{"id":188842,"date":"2024-06-21T14:15:54","date_gmt":"2024-06-21T12:15:54","guid":{"rendered":"https:\/\/www.rse-web.it\/pubblicazioni\/machine-learning-techniques-for-solar-irradiation-nowcasting-cloud-type-classification-forecast-through-satellite-data-and-imagery\/"},"modified":"2024-07-02T14:24:42","modified_gmt":"2024-07-02T12:24:42","slug":"machine-learning-techniques-for-solar-irradiation-nowcasting-cloud-type-classification-forecast-through-satellite-data-and-imagery","status":"publish","type":"pubblicazioni","link":"https:\/\/www.rse-web.it\/en\/publications\/machine-learning-techniques-for-solar-irradiation-nowcasting-cloud-type-classification-forecast-through-satellite-data-and-imagery\/","title":{"rendered":"Machine Learning techniques for solar irradiation nowcasting: Cloud type classification forecast through satellite data and imagery"},"content":{"rendered":"<p class=\"last-updated-date\">Recently updated on July 2nd, 2024 at 02:24 pm<\/p>","protected":false},"excerpt":{"rendered":"<p>The article presents an algorithm for identifying clouds obstructing direct sunlight towards a target and calculates the clearness index for the next fifteen minutes using machine learning, leveraging satellite data and meteorological information..<\/p>\n","protected":false},"author":93,"featured_media":0,"comment_status":"open","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"tags":[1328],"targets":[1314,1317],"pubblicazioni_tipologie":[773],"class_list":["post-188842","pubblicazioni","type-pubblicazioni","status-publish","hentry","tag-photovoltaics","targets-press-media-en","targets-research","pubblicazioni_tipologie-isi-article-en"],"acf":{"dont_show_hompage":true,"projects":{"ID":188408,"post_author":"93","post_date":"2024-06-13 15:10:18","post_date_gmt":"2024-06-13 13:10:18","post_content":"","post_title":"Integration of renewable energy into the land and environment","post_excerpt":"The goal of the project is to develop methodologies and analyses that, starting from the characterization of technologies and with an integrated approach, provide support for energy planning, taking into account technical, economic, environmental and regulatory aspects and possible hybridization and integration.","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"integration-of-renewable-energy-into-the-land-and-environment","to_ping":"","pinged":"","post_modified":"2024-07-12 11:06:31","post_modified_gmt":"2024-07-12 09:06:31","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.rse-web.it\/progetti\/energy-from-renewable-sources-and-integration-in-the-territory\/","menu_order":0,"post_type":"progetti","post_mime_type":"","comment_count":"0","filter":"raw"},"order_posts":"","dont_show_search":false,"related_posts":false,"show_on_slider":false,"single_post_data":{"titolo_spot":"","post_content":"<p>One of the most significant challenges in making renewable production competitive is developing new tools to manage unpredictability, avoid economic losses, ensure compliance with grid constraints, and enhance congestion management. Solar energy exhibits continuous temporal and spatial variations due in part to astronomical factors and partly influenced by meteorological conditions. Ground-level solar fluctuations exert a profound influence on the output power of photovoltaic systems, which can fluctuate significantly over short intervals. This study introduces a new model for real-time identification of clouds obstructing direct sunlight towards a specific geographic target. Additionally, a novel methodology for predicting clear sky index over the target in fifteen-minute intervals is proposed, leveraging Machine Learning techniques with satellite and meteorological data.<\/p>\n","scarica_file":false,"link_estreno":[{"link_text":"Download Publication","link":"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0306261921011600"}],"button":{"text":"","link":""},"referente_group":false,"data_emissione":"2022-01-01","autori":"A. Nespoli, A. Niccolai, E. Ogliari (Politecnico di Milano), G. Perego (Bluefondation, Monticello), E. Collino, D. Ronzio (RSE S.p.A.)","destinazione":"Applied Energy, Vol. 305, No. 117834","rif_rse":"21012635"},"satellite_post_url":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Machine Learning techniques for solar irradiation nowcasting: Cloud type classification forecast through satellite data and imagery - RSE<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.rse-web.it\/en\/publications\/machine-learning-techniques-for-solar-irradiation-nowcasting-cloud-type-classification-forecast-through-satellite-data-and-imagery\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Machine Learning techniques for solar irradiation nowcasting: Cloud type classification forecast through satellite data and imagery - 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