TY - JOUR
T1 - Comparing linear and nonlinear forecasts for Taiwan's electricity consumption
AU - Pao, Hsiao-Tien
PY - 2006/1/1
Y1 - 2006/1/1
N2 - This paper uses linear and nonlinear statistical models, including artificial neural network (ANN) methods, to investigate the influence of the four economic factors, which are the national income (NI), population (POP), gross of domestic production (GDP), and consumer price index (CPI) on the electricity consumption in Taiwan and then to develop an economic forecasting model. Both methods agree that POP and NI influence electricity consumption the most, whereas GDP the least. The results of comparing the out-of-sample forecasting capabilities of the two methods indicate the following. (1) If given a large amount of historical data, the forecasts of ARMAX are better than the other linear models. (2) The linear model is weaker on foretelling peaks and bottoms regardless the amount of historical data. (3) The forecasting performance of ANN is higher than the other linear models based on two sets of historical data considered in the paper. This is probably due to the fact that the ANN model is capable of catching sophisticated nonlinear integrating effects through a learning process. To sum up, the ANN method is more appropriate than the linear method for developing a forecasting model of electricity consumption. Moreover, researchers can employ either ANN or linear model to extract the important economic factors of the electricity consumption in Taiwan.
AB - This paper uses linear and nonlinear statistical models, including artificial neural network (ANN) methods, to investigate the influence of the four economic factors, which are the national income (NI), population (POP), gross of domestic production (GDP), and consumer price index (CPI) on the electricity consumption in Taiwan and then to develop an economic forecasting model. Both methods agree that POP and NI influence electricity consumption the most, whereas GDP the least. The results of comparing the out-of-sample forecasting capabilities of the two methods indicate the following. (1) If given a large amount of historical data, the forecasts of ARMAX are better than the other linear models. (2) The linear model is weaker on foretelling peaks and bottoms regardless the amount of historical data. (3) The forecasting performance of ANN is higher than the other linear models based on two sets of historical data considered in the paper. This is probably due to the fact that the ANN model is capable of catching sophisticated nonlinear integrating effects through a learning process. To sum up, the ANN method is more appropriate than the linear method for developing a forecasting model of electricity consumption. Moreover, researchers can employ either ANN or linear model to extract the important economic factors of the electricity consumption in Taiwan.
KW - ARMAX models
KW - Artificial neural networks
KW - Energy forecasting
UR - http://www.scopus.com/inward/record.url?scp=33745872542&partnerID=8YFLogxK
U2 - 10.1016/j.energy.2005.08.010
DO - 10.1016/j.energy.2005.08.010
M3 - Article
AN - SCOPUS:33745872542
SN - 0360-5442
VL - 31
SP - 2129
EP - 2141
JO - Energy
JF - Energy
IS - 12
ER -