A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: Case studies of the USA and Turkey regions. (15th April 2020)

• Record Type: Journal Article
• Authors: Alizamir, Meysam; Kim, Sungwon; Kisi, Ozgur; Zounemat-Kermani, Mohammad
• Is Part Of: Energy. Volume 197(2020)
• Publication Date: 2020-04-15
• Subjects: Solar radiation -- Gradient boosting tree -- Artificial neural network -- Adaptive neuro fuzzy inference system -- Classification and regression tree; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2020.117239
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: In this study, the potential of six different machine learning models, gradient boosting tree (GBT), multilayer perceptron neural network (MLPNN), two types of adaptive neuro-fuzzy inference systems (ANFIS) based on fuzzy c-means clustering (ANFIS-FCM) and subtractive clustering (ANFIS-SC), multivariate adaptive regression spline (MARS), and classification and regression tree (CART) were used for forecasting solar radiation from two stations of two different locations, Turkey and USA. Wind speed, maximum air temperature, minimum air temperature and relative humidity were used as inputs to the developed models. For accurate evaluation of performance of models, four statistical indicators, root mean squared error (RMSE), coefficient of correlation (R), mean absolute error (MAE) and Nash–Sutcliffe efficiency coefficient (NS) were employed to evaluate accuracy of the developed models. Comparison of results showed that the GBT model performed better than the MLPNN, ANFIS, MARS, and CART in modeling solar radiation. The average RMSE of MLPNN, ANFIS-FCM, ANFIS-SC, MARS and CART models was decreased by 0.26%, 1.5%, 0.51%, 2.5%, and 19.34% using GBT model at Fairfield Station, 4%, 1.37%, 0.24%, 4.12%, and 24.4% at Monmouth Station, 11.99%, 48.7%, 41.6%, 8.23%, and 33.41% at Antalya Station, 11%, 54.8%, 51.9%, 19.65%, and 37.1% at Mersin Station, respectively. The overall results indicated that the GBT model could be successfully applied in forecasting solar radiation by using climatic parameters as inputs. Highlights: Different categories of machine learning models were investigated for solar radiation estimation. Models include tree-based (GBT & CART), regression-based (MARS), and network-based (ANN and ANFISs). Climatic data were used as inputs to the models. Performance of the models were evaluated based on statistical measures and diagnostic analyses. The gradient boosting tree (GBT) model was superior to the other proposed models.