2015
DOI: 10.1080/15567249.2011.559520
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Electricity Consumption Forecasting in Thailand Using an Artificial Neural Network and Multiple Linear Regression

Abstract: In this article, an artificial neural network (ANN) and a regression model are applied to forecast long term electricity consumption in Thailand. The inputs of both nonlinear models are gross domestic product, number of population. Maximum ambient temperature and electricity power demand are used as inputs in a neural network to predict electricity consumption. The results show that the ANN model can give more accurate estimations than regression model as indicated by the performance measures, namely coefficie… Show more

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Cited by 54 publications
(26 citation statements)
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“…22 The testing performance of the network was measured by using the mean absolute percentage error and root mean square error. 23…”
Section: Load Forecasting For Day-ahead Operationmentioning
confidence: 99%
“…22 The testing performance of the network was measured by using the mean absolute percentage error and root mean square error. 23…”
Section: Load Forecasting For Day-ahead Operationmentioning
confidence: 99%
“…According to the structure of the model, it can be divided into the single prediction method and combined prediction method. Among them, single prediction methods include the time series method [4][5][6], linear regression method [7][8][9], grey forecasting method [10,11], support vector machine [12][13][14], and BP neural network [15][16][17]. However, any single prediction method in practical applications, owing to their own defects, causes insufficient prediction accuracy, and it is difficult to accurately predict the future power consumption level of the region.…”
Section: Introductionmentioning
confidence: 99%
“…Various techniques have been applied in electricity consumption forecasting including holt-winters and seasonal regression [2], time series models [3], first-order fuzzy time series [4], multiple linear regression [5][6], autoregressive integrated moving average (ARIMA) [7], seasonal ARIMA (SARIMA) [8], artificial neural network (ANN) [6,[8][9][10][11][12][13], Least-square SVM (LSSVM) [13], support vector regression [9], ANFIS [14][15] and ARIMA-ANFIS [16].…”
Section: Introductionmentioning
confidence: 99%