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題 名 | A New Short-Term Load Forecasting Approach using Self-Organizing Fuzzy ARMAX Models=自我組模糊自迴歸-移動平均-輸入變數模式於短期負載預測 |
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作 者 | 黃昭明; | 書刊名 | 高苑學報 |
卷 期 | 6:2 1997.08[民86.08] |
頁 次 | 頁19-27 |
分類號 | 448.115 |
關鍵詞 | 自我組織模糊; 自迴歸-移動平均-輸入變數模式; 短期負載預測; Fuzzy ARMAX model; Evolutionary optimization; Short term load forecasting; Artificial neural networks; |
語 文 | 英文(English) |
英文摘要 | This paper proposes a new self-organizing model of fuzzy autoregressiv e moving average with exogenous input variables (FARMAX) for one day ahead hourly load forecasting of power systems. To achieve the purpose of self-organizing the FARMAS model, identification of the fuzzy model is formulated as a combinatorial optimization problem. Then a combined use of heuri stics and evolutionary programming (EP) scheme is relied on to solve the problem of determining optimal number of input variables, best partition of fuzzy space s and associated fuzzy membership functions. The proposed approach is verified by using diverse types of practical load and weather data for Taiwan Power (Ta ipower) systems. Comparisons are made of forecasting errors with the existing ARMAX model implemented by commercial SAS package and artifical neural networ ks (ANNs) method. |
本系統中英文摘要資訊取自各篇刊載內容。