{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T19:28:24Z","timestamp":1783625304988,"version":"3.55.0"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012]]},"abstract":"<jats:p>In recent years, estimating the power output of inherently intermittent and potentially distributed renewable energy sources has become a major scientific and societal concern. In this paper, we provide an algorithmic framework, along with an interactive web-based tool, to enable short-to-middle term forecasts of photovoltaic (PV) systems and wind generators output. Importantly, we propose a generic PV output estimation method, the backbone of which is a solar irradiance approximation model that incorporates free-to-use, readily available meteorological data coming from online weather stations. The model utilizes non-linear approximation components for turning cloud-coverage into radiation forecasts, such as an MLP neural network with one hidden layer. We present a thorough evaluation of the proposed techniques, and show that they can be successfully employed within a broad geographical region (the Mediterranean belt) and come with specific performance guarantees. Crucially, our methods do not rely on complex and expensive weather models and data, and our web-based tool can be of immediate use to the community as a simulation data acquisition platform.<\/jats:p>","DOI":"10.3233\/978-1-61499-098-7-981","type":"book-chapter","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:27:05Z","timestamp":1740133625000},"source":"Crossref","is-referenced-by-count":2,"title":["Predicting the Power Output of Distributed Renewable Energy Resources within a Broad Geographical Region"],"prefix":"10.3233","author":[{"family":"Panagopoulos Athanasios Aris","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Chalkiadakis Georgios","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Koutroulis Eftichios","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2012"],"original-title":[],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:33:25Z","timestamp":1740134005000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISSNISBN&issn=0922-6389&volume=242&spage=981"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-098-7-981","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2012]]}}}