Long-term load forecasting using grey wolf optimizer -least-squares support vector machine

Abstract

<p><span lang="EN-US">Long term load forecasting data is important for grid expansion and power system operation. Besides, it also important to ensure the generation capacity meet electricity demand at all times. In this paper, Least-Square Support Vector Machine (LSSVM) is used to predict the long-term load demand. Four inputs are considered which are peak load demand, ambient temperature, humidity and wind speed. Total load demand is set as the output of prediction in LSSVM. In order to improve the accuracy of the LSSVM, Grey Wolf Optimizer (GWO) is hybridized to obtain the optimal parameters of LSSVM namely GWO-LSSVM. Mean Absolute Percentage Error (MAPE) is used as the quantify measurement of the prediction model. The objective of the optimization is to minimize the value of MAPE. The performance of GWO-LSSVM is compared with other methods such as LSSVM and Ant Lion Optimizer – Least-Square Support Vector Machine (ALO-LSSVM). From the results obtained, it can be concluded that GWO-LSSVM provide lower MAPE value which is 0.13% as compared to other methods.</span></p>

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“…It is famous for its extreme sensitivity to a change in the values of its parameters. Consider the following given to start with LS-SVM [39]- [41]:…”

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“…The experimental results demonstrated that the PSO-SVM model provides better accuracy in comparison with the other two models named grey model (GM) and artificial neural network (ANN). Yasin et al [7] developed a hybrid prediction technique based on grey wolf optimizer combined with least square support vector machine (GWO-LSSVM) for grid expansion and power system operation. Temperature, peak load demand, humidity and wind speed are four measured were used as input to the model.…”