## 6 Conclusions  
In conclusion, achieving highly accurate active power predictions requires expert knowledge in the solar energy domain and sophisticated data science techniques. While module sensor data (e.g., irradiance and temperature) often deliver more precise models with lower errors, these measurements are not always available in every region. Consequently, relying on local weather parameters, as demonstrated in a case study obtained from open meteo APIs, can be an effective alternative, providing coverage for a wider array of locations where on-site sensor data may be lacking or impractical to collect.  
Figure 14: Information gain and the frequency of involvement for each weather parameter.  
Figure 13: An example of a decision tree estimator within a trained random forest model.  
This work demonstrates that short-term PV power can be accurately forecasted with high fidelity and interpretability, without the need for exhaustive on-site instrumentation. By coupling a three-stage feature-selection pipeline (variance filtering, hierarchical correlation clustering, and a genetic algorithm wrapper) with complementary importance metrics (permutation scores and information gain), we reduced the original 27 weather variables to an eight-feature core dominated by irradiance and apparent temperature. The trimmed input space preserved a mean R\({}^{2}\cong\) 0.91 on unseen 15 min data from a 1 MW array in Canakkale, Turkishye, while halving model training time. Beyond raw accuracy, the framework exposes the parameters behind PV output: global-tilted, direct, and diffuse shortwave irradiance emerged as the principal drivers, whereas wind speed, direction, and most humidity metrics exhibited marginal or negative informational value. These insights provide plant operators with a data-backed rationale for prioritizing irradiance sensors over costly wind or aerosol instrumentation and refining inverter-level controls during periods of rapid shortwave variability.  
Our results confirm that transparent feature reduction and explainable AI co-exist with state-of-the-art PV forecast accuracy, enlightening the way for scalable, sensor-agnostic deployment in emerging solar grids.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app15148005/s1](https://www.mdpi.com/article/10.3390/app15148005/s1)]([https://www.mdpi.com/article/10.3390/app15148005/s1](https://www.mdpi.com/article/10.3390/app15148005/s1)), apw.xlsx excel and *ipynb files.  
Conceptualization, S.N. and V.E.; methodology, S.N. and V.E.; validation, S.N. and V.E.; formal analysis, S.N.; investigation, V.E.; resources, V.E.; data curation, V.E.; writing--original draft, S.N. and V.E.; visualization, S.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding. The APC was funded by Istanbul Topkapi University.  
Not applicable.  
The utilized dataset and code are publicly available at [[https://github.com/sajjad-nematzadeh/Photovoltaic](https://github.com/sajjad-nematzadeh/Photovoltaic)]([https://github.com/sajjad-nematzadeh/Photovoltaic](https://github.com/sajjad-nematzadeh/Photovoltaic)). Accessed on 3 July 2025.  
We would like to thank [PERSON] and [PERSON] for their guidance and support with their suggestions throughout the study.  
The authors declare no conflict of interest.