預測國內能源電力消費

Abstract

近年全球皆面臨了能源的危機。不僅如此，能源的使用過度還使氣候有了異常的變遷，讓各國不得不開始重視節約能源與適當使用能源的重要性。其中電力也是各項能源中使用十分頻繁的，為防止全球暖化可從節能減碳，節約能源的方式立即落實。台灣是否也在討論這個議題及推廣許多活動之後能由國內每年每月消費電力來看出顯著成效？為了瞭解國內的電力消費，是否隨著政府的政策推廣與宣導而有效的減少用電量，我們使用AREMOS經濟統計資料庫中的「國內能源消費－電力」，時間為西元1990年1月至2010年12月的252筆月資料，並且保留最後12筆來做樣本外預測，以分析歷年來國內電力使用量的狀況。我們應用四種統計預測模型或方法，依序是時間序列迴歸法、指數平滑法、分解法、ARIMA模型配適資料。最後利用MSE、MAD、MPE、MAPE四個準則評估樣本外預測，來評估這四種方法何者最佳，並選出最適模型。從此次分析結果能看出，分解法的配適模型最能有效的預測出國內電力消費的真實值。

In order to better understand electric power expenses in Taiwan, we study “the domestic energy use - electric power” which was obtained from the AREMOS economic statistics information database. The time period is from January 1990 to December 2010, total 252 observations. We save the last 12 observations for out-of-sample forecasts. We apply four kinds of statistical methods/models for forecasting, which included the time series regression, exponential smoothing, decomposition method, and the ARIMA model to model this time series. In order to evaluate the forecast performance, we employ MSE, MAD, MPE, and MAPE four criteria to evaluate the performance of the four methods. Based on MAPE criterion, we found that the decomposition method with X11 and regression technique has the best performance (about 2.3%) among the four methods which is able to effectively forecast domestic electric power to expend in Taiwan for the future .