# Harnessing the Cloud: A Novel Approach to Smart Solar Plant Monitoring
## 5 Artificial Intelligence in Solar Plant Monitoring
### Role of AI in Solar PV Systems  
Artificial intelligence (AI) can perform a vital role in the monitoring, analysis, and management of solar PV systems. AI can be beneficial in the following ways in the context of solar PV systems:  
1. **Predictive Models:** AI will facilitate the development of predictive machine learning models for optimizing renewable energy usage and reducing environmental impact [66]. The authors developed a prediction model for PV systems that measures weather irregularities. Their dataset for training and testing consists of a plant inverter and the weather measurement system and used different machine learning algorithms, including linear regression, random forest, principal component analysis, and support vector regression with RBF kernel to examine the data and create a model that can accurately predict the power output [66].
2. **Performance Optimization**: AI algorithms can make intelligent decisions based on the data collected by different sensors involved in the solar PV systems and the historical data to optimize the performance of the plant by adjusting the tilt angles of solar panels according to the sun's movement or a cleaning mechanism can be installed in place if there is dust on the panels [67].  
3. **Anomaly Detection:** In modern solar power plants, AI has been used for anomaly detection [68]. A recurrent neural network (RNN)-based model called AutoEncoder Long Short-Term Memory (AE-LSTM) based on the Genetic Algorithm (GA) as a hyperparameter tuner was applied to detect anomalies in two power plants. The authors demonstrated outstanding accuracy in detecting anomalies and compared them with the Isolation Forest (IF) and the Local Outlier Factor (LOF) methods [68].
4. **Energy Forecasting:** AI has been used in energy forecasting, by utilizing different machine learning algorithms including linear regression, random forest, principal component analysis, and support vector regression [69]. These algorithms are trained using historical meteorological data to estimate power plant solar energy generation.
5. **Predictive Maintenance**: AI can help the operator to take proactive measures for maintenance and minimize downtime by alerting the operator in real time through malfunction as well as fault detection or degradation in real time. AI algorithms can locate an underperforming panel and identify anomalies by analyzing data patterns and comparing them to the expected outcome [67].
6. **Energy Demand Forecasting and Operational Optimization**: Through AI, vendors can achieve grid stability, energy optimization, and reduce unnecessary energy wastage by analyzing historical data and weather patterns. The data-driven decision of AI can be helpful in grid integration by analyzing the energy demands and supply in real time, ensuring a smooth and cost-effective supply of power, low downtime, balanced generation, and balanced consumption.  
The selection of the AI model is based on different parameters like the monitoring tasks to be performed on the available datasets and the chosen predicted outcomes. Different aspects may suit different models and broad monitoring and optimization can be achieved by the combination of different AI models.