Solar Energy Prediction for Malaysia Using Artificial Neural Networks

Tamer Khatib,Azah Mohamed,K Sopian,M Mahmoud

doi:10.1155/2012/419504

Abstract

This paper presents a solar energy prediction method using artificial neural networks (ANNs). An ANN predicts a clearness index that is used to calculate global and diffuse solar irradiations. The ANN model is based on the feed forward multilayer perception model with four inputs and one output. The inputs are latitude, longitude, day number, and sunshine ratio; the output is the clearness index. Data from 28 weather stations were used in this research, and 23 stations were used to train the network, while 5 stations were used to test the network. In addition, the measured solar irradiations from the sites were used to derive an equation to calculate the diffused solar irradiation, a function of the global solar irradiation and the clearness index. The proposed equation has reduced the mean absolute percentage error (MAPE) in estimating the diffused solar irradiation compared with the conventional equation. Based on the results, the average MAPE, mean bias error and root mean square error for the predicted global solar irradiation are 5.92%, 1.46%, and 7.96%. The MAPE in estimating the diffused solar irradiation is 9.8%. A comparison with previous work was done, and the proposed approach was found to be more efficient and accurate than previous methods.

Highlights

Solar energy is the portion of the sun’s energy available at the earth’s surface for useful applications, such as raising the temperature of water or exciting electrons in a photovoltaic cell, in addition to supplying energy to natural processes like photosynthesis
Humid tropical climate with two monsoon seasons: one between October and February and the other from April to October; the latter is characterized by thunderstorms
Long-term (1984–2004) metrological data containing the global irradiation, diffused irradiation, clearness index, sunshine hours, humidity, ambient temperature, rainfall, and air pressure have been taken from these sites to develop and test the proposed artificial neural networks (ANNs) model

Summary

Introduction

Solar energy is the portion of the sun’s energy available at the earth’s surface for useful applications, such as raising the temperature of water or exciting electrons in a photovoltaic cell, in addition to supplying energy to natural processes like photosynthesis This energy is free, clean, and abundant in most places throughout the year. Solar radiation data provide information on how much of the sun’s energy strikes a surface at a location on the earth during a particular time period. These data are needed for effective research into solar energy utilization. Accurate than the proposed methods in [35, 36], and it will provide hourly, daily, and monthly solar radiation predictions for many different locations in Malaysia because the location coordinates are provided.

Malaysia’s Climate Profile

Solar Energy Prediction Model

Artificial Neural Network for Clearness Index Prediction

Results and Discussion

Conclusion