Please use this identifier to cite or link to this item:
Title: Prediction of solar irradiance using grey Wolf optimizer least square support vector machine
Authors: Yasin, Z.M. 
Salim, N.A. 
Aziz, N.F.A. 
Mohamad, H. 
Wahab, N.A. 
Issue Date: 2019
Abstract: Prediction of solar irradiance is important for minimizing energy costs and providing high power quality in a photovoltaic (PV) system. This paper proposes a new technique for prediction of hourly-ahead solar irradiance namely Grey Wolf Optimizer Least Square Support Vector Machine (GWO-LSSVM). Least Squares Support Vector Machine (LSSVM) has strong ability to learn a complex nonlinear problems. In GWO-LSSVM, the parameters of LSSVM are optimized using Grey Wolf Optimizer (GWO). GWO algorithm is derived based on the hierarchy of leadership and the grey wolf hunting mechanism in nature. The main step of the grey wolf hunting mechanism are hunting, searching, encircling, and attacking the prey. The model has four input vectors: time, relative humidity, wind speed and ambient temperature. Mean Absolute Performance Error (MAPE) is used to measure the prediction performance. Comparative study also carried out using LSSVM and Particle Swarm Optimizer-Least Square Support Vector Machine (PSO-LSSVM). The results showed that GWO-LSSVM predicts more accurate than other techniques. Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved.
DOI: 10.11591/ijeecs.v17.i1.pp10-17
Appears in Collections:UNITEN Scholarly Publication

Show full item record

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.