ArticleData in brief2026
SolarMagix: An integrated multi-station photovoltaic and weather dataset for solar energy forecasting, fault detection, and anomaly analysis.
Article in Data in brief, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
6 authors.
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Abstract
This paper investigates the fusion of photovoltaic (PV) system performance data with weather data to create a robust dataset for energy production forecasting and anomaly detection. The study integrates data from multiple PV stations across various manufacturers and locations, addressing challenges in data quality, timestamp alignment, and inconsistent granularity. The resulting dataset underwent rigorous cleaning and aggregation, providing both aggregated and consolidated views, as well as raw telemetry data, for a wide variety of downstream use cases. The refined dataset includes 13,193 rows of station-level data and 88,285 rows of inverter-level data, reflecting energy production and weather parameters. The dataset also includes granular, unfiltered data from the photovoltaic stations, comprising > 1.2 million rows of combined telemetry data. The integration of energy production data with weather data provides a comprehensive foundation for future machine learning applications to improve the efficiency and reliability of PV systems. This dataset is a valuable contribution to the growing body of work focused on optimising solar energy generation through intelligent data integration.
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