Spatio-Temporal Deep Learning-Based Forecasting of Surface Solar Irradiance: Leveraging Satellite Data and Feature Selection

Spatio-Temporal Deep Learning-Based Forecasting of Surface Solar Irradiance: Leveraging Satellite Data and Feature Selection

This paper proposes a method for forecasting surface solar irradiance (SSI), the most critical factor in solar photovoltaic (PV) power generation. The proposed method uses 16-channel data obtained by the GEO-KOMPSAT-2A (GK2A) satellite of South Korea as the main data for SSI forecasting. To determin...

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Bibliographic Details
Main Authors: Jinyong Kim, Eunkyeong Kim, Seunghwan Jung, Minseok Kim, Baekcheon Kim, Sungshin Kim
Format: Article
Language:English
Published: MDPI AG 2024-03-01
Series:Remote Sensing
Subjects:
solar irradiance forecasting
deep learning-based feature selection
spatio-temporal deep learning model
solar geometry
GK2A satellite data
Online Access:https://www.mdpi.com/2072-4292/16/5/888

Internet

https://www.mdpi.com/2072-4292/16/5/888

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