近年來，全球能源價格節節上升，需求量及消費量大增，由鑑於此，本文將針對台灣地區能源消費量進行預測，希望能掌握未來消費量趨勢，所以將採用四種預測模型，分別為季節性整合自我迴歸移動平均模型(SARIMA)、季節性時間數列迴歸模型(RMTSE)及倒傳遞類神經網路(BPN)，第四種模型將混和SARIMA與BPN(SARIMABP)，並探討此混和性模型是否能改善其預測結果。研究結果發現，當時間序列之資料圖形震盪較為明顯採用BPN能得到較好預測，反之，資料圖形震盪較為平穩，則採用SARIMA能得到較好預測，且採用混合性模型更能改善預測誤差。 Recently, the energy price keeps increasing.Both the demand and the consumption are on the rise.Due to these scenarios,this essay will try to predict the energy consumption in Taiwan,hoping to get a better grasp of the future trend.We will use the following four models for prediction,and they are Seasonal Autoregressive Integrated Moving Average Models(SARIMA),Regression Models with Time Series Errors (RMTSE),Back-propagation Network(BPN),and hybrid SARIMA and BPN(SARIMABP).The findings discovered that,at that time series of graph the sequence shook obviously uses BPN to be able to obtain a better forecast,otherwise,the graph shook steadily, used SARIMA to be able to obtain a better forecast,and adopt the mixing model to be able to improve the forecast error.
