A combined forecasting method for short term load forecasting based on random forest and artificial neural network

2018 4th International Conference on Environmental Science and Material Application

生态环境科学;材料科学

Yuan, Chunming^1 ; Chi, Yuanying^1 ; Li, Xiaojing^2

Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China^1

State Grid Control Center of Gansu Electric Power Company, Lanzhou, China^2

关键词: Combined forecasting;    Combined model;    Electric energies;    Hunan province;    Least square methods;    Random forests;    Short term load forecasting;    Weather information;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/252/3/032072/pdf
DOI  :  10.1088/1755-1315/252/3/032072

来源: IOP

【 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.