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Article

Machine Learning Schemes for Anomaly Detection in Solar Power Plants

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Department of Mechatronics Engineering, German Jordanian University, Amman 11180, Jordan
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Department of Natural Science & Industrial Engineering, Deggendorf Institute of Technology, 94469 Deggendorf, Germany
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Institute of Computer Science, Delta Center, University of Tartu, 51009 Tartu, Estonia
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Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
*
Author to whom correspondence should be addressed.
Academic Editor: Anastasija Nikiforova
Energies 2022, 15(3), 1082; https://doi.org/10.3390/en15031082
Received: 29 December 2021 / Revised: 24 January 2022 / Accepted: 26 January 2022 / Published: 1 February 2022
The rapid industrial growth in solar energy is gaining increasing interest in renewable power from smart grids and plants. Anomaly detection in photovoltaic (PV) systems is a demanding task. In this sense, it is vital to utilize the latest updates in machine learning technology to accurately and timely disclose different system anomalies. This paper addresses this issue by evaluating the performance of different machine learning schemes and applying them to detect anomalies on photovoltaic components. The following schemes are evaluated: AutoEncoder Long Short-Term Memory (AE-LSTM), Facebook-Prophet, and Isolation Forest. These models can identify the PV system’s healthy and abnormal actual behaviors. Our results provide clear insights to make an informed decision, especially with experimental trade-offs for such a complex solution space. View Full-Text
Keywords: anomaly detection; machine learning; time series analysis; correlation anomaly detection; machine learning; time series analysis; correlation
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MDPI and ACS Style

Ibrahim, M.; Alsheikh, A.; Awaysheh, F.M.; Alshehri, M.D. Machine Learning Schemes for Anomaly Detection in Solar Power Plants. Energies 2022, 15, 1082. https://doi.org/10.3390/en15031082

AMA Style

Ibrahim M, Alsheikh A, Awaysheh FM, Alshehri MD. Machine Learning Schemes for Anomaly Detection in Solar Power Plants. Energies. 2022; 15(3):1082. https://doi.org/10.3390/en15031082

Chicago/Turabian Style

Ibrahim, Mariam, Ahmad Alsheikh, Feras M. Awaysheh, and Mohammad Dahman Alshehri. 2022. "Machine Learning Schemes for Anomaly Detection in Solar Power Plants" Energies 15, no. 3: 1082. https://doi.org/10.3390/en15031082

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