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

Z. M. Yasin (Faculty of Electrical Engineering, Universiti Teknologi MARA)
N. A. Salim (Faculty of Electrical Engineering, Universiti Teknologi MARA)
N.F.A. Aziz (Faculty of Electrical Engineering, Universiti Tenaga Nasional)
Y.M. Ali (Faculty of Electrical Engineering, Universiti Teknologi MARA)
H. Mohamad (Faculty of Electrical Engineering, Universiti Teknologi MARA)

IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 9, No 3: September 2020

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.