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Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach
AbstractDemand 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
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Kandananond, K. Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach. Energies 2011, 4, 1246-1257.View more citation formats
Kandananond K. Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach. Energies. 2011; 4(8):1246-1257.Chicago/Turabian Style
Kandananond, Karin. 2011. "Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach." Energies 4, no. 8: 1246-1257.
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