Short-term load forecasting by wavelet transform and evolutionary extreme learning machine

• Main Authors: Li, Song, Wang, Peng, Goel, Lalit
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2015
• Subjects: DRNTU::Engineering::Electrical and electronic engineering::Antennas, wave guides, microwaves, radar, radio
• Online Access: https://hdl.handle.net/10356/107399; http://hdl.handle.net/10220/25475; http://dx.doi.org/10.1016/j.epsr.2015.01.002
• Institution: Nanyang Technological University

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.