# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 2 AIoT in PV Systems: Background
### Artificial Intelligence Techniques
#### 2.1.3 Generative AI  
Generative AI (GenAI) represents a transformative branch of Artificial Intelligence focused on generating new content by learning patterns from existing data. It employs advanced machine learning techniques, particularly deep learning models [26], which are trained on large volumes of unstructured data. This enables the models to learn the underlying structures and relationships within the data. As a result, GenAI has led to signifi  
Figure 7: Backpropagation workflow in deep learning. icant breakthroughs across various domains, including natural language processing, image synthesis, and autonomous decision-making. In the context of PV system monitoring and control, GenAI can enhance fault detection, predictive maintenance, and energy forecasting by generating realistic simulations, detecting anomalies, and automating control strategies. Core components of GenAI include the following:  
* **Generative Adversarial Networks (GANs):** GANs consist of two neural networks--the generator and the discriminator--that are trained simultaneously in a competitive process. The generator creates synthetic data, while the discriminator assesses its authenticity. Through this adversarial training process, the generator gradually improves its ability to produce realistic data [27] (see Figure 8). GANs can be particularly useful for generating synthetic sensor data in cases where real-world datasets are limited or incomplete. This capability is especially valuable for PV fault detection and anomaly prediction, as GANs can simulate a range of fault scenarios (e.g., shading, panel degradation), enhancing the robustness of AI models. * **Variational Autoencoders (VAEs):** VAEs are another powerful technique within Generative AI. They work by encoding input data into a latent space, which is then decoded back into the original data space. This encoding-decoding process enables VAEs to generate new data points by sampling from the latent space, making them highly suitable for applications such as image generation and data augmentation [28]. In the context of PV systems, VAEs can be employed for anomaly detection by learning the normal operational behavior of solar panels and identifying deviations that may signal potential faults. This capability supports real-time monitoring and proactive maintenance by detecting early signs of performance degradation or energy losses. * **Transformers:** Transformers are a type of architecture used for natural language generation and understanding. Models like GPT (Generative Pre-trained Transformer) and BERT are based on the Transformer architecture. These models use self-attention mechanisms to capture dependencies and relationships within text, making them the backbone of large language models (LLMs) [29]. The Transformer architecture follows a structured workflow, as illustrated in Figure 9, where input text is first tokenized and converted into dense vector representations through an embedding layer, followed by positional encoding to incorporate order information. The encoder layers then apply multi-head self-attention mechanisms to capture relationships between tokens while processing information in parallel. The decoder layers generate output step by step using masked self-attention and cross-attention with encoder outputs, ultimately producing a probability distribution over the vocabulary through a final softmax layer to generate meaningful responses. Transformer-based models, such as LLMs, can enhance PV system monitoring through automated analysis of maintenance logs, fault reports, and operational data. By processing large volumes of text data, LLMs can assist engineers in diagnosing faults, summarizing reports, and even predicting optimal maintenance schedules. * **Diffusion Models:** Recently popularized for generating high-quality images, diffusion models operate by progressively refining random noise into detailed outputs.