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• W3147053664 abstract "This work proposes an intra-day forecasting model, which does not require to be trained or fed with real-time data measurements, for global horizontal irradiance (GHI) at a given location. The proposed model uses a series of time-dependant irradiance estimates near the target location as the main input. These estimates are derived from satellite images and are combined with other secondary inputs in an advanced neural network, which features convolutional and dense layers and is trained using a deep learning approach. For the various input combinations, the performance of the model is validated with a quantitative analysis on the forecast accuracy using different error metrics. Accuracies are compared with a commercial solution for irradiance forecasting made by the European Centre for Medium-Range Weather Forecasts (ECMWF) and publications with similar approaches and forecasting horizons, showing state-of-the-art performance even without irradiance measurements." @default.
• W3147053664 created "2021-04-13" @default.
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• W3147053664 date "2021-04-01" @default.
• W3147053664 modified "2023-10-14" @default.
• W3147053664 title "A deep learning model for intra-day forecasting of solar irradiance using satellite-based estimations in the vicinity of a PV power plant" @default.
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• W3147053664 doi "https://doi.org/10.1016/j.solener.2021.02.033" @default.
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