Rule Extraction and Performance Estimation by Using Variable Neighborhood Search for Solar Power Plant in Konya

2020 Turkish Journal of Electrical Engineering and Computer Sciences  
The use of renewable energy sources in the production of electricity has become inevitable in order to reduce 4 the greenhouse gases left in the atmosphere that cause the Earth to warm up. Although countries on a national basis 5 have implemented a number of policies to support electricity generated from renewable energy sources, investments to 6 produce electricity without a license on a local basis are not desirable. Those who want to invest medium and small 7 scale for the most reason expect
more » ... most reason expect that this work will be supported by real data. Although the electricity generated by 8 renewable investments is generated by simulation data, these data are not realistic for such investors. In this study, where 9 the climatic conditions of the power plant of 1 MW installed founded in Konya and power plant production data are 10 monitored. The Artificial Neural Network (ANN) can achieve a high value for accuracy, but these values are sometimes 11 complex and unclear. In the literature, a number of studies have been conducted using different methods to overcome 12 such problems. Real time Solar Power Plant (SPP) data was used to determine the feasibility and success of the proposed 13 method. The Variable Neighbourhood Search (VNS) metaheuristic method was used to acquire the optimal values belong 14 to input vectors, G h , which was maximized to the value of the fitness function Fs belong to output class node s. The 15 results obtained by the VNS method showed that the proposed method has the potential to produce the correct rules. 16 Generally, energy investors are curious about the return on their investment. It is very important for energy providers to 17 estimate how much electricity will be generated from existing solar power plants and accordingly determine the measures 18 they will take to meet the electricity demand in the future. In this study, the performance estimation value obtained 19 from the solar power plant depending on the weather conditions was obtained with 95.55% accuracy. 20
doi:10.3906/elk-1901-232 fatcat:dhpfnjxknzc4xnhwdggmbqdajy