Forecasting Energy Consumption using Vector Autoregression and Genetic Planning

• Main Authors: Lin, Po-Heng, 林柏亨
• Other Authors: Tong, Lee-Ing
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/22425591254582313690

碩士 === 國立交通大學 === 工業工程與管理系所 === 101 === After three oil crises, the international energy situation is now characterized by volatility, rising energy prices, and heightened societal attention to issues related to energy. Accurate predict is a nation's annual energy consumption would enable help government to develop a policy of energy resources. Most studies constructed energy consumption prediction models for using univariate time series model. However, univariate time series model does not consider other variables such as some economic indice. When energy consumption are affected significently by other variables, the result of univariate time series model will be inaccurate. Because the energy consumption is tied up with the economic growth, this study will focus on predict energy consumption using the economic Indicators. This study utilizes economic indicators, such as population and total exportd and their lag effects as input data, and the energy consumption data as the output variable to build a Vector Autoregression model for predicting energy consumption. In addition, genetic Programming is employed to build a residual prediction model to enhance the forecasting accuracy. Finally, a hybrid energy consumption prediction model is developed by combining the Vetor Autoregression model and residual prediction model. The energy consumption data in Taiwan from 1979~2010 are utilized to demonstrate the effectiveness of the proposed method.