# Efficient Machine Learning-Based Prediction of Solar Irradiance Using Multi-Site Data
## 4 Conclusions  
This study used a three-year dataset of solar irradiance data collected from six distinct photovoltaic stations. The primary objective was to evaluate the feasibility of developing a consensus among various machine learning models regarding optimal shared features for predicting GHI across multiple sites. To achieve this, a 30-min shift in data was utilized based on a thorough analysis of ACF and PACF, which indicated a strong correlation between each GHI value and its value from 30 min before. The analysis identified that the DNI, DHI, as well as the temperature of the solar panels and related components (primarily CR variants), are critical features for predicting GHI Subsequently, predictions of GHI were made using data from the six stations, primarily employing tree-based machine learning algorithms and ensemble learners. The results demonstrated that the \(R^{2}\) values exceeded 95% at all six stations and reached 99% for the NWTC station. The RAZON station was an exception, where the metric did not exceed 90%. Specifically, GBR was found to be powerful for NELHA, NWTC, and RAZON, while the MLP performed well for both ULL and UNLV. Finally, CatBoost was the best performer for HSU. The effect of time-shifting values was also evaluated, revealing that with larger shifts, the results deteriorated. MLP emerged as the best model for larger shifting values. It was found that the common lagged GHI values are the most impactful features for predicting the target, along with other features such as solar panel parameters (especially those linked to temperature and ambient conditions), zenith and azimuth angles, and the components of GHI, such as DNI and DHI. These findings underscore the effectiveness of different machine learning models tailored to each station's specific data characteristics. Although we experimented with a unified stacking ensemble to explore potential gains in cross-site generalizability, the observed performance variability between stations reveals a more fundamental insight: local site characteristics, such as sensor calibration, geographic features, and weather dynamics, heavily influence forecasting accuracy. Thus, our findings reaffirm that site-specific models remain more effective under current data constraints and that achieving universal generalization remains a challenging and open research problem. Nevertheless, the stacking strategy still holds promise as a flexible integration framework, especially if combined with domain adaptation techniques in future work.  
Conceptualization, H.N.N., Z.A., O.S., and K.C.; methodology, H.N.N. and Z.A.; software, H.N.N. and Z.A.; validation, H.N.N., Z.A., O.S., and K.C.; formal analysis, H.N.N., Z.A., O.S., and K.C.; writing--original draft preparation, H.N.N. and Z.A.; writing--review and editing, O.S. and K.C.; visualization, Z.A.; supervision, H.N.N. and K.C.; project administration, H.N.N. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by funds from the Bourgogne-Franche-Comte Region (AAP Region 2022 dispositif ANER-IA Limentaire project) and the EIPHI Graduate School (contract \"ANR-17-EURE-0002\").  
Figure 20: Overview of shift-degree impact on GHI and related features.  
Data Availability Statement: The data presented in this study are openly available in MIDC RAW DATA API.  
**Conflicts of Interest:** Dr. [PERSON] was employed by the company \"DeepVu, USA\". The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.