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

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...

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Main Authors:	Nantian Huang, Ruiqing Li, Lin Lin, Zhiyong Yu, Guowei Cai
Format:	Article
Language:	English
Published:	MDPI AG 2018-08-01
Series:	Sustainability
Subjects:	solar irradiation short-term irradiation prediction feature selection Gaussian Process Regression conditional mutual information
Online Access:	http://www.mdpi.com/2071-1050/10/8/2889

id	doaj-4c4e4d2dfbc2464fa1741ae45db42e64
record_format	Article
spelling	doaj-4c4e4d2dfbc2464fa1741ae45db42e642020-11-25T00:13:25ZengMDPI AGSustainability2071-10502018-08-01108288910.3390/su10082889su10082889Low Redundancy Feature Selection of Short Term Solar Irradiance Prediction Using Conditional Mutual Information and Gauss Process RegressionNantian Huang0Ruiqing Li1Lin Lin2Zhiyong Yu3Guowei Cai4School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, ChinaSchool of Electrical Engineering, Northeast Electric Power University, Jilin 132012, ChinaCollege of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, ChinaEconomic Research Institute, State Grid Xinjiang Electric Power limited company, Urumchi 830000, ChinaSchool of Electrical Engineering, Northeast Electric Power University, Jilin 132012, ChinaSolar 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-GPRAEK 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-GPRAEK 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.http://www.mdpi.com/2071-1050/10/8/2889solar irradiationshort-term irradiation predictionfeature selectionGaussian Process Regressionconditional mutual information
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Nantian Huang Ruiqing Li Lin Lin Zhiyong Yu Guowei Cai
spellingShingle	Nantian Huang Ruiqing Li Lin Lin Zhiyong Yu Guowei Cai Low Redundancy Feature Selection of Short Term Solar Irradiance Prediction Using Conditional Mutual Information and Gauss Process Regression Sustainability solar irradiation short-term irradiation prediction feature selection Gaussian Process Regression conditional mutual information
author_facet	Nantian Huang Ruiqing Li Lin Lin Zhiyong Yu Guowei Cai
author_sort	Nantian Huang
title	Low Redundancy Feature Selection of Short Term Solar Irradiance Prediction Using Conditional Mutual Information and Gauss Process Regression
title_short	Low Redundancy Feature Selection of Short Term Solar Irradiance Prediction Using Conditional Mutual Information and Gauss Process Regression
title_full	Low Redundancy Feature Selection of Short Term Solar Irradiance Prediction Using Conditional Mutual Information and Gauss Process Regression
title_fullStr	Low Redundancy Feature Selection of Short Term Solar Irradiance Prediction Using Conditional Mutual Information and Gauss Process Regression
title_full_unstemmed	Low Redundancy Feature Selection of Short Term Solar Irradiance Prediction Using Conditional Mutual Information and Gauss Process Regression
title_sort	low redundancy feature selection of short term solar irradiance prediction using conditional mutual information and gauss process regression
publisher	MDPI AG
series	Sustainability
issn	2071-1050
publishDate	2018-08-01
description	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-GPRAEK 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-GPRAEK 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.
topic	solar irradiation short-term irradiation prediction feature selection Gaussian Process Regression conditional mutual information
url	http://www.mdpi.com/2071-1050/10/8/2889
work_keys_str_mv	AT nantianhuang lowredundancyfeatureselectionofshorttermsolarirradiancepredictionusingconditionalmutualinformationandgaussprocessregression AT ruiqingli lowredundancyfeatureselectionofshorttermsolarirradiancepredictionusingconditionalmutualinformationandgaussprocessregression AT linlin lowredundancyfeatureselectionofshorttermsolarirradiancepredictionusingconditionalmutualinformationandgaussprocessregression AT zhiyongyu lowredundancyfeatureselectionofshorttermsolarirradiancepredictionusingconditionalmutualinformationandgaussprocessregression AT guoweicai lowredundancyfeatureselectionofshorttermsolarirradiancepredictionusingconditionalmutualinformationandgaussprocessregression
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Low Redundancy Feature Selection of Short Term Solar Irradiance Prediction Using Conditional Mutual Information and Gauss Process Regression

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