中国电力2025，Vol.58Issue(8)：31-40,10.DOI:10.11930/j.issn.1004-9649.202411079
高斯分布引导下负荷8760曲线全景最优化预测
Panoramic Optimal Prediction of Load 8760 Curve Guided by Gaussian Distribution
摘要
Abstract
Accurate long-term load forecasting provides base data for power system planning.However,most of the existing long-term load forecasting adopt the form of long time scale such as year and month,without considering the randomness and volatility of load.Taking the power load as variables,a panoramic optimization prediction method of 8760 load curve guided by Gaussian distribution is proposed.Firstly,a long-term panoramic load forecasting framework of load 8760 curve is proposed,which describes the long-term load changes in a panoramic and refined structure for a year which consist of 8760 h.The multi-time scale load indicators contained in the curve were analyzed and decomposed into long-term trend,medium-term fluctuation and short-term change according to statistical characteristics.The probability distribution of the maximum daily load with high fluctuation is determined by Gaussian hypothesis test.For other indicators with obvious statistical characteristics,the change rule is found on its time scale.Then,the load probability optimization model is established with the maximum load probability as the objective function,and the panoramic load prediction is transformed into an optimization problem.By predicting the load characteristics of different time scales,the constraints of the model are formed,and the optimization model is solved and the target annual load 8760 curve is restored.The results of the actual load data arithmetic examples in the central and southern provinces of China show that the proposed method accurately describes the panoramic loads in the form of load 8760 curves by determining the load probability distributions,which can effectively improve the accuracy of long-term forecasting and has great generalizability and interpretability.关键词
负荷8760曲线/全景负荷预测/概率优化Key words
8760 load curve/panoramic load forecasting/probabilistic optimization引用本文复制引用
罗超,倪恬,陈凌云,康义,侯慧,吴细秀..高斯分布引导下负荷8760曲线全景最优化预测[J].中国电力,2025,58(8):31-40,10.基金项目
国家自然科学基金资助项目(52177110) （52177110）
中电工程项目资助(DG2-X02-2022). This work is supported by National Natural Science Foundation of China(No.52177110),China Power Engineering Consulting Group Co.,Ltd.Project Funding(No.DG2-X02-2022). （DG2-X02-2022）
