Comparing linear and nonlinear forecasts for Taiwan's electricity consumption
Comparing linear and nonlinear forecasts for Taiwan's electricity consumption
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DOI：
10.1016/j.energy.2005.08.010
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发表时间：
2006-09
期刊：
影响因子：
9
通讯作者：
H. Pao
中科院分区：
文献类型：
--
作者：
H. Pao
本文利用线性和非线性统计模型，包括人工神经网络（ANN）方法，研究国民收入（NI）、人口（POP）、国内生产总值（GDP）和消费者价格指数（CPI）四个经济因素对台湾电力消耗的影响，并建立经济预测模型。两种方法都一致认为，POP 和 NI 对电力消耗的影响最大，而 GDP 的影响最小。两种方法的样本外预测能力比较结果表明： (1)如果给定大量历史数据，ARMAX的预测效果优于其他线性模型。 (2)无论历史数据有多少，线性模型对预测波峰和波谷的能力较弱。 (3)基于本文考虑的两组历史数据，ANN的预测性能高于其他线性模型。这可能是因为 ANN 模型能够通过学习过程捕捉复杂的非线性积分效应。综上所述，ANN 方法比线性方法更适合建立用电量预测模型。此外，研究人员可以采用人工神经网络或线性模型来提取台湾电力消耗的重要经济因素。
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.