Low Redundancy Feature Selection of Short Term Solar Irradiance Prediction Using Conditional Mutual Information and Gauss Process Regression

Nantian Huang, Ruiqing Li, Lin Lin, Zhiyong Yu and Guowei Cai
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Nantian Huang: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Ruiqing Li: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Lin Lin: College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, China
Zhiyong Yu: Economic Research Institute, State Grid Xinjiang Electric Power limited company, Urumchi 830000, China
Guowei Cai: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China

Sustainability, 2018, vol. 10, issue 8, 1-20

Abstract: Solar irradiation is influenced by many meteorological features, which results in a complex structure meaning its prediction has low efficiency and accuracy. The existing prediction methods are focused on analyzing the correlation between features and irradiation to reduce model complexity but they do not account for redundant analysis in feature subset. In order to reduce the information redundancy in the feature set and improve prediction accuracy, a novel feature selection method for short-term irradiation prediction based on Conditional Mutual Information (CMI) and Gaussian Process Regression (GPR) is proposed. Firstly, the CMI values of different features are calculated to evaluate correlation and redundant information between features in the feature subsets. Secondly, GPR with a stable prediction performance and adaptively determined hyper parameters is used as the predictor. The optimal feature subset and the GPR covariance function can be selected using Sequential Forward Selection (SFS). Finally, an optimal predictor is determined by the minimum prediction error and the prediction of solar irradiation is carried out by the determined predictor. The experimental results show that CMI-GPR AEK has the highest prediction accuracy with the optimal feature set has low dimension, which is 4.33% lower in MAPE than the predictor without feature selection, although both of them have an optimal kernel function. The CMI-GPR AEK is less complicated for the predictor and there is less redundancy between features in the model with the dimension of the optimal feature set is only 14.

Keywords: solar irradiation; short-term irradiation prediction; feature selection; Gaussian Process Regression; conditional mutual information (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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