Open AccessLong-term load forecasting using grey wolf optimizer -least-squares support vector machine
Author(s) -
Zuhaila Mat Yasin,
Nur Ashida Salim,
Nur Fadilah Ab Aziz,
Yusnita Mohd Ali,
Hasmaini Mohamad
Publication year - 2020
Publication title -
iaes international journal of artificial intelligence
Language(s) - English
Resource type - Journals
eISSN - 2252-8938
pISSN - 2089-4872
DOI - 10.11591/ijai.v9.i3.pp417-423
Subject(s) - mean absolute percentage error , term (time) , support vector machine , least squares support vector machine , mean squared error , computer science , least squares function approximation , ant colony optimization algorithms , wind power , wind speed , mathematical optimization , mathematics , statistics , algorithm , artificial intelligence , meteorology , engineering , physics , quantum mechanics , estimator , electrical engineering
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.