Solar irradiance forecasting for Malaysia using multiple regression and artificial neural network

Ho, Yih Hwa and Yew, Poh Leng (2022) Solar irradiance forecasting for Malaysia using multiple regression and artificial neural network. Defence S &T Technical Bulletin, 15 (1). pp. 83-90. ISSN 1985-6571

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Abstract

The installed capacity of solar photovoltaic (PV) globally continues to rise. In Malaysia, the monthly average daily solar radiation is 4,000-5,000 Wh/m², with the average daily sunshine duration ranging from 4 to 8 h. However, the output of solar energy is related to solar irradiance, which lacks stability due to weather variation. Therefore, solar irradiance forecasting has become an important resource for network grid operators to control the output of solar PV energy. Weather forecasting data, such as temperature, dew point, humidity, pressure and wind speed, are widely available from local meteorological organisations. However, solar irradiance forecasting data is often unavailable. In this paper, multiple regression (MR) and artificial neural network (ANN) models are used to forecast solar irradiance using weather forecasting data. The correlation of each weather parameter with solar irradiance is investigated. It is evident that the ANN model is able to improve the accuracy in terms of root mean square error (RMSE) by 18.42% of its as compared to the MR model

Item Type:	Article
Uncontrolled Keywords:	Solar energy, solar irradiance, forecasting, multiple regression (MR), artificial neural network (ANN).
Divisions:	Faculty of Electronics and Computer Engineering > Department of Industrial Electronics
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	13 Apr 2023 15:56
Last Modified:	13 Apr 2023 15:56
URI:	http://eprints.utem.edu.my/id/eprint/26679
Statistic Details:	View Download Statistic

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