负荷预测系统的发展趋势进行总结与展望, 以期为未来电力系统负荷预测模型选择提供参考.
Abstract:Load forecasting methods emerge one after another to maintain the stability of power grids. However, due to the characteristic difference in the generalization ability of algorithms and model complexity, the applicability of these methods to load forecasting differs. This study discusses and summarizes the research status of short-term power load forecasting both at home and abroad in the past five years from multiple dimensions, such as experimental data sets, data preprocessing, forecasting algorithms, optimization models, and evaluation methods. Meanwhile, we also present a summary of the advantages, disadvantages, and applicability of various forecasting algorithms, and the development trend of the short-term power load forecasting system is expounded and predicted. This study is expected to provide a reference for the forecasting model selection of power system loads in the future.
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基金项目:国家自然科学基金(61973180); 山东省产教融合研究生联合培养示范基地项目(2020-19)
引用文本:
梁宏涛,刘红菊,李静,王莹,郭超男.基于机器学习的短期负荷预测算法综述.计算机系统应用,2022,31(10):25-35
LIANG Hong-Tao,LIU Hong-Ju,LI Jing,WANG Ying,GUO Chao-Nan.Survey on Short-term Load Forecasting Algorithm Based on Machine Learning.COMPUTER SYSTEMS APPLICATIONS,2022,31(10):25-35
梁宏涛,刘红菊,李静,王莹,郭超男.基于机器学习的短期负荷预测算法综述.计算机系统应用,2022,31(10):25-35
LIANG Hong-Tao,LIU Hong-Ju,LI Jing,WANG Ying,GUO Chao-Nan.Survey on Short-term Load Forecasting Algorithm Based on Machine Learning.COMPUTER SYSTEMS APPLICATIONS,2022,31(10):25-35