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Title: Short-term load forecasting by wavelet transform and evolutionary extreme learning machine
Authors: Li, Song
Wang, Peng
Goel, Lalit
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Antennas, wave guides, microwaves, radar, radio
Issue Date: 2015
Source: Li, S., Wang, P., & Goel, L. (2015). Short-term load forecasting by wavelet transform and evolutionary extreme learning machine. Electric power systems research, 122, 96-103.
Series/Report no.: Electric power systems research
Abstract: This paper proposes a novel short-term load forecasting (STLF) method based on wavelet transform, extreme learning machine (ELM) and modified artificial bee colony (MABC) algorithm. The wavelet transform is used to decompose the load series for capturing the complicated features at different frequencies. Each component of the load series is then separately forecasted by a hybrid model of ELM and MABC (ELM-MABC). The global search technique MABC is developed to find the best parameters of input weights and hidden biases for ELM. Compared to the conventional neuro-evolution method, ELM-MABC can improve the learning accuracy with fewer iteration steps. The proposed method is tested on two datasets: ISO New England data and North American electric utility data. Numerical testing shows that the proposed method can obtain superior results as compared to other standard and state-of-the-art methods.
ISSN: 0378-7796
DOI: 10.1016/j.epsr.2015.01.002
Rights: © 2015 Elsevier B.V. This is the author created version of a work that has been peer reviewed and accepted for publication by Electric Power Systems Research, Elsevier B.V. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [Article DOI:].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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