Rule extraction and performance estimation by using variable neighborhood search for solar power plant in Konya

Abstract

The use of renewable energy sources in the production of electricity has become inevitable in order to reducethe greenhouse gases left in the atmosphere that cause the Earth to warm up. Although countries on a national basishave implemented a number of policies to support electricity generated from renewable energy sources, investments toproduce electricity without a license on a local basis are not desirable. Those who want to invest medium and small scalefor the most reason expect that this work will be supported by real data. Although the electricity generated by renewableinvestments is generated by simulation data, these data are not realistic for such investors. In this study, the climaticconditions of the power plant of 1 MW installed in Konya and power plant production data are monitored. The artificialneural network (ANN) can achieve a high value for accuracy, but these values are sometimes complex and unclear. Inthe literature, a number of studies have been conducted using different methods to overcome such problems. Real-timesolar power plant (SPP) data were used to determine the feasibility and success of the proposed method. The variableneighborhood 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 obtainedby the VNS method showed that the proposed method has the potential to produce the correct rules. Generally, energyinvestors are curious about the return on their investment. It is very important for energy providers to estimate howmuch electricity will be generated from existing solar power plants and accordingly determine the measures they willtake to meet the electricity demand in the future. In this study, the performance estimation value obtained from thesolar power plant depending on the weather conditions was obtained with 95.55% accuracy.