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A Hybrid Machine Learning Approach for Daily Prediction of Solar Radiation

Authors :
Annamária R. Várkonyi-Kóczy
Amir Mosavi
Vajda Istvan
Pinar Öztürk
Mehrnoosh Torabi
Source :
Recent Advances in Technology Research and Education ISBN: 9783319998336
Publication Year :
2018
Publisher :
Springer International Publishing, 2018.

Abstract

In this paper, we present a Cluster-Based Approach (CBA) that utilizes the support vector machine (SVM) and an artificial neural network (ANN) to estimate and predict the daily horizontal global solar radiation. In the proposed CBA-ANN-SVM approach, we first conduct clustering analysis and divided the global solar radiation data into clusters, according to the calendar months. Our approach aims at maximizing the homogeneity of data within the clusters, and the heterogeneity between the clusters. The proposed CBA-ANN-SVM approach is validated and the precision is compared with ANN and SVM techniques. The mean absolute percentage error (MAPE) for the proposed approach was reported lower than those of ANN and SVM.

Details

ISBN :
978-3-319-99833-6
ISBNs :
9783319998336
Database :
OpenAIRE
Journal :
Recent Advances in Technology Research and Education ISBN: 9783319998336
Accession number :
edsair.doi...........d7d86c695657a09b7dce0030241619f8
Full Text :
https://doi.org/10.1007/978-3-319-99834-3_35