Yapay Sinir Ağları ile Güneş Enerjisi Santralinin Modellenmesi

Author:

ARSLAN SerenORCID,ESEN Hikmet1ORCID,AVCI Engin1ORCID,CENGİZ CanORCID

Affiliation:

1. FIRAT UNIVERSITY

Abstract

This study will enable us to estimate the power of the solar power plant with measurement data such as outdoor temperature-humidity, wind and precipitation amount, to protect the system from imbalance, and to determine the instant and daily effective energy trade more easily. By taking the data from the solar power plant installed in Samsun, Turkey, estimation was made with Artificial Neural Networks for electricity generation. In this study, Levenberg-Marguardt feed-forward backprop learning algorithm was used to find the best approach in the network. The best prediction results were obtained from the 2-layer and 5-neuron Artificial Neural Networks model, and it was observed that the system gave better training results as the number of iterations increased (multiple determination coefficient, R2, 0.99818).

Publisher

International Journal of Innovative Engineering Applications

Subject

Applied Mathematics,General Mathematics

Reference22 articles.

1. Cengiz, C. (2023). Comparison of the parameters of solar power plants with the same characteristics installed in four different directions. (Master’s Dissertation, Fırat University).

2. International Energy Agency (2023). Solar PV Global Supply Chains – Analysis 2023. Retrieved March 2, 2023 from https://www.iea.org/reports/solar-pv-global-supply-chains/executive-summary

3. Sahin, H., Esen, H. (2022). The usage of renewable energy sources and its effects on GHG emission intensity of electricity generation in Turkey. Renew Energy 192:859–69.

4. Republic of Turkey Ministry of Energy and Natural Resources. Electricity (2023). Retrieved March 2, 2023 from https://enerji.gov.tr/infobank-energy-electricity

5. Alvara, R.R. (2018). Design and Modelling of a Large-Scale PV Plant. Escola Tècnica Superior d’Enginyeria Industrial de Barcelona.

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