期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2019
卷号：252
期号：3
页码：1-10
DOI：10.1088/1755-1315/252/3/032072
出版社：IOP Publishing
摘要：Electric energy is closely related to people's life, in recent years, the construction of smart grid has already been proposed. Short-term load forecasting is a research hotspot in the process of smart grid. In this paper, we proposed a combined forecasting method based on random forest and artificial neural network, the final result is the weighted sum of the two single models, and the weight of each single model is obtained by the least square method. The data of experiment is the load data of a power plant in Hunan province from 2012 to 2017, and the corresponding weather information, the sampling granularity of the data is 15 minutes. The combined model we proposed can combine the advantages of random forest and artificial neural network, and the result of experiment shows that the combined model improves the accuracy of short term load forecasting.
