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Turkish Journal of Electrical Engineering and Computer Sciences

DOI

10.3906/elk-1901-232

Abstract

The use of renewable energy sources in the production of electricity has become inevitable in order to reduce the greenhouse gases left in the atmosphere that cause the Earth to warm up. Although countries on a national basis have implemented a number of policies to support electricity generated from renewable energy sources, investments to produce electricity without a license on a local basis are not desirable. Those who want to invest medium and small scale for the most reason expect that this work will be supported by real data. Although the electricity generated by renewable investments is generated by simulation data, these data are not realistic for such investors. In this study, the climatic conditions of the power plant of 1 MW installed in Konya and power plant production data are monitored. The artificial neural network (ANN) can achieve a high value for accuracy, but these values are sometimes complex and unclear. In the literature, a number of studies have been conducted using different methods to overcome such problems. Real-time solar power plant (SPP) data were used to determine the feasibility and success of the proposed method. The variable neighborhood search (VNS) metaheuristic method was used to acquire the optimal values belonging to input vectors, Gh , which were maximized to the value of the fitness function Fs belonging to output class node s. The results obtained by the VNS method showed that the proposed method has the potential to produce the correct rules. Generally, energy investors are curious about the return on their investment. It is very important for energy providers to estimate how much electricity will be generated from existing solar power plants and accordingly determine the measures they will take to meet the electricity demand in the future. In this study, the performance estimation value obtained from the solar power plant depending on the weather conditions was obtained with 95.55% accuracy.

Keywords

Variable neighborhood search, renewable sources, artificial neural network.

First Page

635

Last Page

645

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