Journal
ENERGIES
Volume 15, Issue 3, Pages -Publisher
MDPI
DOI: 10.3390/en15031082
Keywords
anomaly detection; machine learning; time series analysis; correlation
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Funding
- Deanship of Graduate Studies and Scientific Research at the German Jordanian University [SATS 03/2020]
- [MOBTT75]
- [TURSP-2020/126]
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This paper evaluates the performance of different machine learning schemes and applies them to detect anomalies on photovoltaic components, providing models to identify healthy and abnormal behaviors in photovoltaic systems and offering clear insights for solving complex anomaly detection problems.
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.
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