Developing Prediction System for Solar Power Plant Using Machine Learning Algorithms

Year 2020, , 747 - 755, 15.06.2020

Yusuf Uzun

https://doi.org/10.17798/bitlisfen.575808

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.

Keywords

Renewable sources, machine learning, ROC analysis

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