Short-term PV power forecasting using hybrid GASVM technique

VanDeventer, William and Jamei, Elmira and Thirunavukkarasu, Gokul Sidarth and Seyedmahmoudian, Mehdi and Tey, Kok Soon and Horan, Ben and Mekhilef, Saad and Stojcevski, Alex (2019) Short-term PV power forecasting using hybrid GASVM technique. Renewable Energy, 140. pp. 367-379. ISSN 0960-1481, DOI

Full text not available from this repository.
Official URL:


The static, clean and movement free characteristics of solar energy along with its contribution towards global warming mitigation, enhanced stability and increased efficiency advocates solar power systems as one of the most feasible energy generation resources. Considering the influence of stochastic weather conditions over the output power of photovoltaic (PV) systems, the necessity of a sophisticated forecasting model is increased rapidly. A genetic algorithm-based support vector machine (GASVM) model for short-term power forecasting of residential scale PV system is proposed in this manuscript. The GASVM model classifies the historical weather data using an SVM classifier initially and later it is optimized by the genetic algorithm using an ensemble technique. In this research, a local weather station was installed along with the PV system at Deakin University for accurately monitoring the immediate surrounding environment avoiding the inaccuracy caused by the remote collection of weather parameters (Bureau of Meteorology). The forecasting accuracy of the proposed GASVM model is evaluated based on the root mean square error (RMSE) and mean absolute percentage error (MAPE). Experimental results demonstrated that the proposed GASVM model outperforms the conventional SVM model by the difference of about 669.624 W in the RMSE value and 98.7648% of the MAPE error. © 2019

Item Type: Article
Uncontrolled Keywords: Genetic algorithm (GA); Genetic algorithm based support vector machine (GASVM); Photovoltaic (PV); Short-term forecasting; Support vector machine (SVM)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Computer Science & Information Technology
Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 05 Dec 2019 08:06
Last Modified: 05 Dec 2019 08:06

Actions (login required)

View Item View Item