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

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

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Published

2020-03-06

Issue

Section

Research Article

How to Cite

[1]
D. Nurwaha, “Using Artificial Intelligence Techniques for Prediction and Estimation of Photovoltaic System Output Power”, J. Mod. Sim. Mater., vol. 3, no. 1, pp. 15-21, Mar. 2020.