# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 2 AIoT in PV Systems: Background
### Artificial Intelligence Techniques
#### 2.1.2 Deep Learning  
Deep learning (DL) is a specialized subset of machine learning, inspired by the structure and function of the human brain. It utilizes Artificial Neural Networks--particularly Deep Neural Networks--to process and learn from large volumes of data [21].  
DL relies on neural networks composed of interconnected layers. These include the input layer, which receives raw data; one or more hidden layers, where computations are performed through nodes (neurons) using activation functions; and the output layer, which produces the final prediction or classification [22] (see Figure 6). This layered architecture enables DL models to process complex data and extract meaningful patterns effectively.  
A fundamental mechanism that enables deep learning is backpropagation, an optimization technique used to minimize prediction errors by adjusting the weights of a neural network. It operates by propagating the error backward from the output layer to the earlier layers, allowing the model to iteratively update its parameters and refine its predictions. Backpropagation is essential for efficiently training deep networks, enabling them to learn complex patterns, improve performance, and generalize effectively across diverse datasets (see Figure 7).  
Figure 5: Workflow for the development of a machine learning model.  
Figure 6: DL neural network architecture.  
There are several types of architectures in DL, each suited for different tasks.
* **Convolutional Neural Networks (CNNs)**: These are particularly effective for image and video processing, utilizing convolutional layers to detect spatial hierarchies and patterns in data [21]. In the context of solar panel monitoring, CNNs can analyze UAV or satellite imagery to detect faults such as cracks, hot spots, or dust accumulation.
* **Recurrent Neural Networks (RNNs)**: Designed for sequential data, RNNs are well suited to tasks involving time series or natural language processing, where temporal dependencies are critical. In solar energy systems, RNNs can be employed to forecast energy production by analyzing historical weather and energy data, thereby aiding in performance optimization.
* **Long short-term memory (LSTM)**: A specialized form of RNN, LSTMs are engineered to capture long-term dependencies and retain information over extended time intervals [23]. They are particularly useful in predictive maintenance by identifying temporal patterns in time series sensor data that may signal potential equipment failures.  
Training deep learning models typically requires large amounts of labeled data. During the learning phase, these models utilize optimization techniques such as gradient descent and backpropagation to adjust network weights and minimize prediction errors. Gradient descent iteratively updates model parameters by moving in the direction that reduces the cost function, thereby finding the optimal solution. Backpropagation complements this process by computing the gradient of the cost function with respect to each weight using the chain rule, allowing the error to be propagated backward from the output layer to the input layer [24].  
Deep learning models thrive on large datasets, often requiring thousands or even millions of samples to achieve optimal performance. The increasing availability of vast datasets and computational power has been a key driver of recent advancements in DL, enabling the development of highly accurate models that frequently outperform traditional machine learning approaches across a wide range of applications [25].