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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.