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題 名 | Time Series Analysis for Power System Short Term Load Forecasting=應用時間序列法於電力系統短期負載預測 |
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作 者 | 黃昭明; 楊宏澤; | 書刊名 | 高苑學報 |
卷 期 | 5:1 1996.02[民85.02] |
頁 次 | 頁37-48 |
分類號 | 448.115 |
關鍵詞 | ARMAX模式; 短期負載預測; 進化規劃法; 參數估計; ARMAX model; Short term load forecasting; Evolutionary programming; Parameter estimation; |
語 文 | 英文(English) |
中文摘要 | 當傳統時間序列法應用於鑑別模式階數及估計參數時,由於整體預測誤差空間存 在多重局部最佳解, 故所使用的梯度搜尋 (gradient search) 法容易使函數局使於局部最 佳解 (local optimal solution),因而影響其預測精確度。 透過模擬演化過程,本文應用 進化規劃法 (Evolutionary Programming, 簡稱 EP) 鑑別時間序列之自迴歸 -- 移動平均 -- 輸入變數 (Autoregression Moving average with exogenous variable, 簡稱 ARMAX) 模式,作電力系統未來一週每小時負載預測。本文採同時估計預測模式之階數與參數方式, 利用臺電系統不同形態負載作為測試,並與傳統時間序列方法作比較,以證實進化規劃方法 之優越性。 |
英文摘要 | Multiple local minimum points often exist on the surface of forecasting error function of the time series models. Solutions of the traditional gradient search based identification technique therefore may stall at the local optimal points which lead to an inadequate model. By simulating natural evolutionary process, the evolutionary programming (EP) algorithm offers the capability of converging towards the global extremum of a complex error surface. The EP based load forecasting algorithm is developed to identify the autoregression moving average with exogenous variable (ARMAX) model for one week ahead hourly load demand forecasts. Numerical tests indicate the proposed EP approach provides a method to simultaneously estimate the appropriate order and parameter values of the ARMAX model for diverse types of load data. Comparisons of forecasting errors are made to the traditional identification techniques used by SAS statistical commercial package. |
本系統中英文摘要資訊取自各篇刊載內容。