This study proposes a deep learning framework for estimating global horizontal irradiance (GHI) from SEVIRI satellite data, incorporating feature importance analysis and using Heliosat-4 as a reference benchmark. Building on prior work, we refine temporal alignment between satellite inputs and ground truth by aggregating 1-minute BSRN GHI measurements to 15-minute and hourly intervals. A multilayer perceptron (MLP), trained on 12 SEVIRI channels and Mc- Clear clear-sky GHI, is evaluated using leave-one-location-out cross-validation (LOLO-CV) across 16 diverse stations. Results consistently show improved accuracy over Heliosat-4 at both temporal scales. To enhance model interpretability and reduce computational complexity, we apply SHAP (SHapley Additive exPlanations) analysis to assess channel relevance. A reducedinput model that uses only the most informative channels maintains comparable performance, demonstrating the potential for efficient and scalable deployment. These findings support the feasibility of data-driven, interpretable, and resource-efficient GHI estimation systems for operational and forecasting applications.

Solar Irradiance Estimation from SEVIRI: A Feature Importance and Channel Selection Analysis / Gallo, Raimondo; Castangia, Marco; Macii, Alberto; Aliberti, Alessandro; Patti, Edoardo. - ELETTRONICO. - (In corso di stampa). (Intervento presentato al convegno 25th EEEIC International Conference on Environment and Electrical Engineering (EEEIC) tenutosi a Chania, Crete nel 15-18 July, 2025).

Solar Irradiance Estimation from SEVIRI: A Feature Importance and Channel Selection Analysis

Raimondo Gallo;Marco Castangia;Alberto Macii;Alessandro Aliberti;Edoardo Patti
In corso di stampa

Abstract

This study proposes a deep learning framework for estimating global horizontal irradiance (GHI) from SEVIRI satellite data, incorporating feature importance analysis and using Heliosat-4 as a reference benchmark. Building on prior work, we refine temporal alignment between satellite inputs and ground truth by aggregating 1-minute BSRN GHI measurements to 15-minute and hourly intervals. A multilayer perceptron (MLP), trained on 12 SEVIRI channels and Mc- Clear clear-sky GHI, is evaluated using leave-one-location-out cross-validation (LOLO-CV) across 16 diverse stations. Results consistently show improved accuracy over Heliosat-4 at both temporal scales. To enhance model interpretability and reduce computational complexity, we apply SHAP (SHapley Additive exPlanations) analysis to assess channel relevance. A reducedinput model that uses only the most informative channels maintains comparable performance, demonstrating the potential for efficient and scalable deployment. These findings support the feasibility of data-driven, interpretable, and resource-efficient GHI estimation systems for operational and forecasting applications.
In corso di stampa
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/3002719
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