期刊名称：IAENG International Journal of Computer Science
印刷版ISSN：1819-656X
电子版ISSN：1819-9224
出版年度：2021
卷号：48
期号：4
语种：English
出版社：IAENG - International Association of Engineers
摘要：Short-term power load forecasting is an important factor affecting the security and economic operation of modern power systems. In order to avoid the limitation of a single model, this paper proposes a novel variable weight combined model that combines three single models of support vector regression (SVR), linear regression (LR) and random forest (RF). In the proposed model, a weight table is created based on time similarity, which contains weight information of 336 timestamps for determining the weight of each model. Then, particle swarm optimization (PSO) is employed to complete the modeling of the variable weight combined model. To evaluate the forecasting ability of the combined model, this paper takes the half-hourly power data of New South Wales as an example. Experimental analysis shows that all the evaluation metrics of the proposed model are better than the three single models and combined model based on average weight.
关键词：Short-term power load forecasting;combined model;variable weight;time similarity;particle swarm optimization
