Estimating Energy Production of Solar Power Plant at the University of Bakırçay Using Artificial Neural Networks Based on Meteorological Conditions

Abstract

The rapid depletion of fossil fuels and environmental concerns have led people to work on renewable energy sources. In order to leave a cleaner and more liveable world for future generations and enable developed countries to produce more economical energy using their own resources, major investments have been made in renewable energy resources. Photovoltaic (PV) energy has a large share among renewable energy sources. Turkey has taken its place among the countries that are aware of the PV energy potential and invest in this field. The ratio of installed PV energy power to total installed power is also increased day by day in Turkey. However, meteorological factors affecting PV energy production make it difficult to compute energy production in advance. In this study, the relationship between meteorological data and power generation data was analyzed using the power generation data of the solar power plant (SPP) with an installed power of 400 kW in the student car park of the University of Bakırçay and the meteorological data of the province of İzmir. As a result of the comparison of the tests, energy production with respect to meteorological factors achieve a remarkable success rate with 95.3% when artificial neural networks are employed.

Keywords

• renewable energy
• photovoltaic energy
• energy estimation

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