月電力需求預測所提供的資訊乃產電企業發展供電系統的重要基礎，因此本研究之目的是為發電企業建立電力預測系統，以預估未來之用電需求，方便企業作更有效的能源管理。然而，由於影響月電力需求的氣候因子，例如：溫度、濕度等會因為「月」的時間較長而稀釋其效果，因此本研究以過去用電歷史資料為一「整合因子」來做未來的電力預測。本研究應用趨勢萃取法將原來的電力需求序列分為趨勢序列與波動序列，其中趨勢序列可描述電力需求序列的趨勢；波動序列則描述電力需求序列的波動特性。有了此二序列後，本研究應用基因演算法與類神經網路來預測趨勢序列；然而由於波動序列有其週期特性，因此本研究運用傅立葉級數分析週期特性的優勢來預測未來之波動序列；因此，結合整合上述特性，本研究提出GANN-Fourier series預測模式。最後，本研究以美國之用電資料根據預測準確度與計算效率兩個指標，比較過去的類神經預測方法與本研究所提出的預測系統，以驗證所提方法之價值。

The information from electricity demand forecasting helps energy generation enterprises develop an electricity supply system. This study aims to develop a monthly electricity forecasting model to predict the electricity demand for energy management. Given that the influence of weather factors, such as temperature and humidity, is diluted in the overall monthly electricity demand, the forecasting model uses historical electricity consumption data as an integrated factor to obtain future prediction. The proposed approach is applied to a monthly electricity demand time series forecasting model that includes trend and fluctuation series, of which the former describes the trend of the electricity demand series and the latter describes the periodic fluctuation imbedded in the trend. An integrated genetic algorithm and neural network model (GANN) is then trained to forecast the trend series. Given that the fluctuation series demonstrates an oscillatory behavior, we apply Fourier series to fit the fluctuation series. The complete demand model is named GANN–Fourier series. U.S. electricity demand data are used to evaluate the proposed model and to compare the results of applying this model with those of using conventional neural networks.

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