# Efficient Machine Learning-Based Prediction of Solar Irradiance Using Multi-Site Data
## 2 Proposed Solution
### Proposed Framework  
The data processing and modeling workflow for multi-site solar irradiance prediction is illustrated in Figure 2. It integrates a unified preprocessing pipeline, time-lagged feature engineering, and model training strategy across diverse PV stations. Below is the detailed sequence of steps:  
* Data collection from multiple PV sites: Solar irradiance and environmental data were collected for six PV stations over three years using the MIDC Raw Data API. For each site, the raw files were parsed into structured DataFrames with consistent timestamp formatting (30 min resolution). * Data preprocessing pipeline: The preprocessing consisted of the following clearly defined steps: \begin{table}
\begin{tabular}{l l} \hline \hline
**Station ID (Link: https://midcdmz. & Station Name \\ nrel.gov/apps/data\_api\_doc.pl? & \\ \_idtextlist (accessed on 23 July 2025) & \\ \hline RCS & ARM Radiometer Characterization System (RCS) \\ BS & Bluefield State College \\ HSU & Cal Poly Humboldt Solar Radiation Monitoring System (SoRMS) \\ EC & Elizabeth City State University \\ LRSS & Lowry Range Solar Station (RSR) \\ AODSRRLBC & NREL Solar Radiation Research Laboratory (AOD SR-CEeS L2) \\ AODSRRLOS & NREL Solar Radiation Research Laboratory (AOD SkyRad L0) \\ AODSRRL1S & NREL Solar Radiation Research Laboratory (AOD SkyRad L1.1) \\ SS1 & Sun Spot One—San Luis Valley (RSR) \\ USVIBOVB & US Virgin Islands Bovoni 2 \\ USVIIOLNA & US Virgin Islands Longford \\ UFL & University of Florida \\ ULL & University of Louisiana at Lafayette \\ UNLV & University of Nevada—Las Vegas \\ PWVUO & University of Oregon (GPS-based PWV) \\ UOSMRL & University of Oregon (SRML) \\ UTPASRL & University of Texas Rio Grande Valley Solar Radiation Lab \\ PVSUOSMP & UofO PV Resource (Samples) \\ PVSUO & UofO PV Resource \\ XECS & Xcel Energy Comanche Station (RSR) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A set of possible station IDs and names. * Synchronization and alignment: All station data were synchronized based on UTC timestamps. Any mismatched timestamps or duplicated entries were dropped. Feature names were standardized (e.g., GHI, DHI, DNI) across stations to ensure schema consistency. This means that columns were renamed and standardized (e.g., \"Global Horizontal\" \(\sim\) \"GHI\"). Units were converted where necessary to ensure consistent W/m\({}^{2}\) scales. * Concatenation: After alignment, datasets from the six stations were concatenated into a unified dataset. Each row retained its station identifier, enabling station-specific modeling and evaluation while allowing for global analysis. * Data cleaning:
* Features with more than 30% missing values were dropped. * Remaining missing values were imputed using forward-fill interpolation. * Outliers were identified using the IQR method and removed per feature. * Resampling and time lagging: Each DataFrame was resampled to a specific interval. Then, a windowing function was applied to generate time-lagged features. Specifically, lagged versions of GHI, DHI, DNI, and other relevant features were created for \(t-1\), \(t-2\), \(\ldots\), \(t-n\) intervals (e.g., past 30, 60, 90 min) to support prediction at \(t+1\) or \(t+2\). * Feature selection: A two-step feature selection strategy was adopted:
* Kendall correlation coefficients were calculated between all feature pairs, excluding the target variable. To mitigate multicollinearity, one feature from each highly correlated pair (correlation coefficient > 0.95) was removed. Furthermore, Kendall correlation coefficients were also computed between each feature and the target variable (GHI). Features exhibiting a correlation coefficient below 0.3 with the target were discarded. This selection process ensured the retention of the most relevant and informative predictors. * Feature importance scores were computed using an ensemble of tree-based models. The importance scores were averaged across models, and the top 15 most influential features were retained for each station.