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Open AccessArticle

Industry Experience of Developing Day-Ahead Photovoltaic Plant Forecasting System Based on Machine Learning

1
Ural Power Engineering Institute, Ural Federal University named after the first President of Russia B.N. Yeltsin, 620002 Ekaterinburg, Russia
2
Power Plants Department, Novosibirsk State Technical University, 630073 Novosibirsk, Russia
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Department of Electrical and Electronics Engineering, Bharath Institute of Higher Education and Research, Chennai 600073, India
4
Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai 600073, India
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Youth Research Institute, Saint Petersburg Electrotechnical University “LETI”, 197376 Saint Petersburg, Russia
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(20), 3420; https://doi.org/10.3390/rs12203420
Received: 15 September 2020 / Revised: 10 October 2020 / Accepted: 15 October 2020 / Published: 18 October 2020
(This article belongs to the Special Issue Assessment of Renewable Energy Resources with Remote Sensing)
This article highlights the industry experience of the development and practical implementation of a short-term photovoltaic forecasting system based on machine learning methods for a real industry-scale photovoltaic power plant implemented in a Russian power system using remote data acquisition. One of the goals of the study is to improve photovoltaic power plants generation forecasting accuracy based on open-source meteorological data, which is provided in regular weather forecasts. In order to improve the robustness of the system in terms of the forecasting accuracy, we apply newly derived feature introduction, a factor obtained as a result of feature engineering procedure, characterizing the relationship between photovoltaic power plant energy production and solar irradiation on a horizontal surface, thus taking into account the impacts of atmospheric and electrical nature. The article scrutinizes the application of different machine learning algorithms, including Random Forest regressor, Gradient Boosting Regressor, Linear Regression and Decision Trees regression, to the remotely obtained data. As a result of the application of the aforementioned approaches together with hyperparameters, tuning and pipelining of the algorithms, the optimal structure, parameters and the application sphere of different regressors were identified for various testing samples. The mathematical model developed within the framework of the study gave us the opportunity to provide robust photovoltaic energy forecasting results with mean accuracy over 92% for mostly-sunny sample days and over 83% for mostly cloudy days with different types of precipitation. View Full-Text
Keywords: feature engineering; forecasting; graphical user interface software; machine learning; photovoltaic power plant feature engineering; forecasting; graphical user interface software; machine learning; photovoltaic power plant
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MDPI and ACS Style

Khalyasmaa, A.I.; Eroshenko, S.A.; Tashchilin, V.A.; Ramachandran, H.; Piepur Chakravarthi, T.; Butusov, D.N. Industry Experience of Developing Day-Ahead Photovoltaic Plant Forecasting System Based on Machine Learning. Remote Sens. 2020, 12, 3420.

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