Using Artificial Intelligence Techniques for Prediction and Estimation of Photovoltaic System Output Power

Deogratias Nurwaha

doi:10.21467/jmsm.3.1.15-21

Authors

Deogratias Nurwaha Intitute of applied pedagogy, department of physics and technology, university of Burundi

DOI:

https://doi.org/10.21467/jmsm.3.1.15-21

Abstract

Two artificial intelligence methods, namely, support vector machines (SVM) and gene expression programming (GEP), were explored for prediction and estimation of the Photovoltaic (PV)output power. Measured values of temperature T (°C) and irradiance E (kWh/㎡) were used as inputs (independent variables) and PV output power P (Kw) was used as output (dependent variable). The statistical metrics were used to assess the predictive performances of the methods. The results of the two models were estimated and compared. The results showed that the two techniques performances are better and similar. Using GEP technique, the relationships between the two parameters and output power were established. Importance of each parameter as contributor to PV output power was also investigated. The results indicated that the SVM and GEP would become the powerful tools that could help estimate the PV output power capacity reserve.

Keywords:

artificial intelligence; PV output power; GEP; SVMs

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References

R. Hossain, A. Maung Than O, A. B. M. Shawkat Ali,Hybrid Prediction Method for Solar Power Using Different Computational Intelligence Algorithms,Smart Grid and Renewable Energy, 2013, 4, pp.76-87.

Soteris A. Kalogirou and A.Şencan, Artificial Intelligence Techniques in Solar Energy Applications,www.intechopen.

Hussein A Kazem,Jabar H. Yousif,Miqdam T Chaichan,Modelling of Daily Solar Energy System Prediction using Support Vector Machine for Oman, International Journal of Applied Engineering Research ISSN 0973-, 2016, pp. 10166-10172.

F. H. Anuwar and A. M.Omar, Future Solar Irradiance Prediction using Least Square Support Vector Machine, ,International Journal on Advanced Science EngineeringInformation technology, 2016, pp.

Ferreira, C. "Gene Expression Programming: A New Adaptive Algorithm for Solving Problems" (PDF). Complex Systems, 2001, pp. 87–129.

Ferreira, C. (2002). "Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence". Portugal: Angra do Heroismo. ISBN 972-95890-5-4.

Cortes, Corinna; Vapnik, Vladimir N, (PDF). Machine Learning, 1995, pp. 273.297. doi:10.1007/BF00994018.

Ben-Hur, Asa; Horn, David; Siegelmann, Hava; and Vapnik, Vladimir N.; "Support vector clustering"; Journal of Machine Learning Research, 2001,pp. 125–137.

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