Short term electricity load forecasting using a hybrid model

Jinliang Zhang, Yi-Ming Wei, Dezhi Li, Zhongfu Tan and Jianhua Zhou

Energy, 2018, vol. 158, issue C, 774-781

Abstract: Short term electricity load forecasting is one of the most important issue for all market participants. Short term electricity load is affected by natural and social factors, which makes load forecasting more difficult. To improve the forecasting accuracy, a new hybrid model based on improved empirical mode decomposition (IEMD), autoregressive integrated moving average (ARIMA) and wavelet neural network (WNN) optimized by fruit fly optimization algorithm (FOA) is proposed and compared with some other models. Simulation results illustrate that the proposed model performs well in electricity load forecasting than other comparison models.

Keywords: Electricity load forecasting; IEMD; ARIMA; WNN; FOA (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (60)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:158:y:2018:i:c:p:774-781

DOI: 10.1016/j.energy.2018.06.012

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