ArticleiScience2026
Microclimate prediction for sandy photovoltaic power plants using a spatio-temporal graph convolutional network with environmental covariates.
Article in iScience, 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.
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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
8 authors.
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Abstract
The deployment of photovoltaic (PV) power plants in sandy regions alters the local microclimate. Accurate microclimate prediction is essential for optimizing short-term operations and maintenance plans. Compared with traditional statistical and numerical models, neural networks are suited to capture the complex nonlinear dependencies in sandy-region PV environments. Graph neural networks (GNNs) extend neural networks to process data defined on graph domains, enabling them to confront high-dimensional feature redundancy, attenuated temporal dependencies, and pronounced spatial heterogeneity. Accordingly, we propose a spatio-temporal graph convolutional network with environmental covariates (EC-STGCN) for station-level microclimate prediction at meteorological stations. Experimental results demonstrate that EC-STGCN achieves accurate high-resolution spatio-temporal predictions of temperature, relative humidity (RH), and particulate matter (PM
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.