Machine Learning Schemes for Anomaly Detection in Solar Power Plants

Mariam Ibrahim, Ahmad Alsheikh, Feras M. Awaysheh and Mohammad Dahman Alshehri
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Mariam Ibrahim: Department of Mechatronics Engineering, German Jordanian University, Amman 11180, Jordan
Ahmad Alsheikh: Department of Natural Science & Industrial Engineering, Deggendorf Institute of Technology, 94469 Deggendorf, Germany
Feras M. Awaysheh: Institute of Computer Science, Delta Center, University of Tartu, 51009 Tartu, Estonia
Mohammad Dahman Alshehri: Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia

Energies, 2022, vol. 15, issue 3, 1-17

Abstract: 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.

Keywords: anomaly detection; machine learning; time series analysis; correlation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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