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http://dx.doi.org/10.9717/kmms.2021.24.2.222

Predicting the Power Output of Solar Panels based on Weather and Air Pollution Features using Machine Learning  

Chuluunsaikhan, Tserenpurev (Dept. of Computer Science, Chungbuk National University)
Nasridinov, Aziz (Dept. of Computer Science, Chungbuk National University)
Choi, Woo Seok (Dept. of Bigdata, Chungbuk National University)
Choi, Da Bin (Dept. of Management Information Systems, Chungbuk National University)
Choi, Sang Hyun (Dept. of MIS. Dept. of Bigdata, Chungbuk National University)
Kim, Young Myoung (BC Card Co., LTD)
Publication Information
Journal of Korea Multimedia Society / v.24, no.2, 2021 , pp. 222-232 More about this Journal
Abstract
The power output of solar panels highly depends on environmental situations like weather and air pollution. Due to bad weather or air pollution, it is difficult for solar panels to operate at their full potential. Knowing the power output of solar panels in advance helps set up the solar panels correctly and work their possible potential. This paper presents an approach to predict the power output of solar panels based on weather and air pollution features using machine learning methods. We create machine learning models with three kinds set of features, such as weather, air pollution, and weather and air pollution. Our datasets are collected from the area of Seoul, South Korea, between 2017 and 2019. The experimental results show that the weather and air pollution features can be efficient factors to predict the power output of solar panels.
Keywords
Solar Panel Power; Machine Learning; Solar Panel and Weather; Solar Panel and Air Pollution;
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