# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 3 AIoT Applications in PV Systems: A Comprehensive Review
### Applications of AI in PV Systems
#### 3.1.3 Energy Forecasting and Optimization  
Accurately forecasting solar energy production and optimizing energy management are vital for enhancing the efficiency and reliability of PV systems. Researchers have explored diverse methodologies to address these challenges, leveraging advanced machine learning techniques, hybrid models, and optimization frameworks. A wide range of studies has focused on improving forecasting accuracy, handling weather variability, optimizing energy dispatch, and managing uncertainties, with approaches varying across different forecasting horizons. Early efforts to improve forecasting accuracy include the work of [PERSON] et al. [106], who compared physical models and neural network (NN)-based statistical models for day-ahead PV power forecasting. Their study demonstrated the superior performance of NN models, achieving a normalized Root Mean Square Error (nRMSE) of 10.5%, compared to 12.45% for physical models. Similarly, [PERSON] et al. [107] employed ANNs with time series preprocessing to predict day-ahead solar irradiation, reducing forecasting errors by 5-6% compared to traditional methods such as ARIMA. For short-term predictions, [PERSON] et al. [108] focused on a 5 min forecasting model for tilted surfaces, achieving high accuracy with an nRMSE of 8.81%. These studies highlighted the importance of high-quality input data and preprocessing techniques for achieving reliable predictions. [PERSON] et al. [109] contributed to improving forecasting accuracy by introducing a hybrid Estimation Model (EM) using Adaptive Resonance Theory (ART) neural networks. This model refined power predictions from single-diode models, enhancing accuracy for nonlinear system behavior. [PERSON] et al. [110] compared General Regression Neural Networks (GRNNs) and Feedforward Backpropagation Neural Networks (FFBP) for solar power forecasting, finding FFBP to outperform GRNNs in terms of accuracy when predicting power output from temperature and irradiance data. Building on these foundational works, [PERSON] and [PERSON] [111] introduced a short-term solar irradiance forecasting framework \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Study** & **Approach** & **AI Technique** & **Data Inputs** & **Fault Type** & **Key Contributions** & **Limitations** \\ \hline [PERSON] et al. [105] & Predict performance trends and detect underperformance change conditions using advanced statistical and ML techniques. & XGBoost + One-Class SVM + Prophet & Current, voltage, performance data (trends) & Inverter shutdowns, string disconnections & High sensitivity (92.9\%) and accuracy (99.4\%); detects specific conditions like inverter conditions; scalability to very large systems is untested. & May require additional accuracy (99.4\%); resources for deployment in diverse climatic conditions; conditions; scalability to very large systems is untested. & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._using optimized kNN and ANN models. Their approach incorporated feature extraction techniques to improve forecasting for time horizons ranging from 15 min to 2 h. [PERSON] and [PERSON] [112] proposed a hybrid method for forecasting the 24 h ahead energy output of PV systems based on daily weather forecasts. Their approach integrates stepwise regression to identify the most significant meteorological variables, which are then used as inputs for three single-stage models: GRNN, feedforward neural network (FFNN), and multiple linear regression (MLR). The hybrid models demonstrated superior performance compared to their single-stage counterparts, with the stepwise regression-feedforward neural network (SR-FFNN) model achieving the best results. In contrast, [PERSON] et al. [113] applied LSTM models to forecast PV power output across multiple time horizons, ranging from 15 min to several days, improving grid stability and reducing dependency on non-renewable energy. Weather variability remains a significant challenge in solar energy forecasting.