Machine Learning-Based Power Forecasting for Outdoor Sun-Tracking PV Systems Under Variable Sky Conditions in Semi-Arid Regions

This dataset contains raw and processed data used for photovoltaic (PV) power forecasting using machine learning models (Linear Regression, Random Forest, and Support Vector Machine).

Location:
URAER, Ghardaïa, Algeria (Semi-arid climate)

Contents:
1. Raw meteorological and PV system data
2. Processed datasets used for model training
3. Input/output data for machine learning models
4. Forecast results for LR, RF, and SVM models
5. Data used to generate all figures and statistical results

Variables:
- GHI: Global Horizontal Irradiance (W/m²)
- Temperature: Ambient temperature (°C)
- PV Power: Output power (W)

Usage:
The dataset allows full replication of the results presented in the associated research article.

Author:
Zaghba Layachi et al.