# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 3 AIoT Applications in PV Systems: A Comprehensive Review
### Bridging AI and IoT: Innovations in PV Energy Management
The researchers aimed to enhance fault detection in solar panels by integrating deep learning models--MobileNetV2 and InceptionV3--into the system. These models were evaluated for their accuracy, loss values, and computational efficiency in detecting solar panel defects. MobileNetV2 achieved 80% accuracy and proved effective for resource-constrained environments, while InceptionV3 reached 90% accuracy with higher resource demands. By customizing ISO 50001:2018 standards [192] to the university's specific needs, the study underlines how AIoT and deep learning can be practically applied for sustainable and intelligent energy management in academic institutions. As well, [PERSON] et al. [193] present a real-world case study where solar energy is integrated into a distributed energy system using IoT and AI technologies. In this case study, IoT sensors and smart meters collect real-time data on energy usage, occupancy, temperature, and lighting, including solar energy generation. The AI algorithms then analyze these data to optimize solar energy generation, ensuring maximum efficiency. The case study highlights how solar energy is monitored and managed in a practical, real-world setting, demonstrating the feasibility, cost-effectiveness, and sustainability of using IoT and AI for energy management in buildings. In the context of agriculture, the paper by [PERSON] et al. [194] presents a solar-powered portable water pump (SPWP) integrated into an IoT-enabled smart irrigation system that was tested in a real-world environment. The system uses a NodeMCU microcontroller and sensors to monitor soil moisture, temperature, and humidity. It enables farmers to control the pump remotely through the Blynk IoT cloud and a smartphone app. The portable, eco-friendly pump is powered by solar energy and helps reduce electricity costs while improving water usage efficiency. The system's functionality and performance were practically evaluated, demonstrating its effectiveness in real-world agricultural settings. In smart home energy management, [PERSON] et al. [195] present the design and field implementation of a smart Home Energy Management System (HEMS) as part of a pilot project in a test-bed house at the Smart Campus in Benguerir, Morocco. The system improves residential energy efficiency through the smart integration and management of PV energy, using an AI-based multi-objective optimization algorithm. It combines supply-side (generation, storage, consumption) and demand-side (flexible appliance scheduling) management, based on electricity pricing, weather forecasts, PV generation, and user preferences. The results show that the system effectively reduces energy costs, boosts PV self-consumption, and balances comfort with savings, offering a promising model for scaling up smart energy solutions in residential buildings. For fault detection in PV systems, the project by [PERSON] et al. [196] demonstrates a real-world implementation of an AI-based IoT platform for diagnosing PV systems using infrared thermography (IRT). The system was tested on an actual PV installation, where various fault conditions--such as dirt, shading, and short-circuits--were intentionally induced to evaluate performance. Two AI models (deep learning and machine learning) were trained to detect hot spots under irradiance as low as 300 W/m\({}^{2}\), overcoming limitations of traditional IRT tools. The successful field deployment highlights the system's high sensitivity (0.995) and accuracy (0.923), offering a practical and cost-efficient tool for PV maintenance in real environments. An alternative perspective is offered by [PERSON] et al. [197]. In this paper, they review how AI and Industrial IoT (IIoT) can be integrated into solar energy systems at a national level. It highlights the use of solar-powered sensor nodes for monitoring and optimizing energy usage, fault detection in PV plants, and improving energy management. The proposed open-source, distributed architecture supports efficient solar energy harvesting and real-time data analysis.