Open AccessA Wavelet Based Solar Radiation Prediction in Nigeria Using Adaptive Neuro-Fuzzy Approach
Author(s) -
Sani Salisu,
Mohd Wazir Mustafa,
Mamunu Mustapha
Publication year - 2018
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v12.i3.pp907-915
Subject(s) - mean squared error , mean absolute percentage error , adaptive neuro fuzzy inference system , mathematics , span (engineering) , relative humidity , statistics , coefficient of determination , akaike information criterion , algorithm , meteorology , fuzzy logic , computer science , fuzzy control system , artificial intelligence , physics , engineering , structural engineering
In this study, a hybrid approach combining an Adaptive Neuro-Fuzzy Inference System (ANFIS) and Wavelet Transform (WT) is examined for solar radiation prediction in Nigeria. Meteorological data obtained from NIMET Nigeria comprising of monthly mean minimum temperature, maximum temperature, relative humidity and sunshine hours were used as inputs to the model and monthly mean solar radiation was used as the model output. The data used was divided into two for training and testing, with 70% used during the training phase and 30% during the testing phase. The hybrid model performance is assessed using three statistical evaluators, Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and Coefficient of determination (R 2 ). According to the results obtained, a very accurate prediction was achieved by the WT- ANFIS model by improving the value of (R 2 ) by at least 14% and RMSE by at least 78% when compared with other existing models. And a MAPE of 2% is recorded using the proposed approach. The obtained results prove the developed WT-ANFIS model as an efficient tool for solar radiation prediction.