{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T17:35:15Z","timestamp":1776274515337,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T00:00:00Z","timestamp":1709337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of SMEs and Startups (MSS, Korea)","award":["S3238625"],"award-info":[{"award-number":["S3238625"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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 determine feature variables related to SSI from the 16-channel data, the differences and ratios between the channels were utilized. Additionally, to consider the fundamental characteristics of SSI originating from the sun, solar geometry parameters, such as solar declination (SD), solar elevation angle (SEA), and extraterrestrial solar radiation (ESR), were used. Deep learning-based feature selection (Deep-FS) was employed to select appropriate feature variables that affect SSI from various feature variables extracted from the 16-channel data. Lastly, spatio-temporal deep learning models, such as convolutional neural network\u2013long short-term memory (CNN-LSTM) and CNN\u2013gated recurrent unit (CNN-GRU), which can simultaneously reflect temporal and spatial characteristics, were used to forecast SSI. Experiments conducted to verify the proposed method against conventional methods confirmed that the proposed method delivers superior SSI forecasting performance.<\/jats:p>","DOI":"10.3390\/rs16050888","type":"journal-article","created":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T10:11:57Z","timestamp":1709547117000},"page":"888","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Spatio-Temporal Deep Learning-Based Forecasting of Surface Solar Irradiance: Leveraging Satellite Data and Feature Selection"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0235-9872","authenticated-orcid":false,"given":"Jinyong","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2782-7775","authenticated-orcid":false,"given":"Eunkyeong","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9387-393X","authenticated-orcid":false,"given":"Seunghwan","family":"Jung","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9013-2079","authenticated-orcid":false,"given":"Minseok","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5077-6569","authenticated-orcid":false,"given":"Baekcheon","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4932-5458","authenticated-orcid":false,"given":"Sungshin","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"114216","DOI":"10.1016\/j.apenergy.2019.114216","article-title":"A hybrid deep learning model for short-term PV power forecasting","volume":"259","author":"Li","year":"2020","journal-title":"Appl. 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