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首页> 外文会议>International Conference on Electronic Business（ICEB 2004) vol.2; 20041205-09; Beijing(CN) >Forecasting the Consumption for Electricity in Taiwan
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Forecasting the Consumption for Electricity in Taiwan

机译：预测台湾的用电量

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摘要

This paper use linear regression and non-linear artificial neural network (ANN) model to analyze how the four economic factors: national income (NI), population (POP), gross of domestic production (GDP), and consumer price index (CPI), affect Taiwan's electricity consumption, furthermore, develop an economic forecasting model. Both models agree with that POP and NI are of the most influence on electricity consumption, whereas GDP of the least. Then, we compare the out-of-sample forecasting capabilities of the two models. The comparing result indicates that the linear model is obviously of higher bias value than that of ANN model, and of weaker ability of forecasting capability on peaks or bottoms. This probably results from: 1) linear regression model is built on the logarithm function of electricity consumption, and ANN is built oh the original data; 2) ANN model is capable of catching sophisticated non-linear integrating effects. Consequently, ANN model is the more appropriate between the two to be applied to building an economic forecasting model of Taiwan's electricity consumption.
机译：本文使用线性回归和非线性人工神经网络（ANN）模型来分析四个经济因素：国民收入（NI），人口（POP），国内生产总值（GDP）和消费者价格指数（CPI） ，影响台湾的用电量，进而建立经济预测模型。两种模型都同意，POP和NI对用电量的影响最大，而GDP的影响最小。然后，我们比较两个模型的样本外预测能力。比较结果表明，线性模型的偏差值明显高于ANN模型，并且对峰或谷的预测能力较弱。这可能是由于：1）线性回归模型建立在用电量的对数函数上，而ANN建立在原始数据上； 2）ANN模型能够捕获复杂的非线性积分效应。因此，ANN模型在两者之间更适合用于构建台湾电力消耗的经济预测模型。

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