A new short-term load forecasting approach using self-organizing fuzzy ARMAX models

被引：94

作者：

Yang, HT ^{[1
]}

Huang, CM

机构：

[1] Chung Yuan Christian Univ, Dept Elect Engn, Chungli 320, Taiwan

[2] Kaoyuan Jr Coll TEchnol & Commerce, Dept Elect Engn, Kaohsiung 821, Taiwan

来源：

IEEE TRANSACTIONS ON POWER SYSTEMS | 1998年 / 13卷 / 01期

关键词：

fuzzy ARMAX model; evolutionary optimization; short term load forecasting; artificial neural networks;

D O I：

10.1109/59.651639

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper proposes a new self-organizing model of fuzzy autoregressive 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 FARMAX model, identification of the fuzzy model is formulated as a combinatorial optimization problem. Then a combined use of heuristics and evolutionary programming (EP) scheme is relied on to solve the problem of determining optimal number of input variables, best partition of fuzzy spaces and associated fuzzy membership functions. The proposed approach is verified by using diverse types of practical load and weather data for Taiwan Power (Taipower) systems. Comparisons are made of forecasting errors with the existing ARMAX model implemented by commercial SAS package and artificial neural networks (ANNs) method.

引用

页码：217 / 225

页数：9

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