Machine Learning-Based Power Forecasting for Outdoor Sun-Tracking PV Systems Under Variable Sky Conditions in Semi-Arid Regions
收藏Zenodo2026-03-23 更新2026-05-26 收录
下载链接：
https://zenodo.org/doi/10.5281/zenodo.19186109
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资源简介：
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 data2. Processed datasets used for model training3. Input/output data for machine learning models4. Forecast results for LR, RF, and SVM models5. 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.
提供机构：
Zenodo
创建时间：
2026-03-23
