An Ensemble Model Based on Machine Learning Methods for Short-term Power Load Forecasting

2018 International Conference of Green Buildings and Environmental Management

土木建筑工程;生态环境科学

Ren, Liqiang^1 ; Zhang, Limin^1 ; Wang, Haipeng^1 ; Qi, Lin^1

Institute of Information Fusion, Naval Aviation University, SHANDONG

264001, China^1

关键词: Combined forecasting;    Forecasting modeling;    Generalization ability;    Individual prediction;    Multi-model ensemble;    Numerical experiments;    Power load forecasting;    Training data sets;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/186/5/012042/pdf
DOI  :  10.1088/1755-1315/186/5/012042

来源: IOP

【 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.