Energy Performance Analysis and Output Prediction Pipeline for East-West Solar Microgrids

Khanh Nguyen (), Kevin Koch, Swati Chandna and Binh Vu
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Khanh Nguyen: Applied Data Science and Analytics, SRH University Heidelberg, 69123 Heidelberg, Germany
Kevin Koch: Applied Data Science and Analytics, SRH University Heidelberg, 69123 Heidelberg, Germany
Swati Chandna: Applied Data Science and Analytics, SRH University Heidelberg, 69123 Heidelberg, Germany
Binh Vu: Applied Data Science and Analytics, SRH University Heidelberg, 69123 Heidelberg, Germany

J, 2024, vol. 7, issue 4, 1-18

Abstract: Local energy networks, known as microgrids, can operate independently or in conjunction with the main grid, offering numerous benefits such as enhanced reliability, sustainability, and efficiency. This study focuses on analyzing the factors that influence energy performance in East-West microgrids, which have the unique advantage of capturing solar radiation from both directions, maximizing energy production throughout the day. A predictive pipeline was also developed to assess the performance of various machine learning models in forecasting energy output. Key input data for the models included solar radiation levels, photovoltaic (DC) energy, and the losses incurred during the conversion from DC to AC energy. One of the study’s significant findings was that the east side of the microgrid received higher radiation and experienced fewer losses compared to the west side, illustrating the importance of orientation for efficiency. Another noteworthy result was the predicted total energy supplied to the grid, valued at €15,423. This demonstrates that the optimized energy generation not only meets grid demand but also generates economic value by enabling the sale of excess energy back to the grid. The machine learning models—Random Forest, Extreme Gradient Boosting, and Recurrent Neural Networks—showed superior performance in energy prediction, with mean squared errors of 0.000318, 0.000104, and 0.000081, respectively. The research concludes that East-West microgrids have substantial potential to generate significant energy and economic benefits. The developed energy prediction pipeline can serve as a useful tool for optimizing microgrid operations and improving their integration with the main grid.

Keywords: East-West microgrids; energy prediction pipeline; energy output analysis; solar energy; grid-connected mode; renewable energy; machine learning models; global radiation; energy forecasting (search for similar items in EconPapers)
JEL-codes: I1 I10 I12 I13 I14 I18 I19 (search for similar items in EconPapers)
Date: 2024
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