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Open AccessArticle

Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach

Rajabhat University Valaya-Alongkorn, Paholyothin Rd., Klong-Luang District, Prathumthani 13180, Thailand
Energies 2011, 4(8), 1246-1257; https://doi.org/10.3390/en4081246
Received: 3 May 2011 / Revised: 27 July 2011 / Accepted: 9 August 2011 / Published: 22 August 2011
(This article belongs to the Special Issue Intelligent Energy Demand Forecasting)
Demand planning for electricity consumption is a key success factor for the development of any countries. However, this can only be achieved if the demand is forecasted accurately. In this research, different forecasting methods—autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and multiple linear regression (MLR)—were utilized to formulate prediction models of the electricity demand in Thailand. The objective was to compare the performance of these three approaches and the empirical data used in this study was the historical data regarding the electricity demand (population, gross domestic product: GDP, stock index, revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010. The results showed that the ANN model reduced the mean absolute percentage error (MAPE) to 0.996%, while those of ARIMA and MLR were 2.80981 and 3.2604527%, respectively. Based on these error measures, the results indicated that the ANN approach outperformed the ARIMA and MLR methods in this scenario. However, the paired test indicated that there was no significant difference among these methods at α = 0.05. According to the principle of parsimony, the ARIMA and MLR models might be preferable to the ANN one because of their simple structure and competitive performance View Full-Text
Keywords: artificial neural network (ANN); autoregressive integrated moving average (ARIMA); electricity demand; multiple linear regression (MLR) artificial neural network (ANN); autoregressive integrated moving average (ARIMA); electricity demand; multiple linear regression (MLR)
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MDPI and ACS Style

Kandananond, K. Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach. Energies 2011, 4, 1246-1257. https://doi.org/10.3390/en4081246

AMA Style

Kandananond K. Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach. Energies. 2011; 4(8):1246-1257. https://doi.org/10.3390/en4081246

Chicago/Turabian Style

Kandananond, Karin. 2011. "Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach" Energies 4, no. 8: 1246-1257. https://doi.org/10.3390/en4081246

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