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Short-term Electric Load Forecasting Based on Wavelet Transform and GMDH
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 Title & Authors
Short-term Electric Load Forecasting Based on Wavelet Transform and GMDH
Koo, Bon-Gil; Lee, Heung-Seok; Park, Juneho;
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The group method of data handling (GMDH) algorithm has proven to be a powerful and effective way to extract rules or polynomials from an electric load pattern. However, because it is nonstationary, the load pattern needs to be decomposed using a discrete wavelet transform. In addition, if a load pattern has a complicated curve pattern, GMDH should use a higher polynomial, which requires complex computing and consumes a lot of time. This paper suggests a method for short-term electric load forecasting that uses a wavelet transform and a GMDH algorithm. Case studies with the proposed algorithm were carried out for one-day-ahead forecasting of hourly electric loads using data during the years 2008-2011. To prove the effectiveness of our proposed approach, the results were evaluated and compared with those obtained by Holt-Winters method and artificial neural network. Our suggested method resulted in better performance than either comparison group.
Discrete wavelet transform;GMDH;Short-term electric load forecasting;
 Cited by
U. S. Department of Energy, "Energy policy Act of 2005," section 1252, Feb. 2006.

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