Performance Analysis of Dual-Axis Solar Tracking System

Trzasko, W.

doi:10.14313/par_227/11

Cited by 2 publications

(4 citation statements)

References 6 publications

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“…1) usytułowana jest na kampusie Politechniki Białostockiej w Białymstoku. Elementami składowymi elektrowni są dwie turbiny wiatrowe oraz cztery zespoły paneli fotowoltaicznych [13]. Wśród paneli możemy wyróżnić: − panele ustawione optymalnie, o mocy DC 3,0 kWp (PV1), − panele na trackerze, o mocy DC 3,0 kWp (PV3), − panele na elewacji południowo-wschodniej, o mocy DC 1,5 kWp (PV2a), − panele na elewacji południowo-zachodniej, o mocy DC 1,5 kWp (PV2b).…”

Section: Materiały I Metodyunclassified

The Use of Machine Learning Algorithms to Forecast Energy Production in a Small PV System

Idźkowski,

Sumorek

2024

PAR

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The article presents data analysis for predicting energy production in photovoltaic (PV) power plant systems. The purpose of long-term forecasts is to determine the effectiveness of preventive actions and manage the power system effectively. Climate variables affecting the production of electricity in photovoltaic systems were analyzed. Forecasting methods using machine learning techniques such as Multi-Layer Perceptron (MLP) neural networks and Support Vector Machine (SVM) were compared. In addition, metrics were selected to determine the quality of forecasts. Determining the quality of forecasts was based on the actual varying conditions, not on the weather forecast data. The way of data preparation to create forecasting models were presented and the models with the best metrics were selected. For this purpose, the Scikit-learn library was used to create scripts in Python. The best results were obtained for regression models: MLPRegressor, CatBoostRegressor and Support Vector Regression. Actual measurement data from a system of optimally-positioned panels with a power of 3.0 kWp were used. For the MLPRegressor model, the highest coefficient of determination 0.605 was achieved with the smallest root-mean-square error of 1.79 KWh.

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Section: Materiały I Metodyunclassified

The Use of Machine Learning Algorithms to Forecast Energy Production in a Small PV System

Idźkowski,

Sumorek

2024

PAR

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“…Najlogiczniejsze, co również jest potwierdzone w literaturze naukowej [8], wydaje się ustawienie paneli fotowoltaicznych prostopadle do słońca, które świeci o 12:00 w dniu 21 marca lub 23 września. W dalszej części artykułu ta teza zostanie potwierdzona lub zaprzeczona.…”

Section: Rys 3 Panel Polikrystaliczny I Monokrystalicznyunclassified

“…W celu podniesienia skuteczności systemów zasilania słonecznego można wykorzystać układy do śledzenia słońca, aby kąt horyzontalny i nachylenia paneli był identyczny z kątem położeniem słońca na niebie. Istnieją dwa rozwiązania takich urządzeń: − jednoosiowy system nadążny za słońcem [5], − dwuosiowy system nadążny za słońcem [8].…”

Section: Podsumowanieunclassified

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Analysis of the Correct Installation of Solar Panels for IoT Systems

Słota¹

2020

PAR

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The article considers the impact of the building and roof structures on the efficiency of obtaining electricity from photovoltaic panels. Meteorological and astronomical data were also taken into account. The result of the analysis on the basis of installations in single-family houses was used as a base for installations in IoT systems. In addition, mechanical installation of photovoltaic panels for measuring systems in island configuration will be considered. The article was written as a part of Statutory Work 1115538-173.

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Performance Analysis of Dual-Axis Solar Tracking System

Cited by 2 publications

References 6 publications

The Use of Machine Learning Algorithms to Forecast Energy Production in a Small PV System

The Use of Machine Learning Algorithms to Forecast Energy Production in a Small PV System

Analysis of the Correct Installation of Solar Panels for IoT Systems

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