Developing Prediction System for Solar Power Plant Using Machine Learning Algorithms
Developing Prediction System for Solar Power Plant Using Machine Learning Algorithms
Yazar: Uzun, Yusuf
Dergi: Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, Vol. 9 No. 2, 2019
Anahtar kelimeler: Renewable sources,machine learning,ROC analysis

Theuse of renewable energy sources in the production of electricity has becomeinevitable in order to reduce the greenhouse gases left in the atmosphere thatcause the Earth to warm up. Although countries on a national basis haveimplemented a number of policies to support electricity generated fromrenewable energy sources, investments to produce electricity without a licenseon a local basis are not desirable. According to the climatic conditions of thepower plant of 1 MW installed founded in Konya and power plant production dataare monitored. Machine learning is a sub-branch of artificial intelligence thatdeals with the design and development of algorithms that allow computers todevelop 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 AdaBoostmachine 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 willbe generated from existing solar power plants and accordingly determine themeasures they will take to meet the electricity demand in the future. ROCanalyzes were performed for machine learning models and performance evaluationwas performed. In this study, the best performance estimation value obtainedfrom the solar power plant depending on the weather conditions was obtainedwith 92.24% accuracy.

Theuse of renewable energy sources in the production of electricity has becomeinevitable in order to reduce the greenhouse gases left in the atmosphere thatcause the Earth to warm up. Although countries on a national basis haveimplemented a number of policies to support electricity generated fromrenewable energy sources, investments to produce electricity without a licenseon a local basis are not desirable. According to the climatic conditions of thepower plant of 1 MW installed founded in Konya and power plant production dataare monitored. Machine learning is a sub-branch of artificial intelligence thatdeals with the design and development of algorithms that allow computers todevelop 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 AdaBoostmachine 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 willbe generated from existing solar power plants and accordingly determine themeasures they will take to meet the electricity demand in the future. ROCanalyzes were performed for machine learning models and performance evaluationwas performed. In this study, the best performance estimation value obtainedfrom the solar power plant depending on the weather conditions was obtainedwith 92.24% accuracy.