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題 名 | A Time Series Approach to Short Term Load Forecasting Through Evolutionary Programming Structures=時間序列演化模式作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 | 5:2 1996.08[民85.08] |
頁 次 | 頁71-77 |
分類號 | 448.115 |
關鍵詞 | 進化規劃法; 自迴歸-移動平均; 隨機時間序列; 梯度搜尋; ARMA model; Short term load forecasting; Evolutionary programming; Parameter estimation; |
語 文 | 英文(English) |
中文摘要 | 傳統隨機時間序列(stochastic time series)方法在鑑別預測模式,大多利用梯皮搜尋(gradient search)方式,再以最小平方法作為擇取標準,但由於整體預測誤差空間存在多重局部最佳解,使用梯度搜尋法容易局限於兩數局部最佳解(local optimal solution),故在預測精確度上仍受限制。經由模擬進化過程,求取預測模式階數及估計參數之最佳解,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(Autoregression Moving avera民簡稱ARMA)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,利用台電系統不同型態負載作測試,並與傳統隨機時間序列方法作比較,以證實EP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 con-verging towards the global extremum of a complex error surface. The EP based load forecasting algorithm is devel-oped to identify the autoregression moving average (ARMA) model for one week ahead hourly load demand forecasts. Numerical tests indicate the proposed EP ap-proach provides a method to simultaneously estimate the appropriate order and parameter values of the ARMA model for diverse types of load data. Comparisons of fore-casting 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。