# Efficient Machine Learning-Based Prediction of Solar Irradiance Using Multi-Site Data
## 1 Introduction
### Motivation, Contributions, and Organization  
Predicting solar irradiance is crucial for optimizing solar power generation, ensuring grid stability, and supporting energy forecasting and trading. Accurate irradiance forecasts enhance climate and weather modeling, aiding in climate research and agricultural planning. They also play a vital role in environmental monitoring, air quality management, and assessing renewable resources. Moreover, reliable solar irradiance predictions inform energy policies, investment decisions, and the development of advanced solar technologies. Leveraging machine learning for this purpose promises improved accuracy and efficiency, thereby maximizing the benefits of solar energy and contributing to a sustainable future. In this work, we enhance our latest solar irradiance prediction scheme, developed in [4], in terms of generalization. We use several stations of PV panels and create time-lagged versions of the data, meaning that a new dataset is generated by considering windows of historical feature values and forecasting the next GHI value and time \(T_{Current}+\delta_{T}\), where \(\delta_{T}\) is a step into the future. Our approach involves analyzing data from multiple solar panel sites in diverse locations. This allows us to provide a detailed analysis of the features \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Contribution** & **Models Used** & **Result** \\ \hline \multirow{3}{*}{[17]} & Develop a real-time solar irradiance forecasting & Cloud tracking techniques, implanted fisheye network & The techniques outperformed existing forecasting models at two \\  & technique & camera, ANN algorithm & locations. \\ \hline \multirow{3}{*}{[18]} & Review the use of ANNs in & & ANN usage increases accuracy; \\  & solar power generation & ANN & hybrid systems and instrument calibration improve accuracy. \\  & forecasting and analyze & & \\  & measurement instruments & & \\ \hline \multirow{3}{*}{[19]} & Forecast hourly GHI using & Extreme gradient boosting & The proposed model outperformed \\  & ensemble model & forest, deep neural network, & standalone ML and DL models in \\  & & ridge regression & stability and accuracy. \\ \hline \multirow{3}{*}{[20]} & Compare ML and DL methods & SVR, Random Forest, & DL algorithms were more accurate \\  & for predicting solar irradiance & polynomial regression, ANN, & but required more computational \\  & & CNN, RNN & power than ML algorithms. \\ \hline \multirow{3}{*}{[21]} & Predict daily solar irradiance & Feed Forward Neural Nets & ANN model outperformed \\  & (FFNNs), empirical models, & Holt–Winters, RSM, and empirical \\  & Holt–Winters, RSM & models. \\ \hline \multirow{3}{*}{[22]} & Model solar radiation using & Least square support vector & \\  & module, multi-verse optimizer & The proposed technique \\  & robust soft computing method & algorithm, genetic algorithm, & outperformed others in terms of \\  & & gray wolf optimization, sine & accuracy. \\  & & cosine algorithms & \\ \hline \multirow{3}{*}{[23]} & Develop hourly day-ahead & & The proposed approach \\  & solar irradiance forecasting & LSTM+RNN, FFNN & outperformed FFNN; simulation \\  & model & & showed 2\% increase in annual \\ \hline \multirow{3}{*}{[24]} & Predict solar irradiance using & PSO-XGBoost, PSO-LSTM, & \\  & hybrid models and & PSO-GBRT, ANN, CNN, LSTM, & PSO-LSTM showed superiority for \\  & probabilistic forecasts & RF, GBRT, XGBoost & day-ahead solar prediction. \\ \hline \multirow{3}{*}{Current} & Predict GHI for the next \(\delta\) & & \\  & temporal step with 6 different & \\  & stations at once, different & Tree-based algorithms, MLP, & \\  & characteristics, and different & ensemble learners & \\  & lagged versions & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of solar irradiance forecasting studies. that improve prediction accuracy for each site. Subsequently, we establish a consensus on the most critical features for a generalized machine learning model.