{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T04:53:01Z","timestamp":1775191981657,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T00:00:00Z","timestamp":1700265600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Regional Development Fund of the European Union","award":["T2EDK-00864"],"award-info":[{"award-number":["T2EDK-00864"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Aiming at effectively improving photovoltaic (PV) park operation and the stability of the electricity grid, the current paper addresses the design and development of a novel system achieving the short-term irradiance forecasting for the PV park area, which is the key factor for controlling the variations in the PV power production. First, it introduces the Xception long short-term memory (XceptionLSTM) cell tailored for recurrent neural networks (RNN). Second, it presents the novel irradiance forecasting model that consists of a sequence-to-sequence image regression NNs in the form of a spatio-temporal encoder\u2013decoder including Xception layers in the spatial encoder, the novel XceptionLSTM in the temporal encoder and decoder and a multilayer perceptron in the spatial decoder. The proposed model achieves a forecast skill of 16.57% for a horizon of 5 min when compared to the persistence model. Moreover, the proposed model is designed for execution on edge computing devices and the real-time application of the inference on the Raspberry Pi 4 Model B 8 GB and the Raspberry Pi Zero 2W validates the results.<\/jats:p>","DOI":"10.3390\/info14110617","type":"journal-article","created":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T07:33:18Z","timestamp":1700292798000},"page":"617","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Neural Network-Based Solar Irradiance Forecast for Edge Computing Devices"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7660-2642","authenticated-orcid":false,"given":"Georgios","family":"Venitourakis","sequence":"first","affiliation":[{"name":"Electronics Lab, Physics Department, National & Kapodistrian University of Athens, 15772 Athens, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1470-0215","authenticated-orcid":false,"given":"Christoforos","family":"Vasilakis","sequence":"additional","affiliation":[{"name":"Electronics Lab, Physics Department, National & Kapodistrian University of Athens, 15772 Athens, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7124-1203","authenticated-orcid":false,"given":"Alexandros","family":"Tsagkaropoulos","sequence":"additional","affiliation":[{"name":"Electronics Lab, Physics Department, National & Kapodistrian University of Athens, 15772 Athens, Greece"}]},{"given":"Tzouma","family":"Amrou","sequence":"additional","affiliation":[{"name":"Inaccess Networks, 12 Sorou Str., 15125 Maroussi, Greece"}]},{"given":"Georgios","family":"Konstantoulakis","sequence":"additional","affiliation":[{"name":"Inaccess Networks, 12 Sorou Str., 15125 Maroussi, Greece"}]},{"given":"Panagiotis","family":"Golemis","sequence":"additional","affiliation":[{"name":"Inaccess Networks, 12 Sorou Str., 15125 Maroussi, Greece"}]},{"given":"Dionysios","family":"Reisis","sequence":"additional","affiliation":[{"name":"Electronics Lab, Physics Department, National & Kapodistrian University of Athens, 15772 Athens, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.patrec.2020.12.006","article-title":"Artificial intelligence for distributed smart systems","volume":"142","author":"Molinara","year":"2021","journal-title":"Pattern Recognit. 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