# 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
\begin{table}
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**Study** & **Approach** & **Forecasting Horizon** & **AI Technique** & **Data Inputs** & **Key Contributions** & **Limitations** \\ \hline [PERSON] et al. (2019) & Hybrid model for short-term PV forecasting & Short-term & SVM + GA & Meteorological data (temperature, irradiance), PV & RMSE improved by 669.624 W; & Computationally expensive due to the optimization process. \\ \hline [PERSON] and [PERSON] (2010) & Hidden model for predicting solar energy & Short-term & HMM & Meteorological data (temperature, humidity, irradiance) & Optimized smart system operation with time series & Limited generalization for complex systems with diverse configurations. \\ \hline [PERSON] et al. (2012) & ANN-based solar tracking system to estimate azimuth angles and optimize panel orientation & Short-term & ANN & Sun position data (azimuth angles), weather & Improved energy efficiency by optimizing solar & Limited to local weather data; requires \\ \hline [PERSON] et al. (2012) & Machine learning-based dual-axis solar program & Short-term & Naive Bayes algorithm & LDR sensor data, solar panel angles, environmental conditions & Significantly improved energy efficiency with automated orientation adjustments. \\ \hline [PERSON] et al. (2012) & Comparative analysis for renewable energy forecasting & Day-ahead & ANN, SVR, GPR & Weather data, solar temperature & Demonstrated superior accuracy of ANNs for solar energy forecasting. & Limited applicability to dual-axis tracking infrastructure. \\ \hline [PERSON] et al. (2012) & Multi-objective optimization of prediction intervals & Day-ahead & Neural Networks, MOPSO & Meteorological data (temperature, radiation, cloud cover) & Balanced interval width and reliability; adaptable to user needs via Pareto fronts; MOPSO \\ \hline [PERSON] et al. (2012) & Cascaded input/structure optimization for ANN-based solar energy prediction & Day-ahead & ANN with Optimization & Air temperature, humidity, zenith angle & Achieved prediction errors below 2\%, optimizing network structure for high accuracy. \\ \hline [PERSON] et al. (2012) & Probabilistic solar power forecasting & Multi-horizon & Bayesian BiLSTM + VAE & Historical PV output, meteorological data & Quantified uncertainties and prediction & Complexity in training Bayesian models and prediction & Complexity in training Bayesian models and prediction & Complexity in training Bayesian models and prediction \\ \hline \hline \end{tabular}
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Table 7: _Cont._These studies collectively highlight significant advancements in PV energy forecasting, focusing on improving accuracy, optimizing energy dispatch, and managing uncertainties. Integrating advanced AI techniques and hybrid models has enhanced the ability to handle weather-induced variability, temporal dependencies, and energy storage optimization. Short-term forecasting, in particular, benefits from neural networks and hybrid architectures such as LSTM, GANs, and ART-based models, which outperform traditional statistical methods. Long-term and day-ahead predictions are strengthened through model ensembles and feature selection techniques, improving robustness and generalizability. Despite these advancements, challenges such as computational complexity, reliance on high-quality data, and scalability persist. Energy dispatch optimization increasingly integrates forecasting with storage and demand-side elements (e.g., ESS, EVs), illustrating a shift toward more comprehensive energy management systems. However, practical constraints remain, especially for real-time applications.