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A New Approach for Prediction of Solar Radiation with Using Ensemble Learning Algorithm

  • Kivanc BasaranEmail author
  • Akın Özçift
  • Deniz Kılınç
Research Article - Electrical Engineering

Abstract

This article investigates the competence of ensemble learning techniques in solar irradiance prediction. It was seen from the literature survey, an ensemble tree model, random forests is studied more frequently as ensemble models. However, ensemble of support vector regression (SVR) and artificial neural networks (ANN) is also possible. So, this study is the first detailed evaluation of ensemble models in solar irradiance estimation domain. Boosting and bagging ensembles of SVR, ANN and decision tree (DT), are developed to estimate solar irradiance in hourly basis in five cities in Turkey. First frequently used base models (SVR, ANN, and DT) are created and tested with the use of 5 years meteorological data. Then boosting and bagging ensembles of the base models are developed and tested with the same data. The base models are compared with their ensemble counterparts in terms of average coefficient of determination (R2) and root mean squared error (RMSE). The comparative results show that boosting and bagging ensemble models improve SVR, ANN, and DT in terms of RMSE between 4.6 and 14.6% in average. The results show empirically that ensemble models improve prediction accuracies of various base regression models and it can be applied to other machine learning models used in solar irradiance prediction.

Keywords

Solar irradiance Prediction Machine learning Ensemble methods 

Abbreviations

DMI

Turkish state meteorological service

SDF

Sunshine duration fraction

MSDF

Modified sunshine duration fraction

ANFIS

Adaptive neuro-fuzzy inference system

ARMA

Autoregressive moving average

ANN

Artificial neural network

MLP

Multi-layer perceptron

SVM

Support vector machine

SVR

Support vector regression

DT

Decision-tree

ID3

Iternative Dichotomizer

KNN

K-nearest neighbors

GBT

Gradient boosting tree

RF

Random forests

RMSE

Root mean square error

R2

Coefficient of determination

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Copyright information

© King Fahd University of Petroleum & Minerals 2019

Authors and Affiliations

  1. 1.Department of Energy Systems EngineeringManisa Celal Bayar UniversityManisaTurkey
  2. 2.Department of Software EngineeringManisa Celal Bayar UniversityManisaTurkey

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