Short-Term Load Forecasting Based on Variable Selection and Gaussian Process Regression#br#

doi:10.3969/j.issn.1000－7229.2017.02.017

Abstract

Abstract: Improving the short-term load forecasting accuracy is one of the technical measures to ensure the safe and stable operation of the power grid. This paper establishes the short-term load forecasting model based on Gaussian process regression （GPR） by selecting the optimal set of input variables influencing the load consumption. The selection of input variables in load forecasting modeling has great influence on the forecasting accuracy. Firstly, we adopt random forest （RF） algorithm to obtain each input variable importance measure （VIM） and rank the input variables according to the influence degree. Based on the forward search strategy, the optimal input variable set can be determined, which can avoid the shortage of artificial experience. The conjugate gradient （CG） method is easy to fall into local optimal solution when solving the hyperparameters of Gaussian process regression （GPR） model, and the optimal performance depends on the selection of initial value, also the iteration number is difficult to be determined. In view of these problems, we use the improved particle swarm optimization （PSO） algorithm to search the model hyperparameters, and develop the load forecasting model based on optimal GPR. Finally, the effectiveness of proposed model is illustrated through testing simulation.

Key words: short-term load forecasting, input variables selection, random forest （RF） algorithm, Gaussian process regression （GPR）, improved particle swarm optimization （PSO） algorithm

CLC Number:

TM 715

LIANG Zhi，SUN Guoqiang，WEI Zhinong，ZANG Haixiang. Short-Term Load Forecasting Based on Variable Selection and Gaussian Process Regression#br#[J]. Electric Power Construction, 2017, 38(2): 122-.

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