In this paper, the accuracy of a hybrid machine learning technique for solar radiation prediction based on some meteorological data is examined. For this aim, a novel method named as SVM–FFA is developed by hybridizing the Support Vector Machines (SVMs) with Firefly Algorithm (FFA) to predict the monthly mean horizontal global solar radiation using three meteorological parameters of sunshine duration (n¯), maximum temperature (Tmax) and minimum temperature (Tmin) as inputs. The predictions accuracy of the proposed SVM–FFA model is validated compared to those of Artificial Neural Networks (ANN) and Genetic Programming (GP) models. The root mean square (RMSE), coefficient of determination (R2), correlation coefficient (r) and mean absolute percentage error (MAPE) are used as reliable indicators to assess the models’ performance. The attained results show that the developed SVM–FFA model provides more precise predictions compared to ANN and GP models, with RMSE of 0.6988, R2 of 0.8024, r of 0.8956 and MAPE of 6.1768 in training phase while, RMSE value of 1.8661, R2 value of 0.7280, r value of 0.8532 and MAPE value of 11.5192 are obtained in the testing phase. The results specify that the developed SVM–FFA model can be adjudged as an efficient machine learning technique for accurate prediction of horizontal global solar radiation.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A support vector machine–firefly algorithm-based model for global solar radiation prediction



    Published in:

    Solar Energy ; 115 ; 632-644


    Publication date :

    2015


    Size :

    13 Seiten, 62 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




    A genetic algorithm-based support vector machine for bus travel time prediction

    Moridpour, Sara / Anwar, Toni / Sadat, Mojtaba T. et al. | IEEE | 2015


    Traffic fatalities prediction based on support vector machine

    Ting Li / Yunong Yang / Yonghui Wang et al. | DOAJ | 2016

    Free access


    Robust pole placement using firefly algorithm

    Sababha, Moath / Zohdy, Mohamed / Kafafy, Maged | BASE | 2019

    Free access

    Limit Cycle Oscillation Prediction via Support Vector Machine Based Reduced Order Model

    Chen, Gang / Li, Yueming / Yingtao, Zuo et al. | AIAA | 2011