A new hybrid model to predict the electrical load in five states of Australia

Jinran Wu, Zhesen Cui, Yanyan Chen, Demeng Kong and You-Gan Wang

Energy, 2019, vol. 166, issue C, 598-609

Abstract: Short-term electrical load forecasting is an important part in the management of electrical power because electrical load is an extreme, complex non-linear system. To obtain parameter values that provide better performances with high precision, this paper proposes a new hybrid electrical load forecasting model, which combines ensemble empirical mode decomposition, extreme learning machine, and grasshopper optimization algorithm for short-term load forecasting. The most important difference that distinguishes this electrical load forecasting model from other models is that grasshopper optimization can search suitable parameters (weight values and threshold values) of extreme learning machine, while traditional parameters are selected randomly. It is applied in Australia electrical load prediction to show its superiority and applicability. The simulation studies are carried out using a data set collected from five main states (New South Wales, Queensland, Tasmania, South Australia and Victoria) in Australia from February 1 to February 27, 2018. Compared with all considered basic models, the proposed hybrid model has the best performance in predicting electrical load.

Keywords: Electrical load; Forecast; Ensemble empirical mode decomposition; Extreme learning machine; Grasshopper optimization algorithm (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:166:y:2019:i:c:p:598-609

DOI: 10.1016/j.energy.2018.10.076

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