Developing Prediction System for Solar Power Plant Using Machine Learning Algorithms

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. According to the climatic conditions of the power plant of 1 MW installed founded in Konya and power plant production data are monitored. Machine learning is a sub-branch of artificial intelligence that deals with the design and development of algorithms that allow computers to develop their behavior based on experimental data. In this study, Naive Bayes, Decision Tree, CN2 Rule Induction, Random Forest, Support Vector Machine, k-Nearest Neighbor, Artificial Neural Network, Logistic Regression and AdaBoost machine learning algorithms are used for prediction and classification. Generally, energy investors are curious about the return on their investment. It is very important for energy providers to predict 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. ROC analyzes were performed for machine learning models and performance evaluation was performed. In this study, the best performance estimation value obtained from the solar power plant depending on the weather conditions was obtained with 92.24% accuracy.