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：2018
卷号：186
期号：5
页码：012042
DOI：10.1088/1755-1315/186/5/012042
语种：English
出版社：IOP Publishing
摘要：Given the significant fluctuation of errors for single forecasting model and limitation of linear combined forecasting models, A nonlinear multi-model ensemble method for short-term power load forecasting is proposed. Firstly, the power load big data is pre-processed, and multi-dimensional input feature variables are constructed and selected. On this basis, three kinds of single prediction models of random forest, support vector machine and Xgboost are modelled, and three different prediction results are obtained. Then, each individual prediction result and actual load are taken as a new training data set, and secondary learning is performed to obtain a final prediction result. The numerical experiments show that the proposed ensemble method combines the advantages of the single model, and has strong generalization ability and higher stability and accuracy, and has a high practical value.
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