Research & Studies

A Hybrid Machine Learning Approach for Daily Prediction of Solar Radiation

This conference paper was written by Amir Mosavi, Mehrnoosh Torabi, Pinar Ozturk, Annamaria Varkonyi-Koczy and Vajda Istvan  and is available in Laukaitis G. (eds) Recent Advances in Technology Research and Education. 


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.


Global solar radiation, Prediction, Support vector machine (SVM), Machine learning, Artificial neural networks (ANN) 

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