Smart Anomaly Detection in Solar Power Plants with Auto Encoder and LSTM Integration

Detection of anomalies in solar power plants is a critical task for the analysis of operating conditions so as to optimize the outcome in terms of safety and output. Due to scaling-up of renewable energy resources, it is imperative to detect abnormalities in photovoltaic (PV) systems on time in order to avoid large losses and avoid possible dangers like failure of equipment or fire. However, a number of techniques which are used for anomaly detection are generally accompanied by problems with regards to its versatility, efficiency and accuracy during its application for real-time analysis of large scale multi-dimensional time-series data. It is possible that traditional approaches have difficulties in capturing the rich structures and temporal interactions inherent in solar energy data, which results in slow response and higher maintenance requirements. In this paper, we present an AE+LSTM model to accommodate these challenges of anomaly detection for solar power plants. The proposed AE+LSTM combines the best of AEs which are good at learning compact fixed-sized representations of data and LSTM networks which are great for sequential data over time. It also deserves an improved view of sorting regular patterns of operation from other forms that need immediate attention or intervention. When comparing our work to current machine learning and deep learning approaches, experimental results clearly reveal that the AE+LSTM model yields the highest accuracy, precision, recall, and F1-score. Thus, using this model, the existing solar power plant's monitoring can be advanced by operators to improve general performance, reliability, and safety of energy generation.