# 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
[PERSON] et al. [114] tackled this issue by integrating fuzzy logic preprocessing with ANN models to classify weather changes and introduced an error correction mechanism for 5 min short-term forecasts. [PERSON] et al. [115] further addressed weather-related challenges by combining GANs with CNNs to augment weather datasets and improve day-ahead predictions. Similarly, [PERSON] and [PERSON] [116] proposed a hybrid Radial Basis Function Neural Network (RBFNN) and Grey Theory model for day-ahead forecasts, enhancing forecasting precision by improving data regularity. [PERSON] et al. [117] advanced this area by introducing HCISEM-GAN, a real-time solar energy management system that combines GANs with Human-Computer Interaction to achieve a 20% improvement in prediction accuracy. In the context of energy dispatch and load management, [PERSON] et al. [118] proposed a DRNN-LSTM model for forecasting residential load and PV output in microgrids. This model enabled optimal energy dispatch by integrating PV forecasts with energy storage systems (ESS) and electric vehicles (EVs), reducing peak loads and operational costs by 8.97%. Similarly, [PERSON] et al. [119] utilized a Genetic algorithm-Support Vector Machine (GASVM) framework to optimize short-term PV power predictions, achieving significant reductions in RMSE and MAPE errors. Efforts to enhance solar panel performance have also contributed to the field. [PERSON] and [PERSON] [120] developed a Hidden Markov Model (HMM)-based approach for predicting solar energy generation using time series meteorological data. Their system optimized smart solar system performance by capturing the probabilistic correlation between past and future values, closely replicating actual power output. [PERSON] et al. [121] employed ANN models to estimate azimuth angles for solar tracking, optimizing panel orientation under various weather conditions. Additionally, [PERSON] et al. [122] developed a dual-axis solar tracker based on the Naive Bayes algorithm to predict optimal panel orientation, significantly increasing energy harvesting efficiency. Addressing uncertainties in PV energy forecasting has been another area of focus. [PERSON] et al. [123] conducted a comparative analysis of ANNs, Support Vector Regression (SVR), and Gaussian Process Regression (GPR), finding ANNs to be the most accurate model despite challenges in parameter optimization. [PERSON] et al. [124] proposed a multi-objective approach using neural networks combined with Multi-Objective Particle Swarm Optimization (MOPSO) to optimize prediction intervals, minimizing interval width while maximizing coverage probability. This method provided a reliable balance between prediction accuracy and uncertainty reduction, though it faced computational challenges due to the evolutionary optimization process. [PERSON] et al. [125] further improved ANN-based forecasting by optimizing input features and network structure, achieving prediction errors below 2%. [PERSON] et al. [126] proposed a VAE-Bayesian BiLSTM framework to quantify uncertainties and enhance reliability, demonstrating its potential for managing variability in energy predictions. Table 7 provides a comparative overview of these studies, highlighting their methodologies, forecasting horizons, data requirements, key contributions, and limitations. \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} p{56.9 pt}} \hline \hline
**Study** & **Approach** & **Forecasting Horizon** & **AI Technique** & **Data Inputs** & **Key Contributions** & **Limitations** \\ \hline [PERSON] et al.