## 1 Introduction
### Problem Statement and Research Objectives  
PV power forecasting is crucial for power networks to include solar energy, increase energy efficiency, and ensure grid stability. However, the availability of redundant and plentiful meteorological data poses a significant challenge to PV power forecasting, which could negatively impact forecast precision and computational efficiency. Temperature, humidity, solar radiation, air pressure, and many other interrelated climatic characteristics can introduce noise, raise processing costs, and complicate the interpretation of models. Furthermore, redundant data may lead to model overfitting, a phenomenon in which predictive algorithms find patterns that do not translate well to fresh inputs. Additionally, PV forecasting models often require extensive preprocessing to filter out irrelevant variables while retaining the most relevant predictors. The inclusion of unnecessary meteorological variables increases computational time without necessarily improving accuracy.  
This study aims to investigate the impact of weather parameters on PV power estimation by employing feature selection and ranking methods.  
With feature selection, irrelevant or ineffective input data are eliminated without compromising the model's prediction accuracy. This process is useful for determining the fewest features that provide the best performance. Therefore, efficiency can be increased, and interpretability can be achieved easily. Separating unnecessary features from the data with feature selection simplifies the model and reduces the risk of overfitting. Finding each feature's degree of influence in predicting the target variable is the main goal of feature ranking. By elucidating the relative significance of various inputs, ranking techniques offer insights into the reasons behind the model's specific behavior. Because it identifies the factors that influence decisions, this level of explanation is particularly helpful in avoiding training problems, such as overfitting or bias. Ultimately, these two strategies provide more reliable and intelligible models by facilitating the feature set while maintaining the most useful predictors.  
PV power forecasting and removing useless data can increase forecast accuracy, reduce computational complexity, and improve energy management. Predictive models become more effective by eliminating unnecessary climate factors, which enhances the integration of solar power into power grids. This optimization promotes the efficient use of renewable resources, reduces energy waste, and increases grid stability. Therefore, improved PV forecasting supports a more resilient and environmentally friendly power system by promoting efficiency, reducing dependence on fossil fuels, and enabling a cleaner energy transition, all of which contribute to the larger goal of sustainable energy.  
The main contributions of this study are as follows:  
* We propose a three-stage feature selection pipeline combining variance filtering, hierarchical correlation clustering, and a meta-heuristic wrapper, specifically tailored for photovoltaic power forecasting.
* We perform a dual-perspective feature-importance analysis using tree-based permutation scores and information-theory metrics, providing a robust and explainable understanding of the key weather parameters.
* We use areal meteorological data instead of module-embedded sensors, enabling generalizable and sensor-free forecasting solutions suitable for locations without detailed instrumentation.
* We identify that irradiance-related variables dominate predictive power, while many commonly used variables (e.g., humidity, wind speed, wind direction) contribute minimally or even negatively, which is critical for model simplification and deployment.
* We release a high-resolution, real-world dataset (covering 15 min intervals over two years) and provide open access to the data for reproducibility and further research.