{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T08:45:47Z","timestamp":1769762747225,"version":"3.49.0"},"reference-count":68,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2012,11,12]],"date-time":"2012-11-12T00:00:00Z","timestamp":1352678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate measurements of global solar radiation and atmospheric temperature,as well as the availability of the predictions of their evolution over time, are importantfor different areas of applications, such as agriculture, renewable energy and energymanagement, or thermal comfort in buildings. For this reason, an intelligent, light-weightand portable sensor was developed, using artificial neural network models as the time-seriespredictor mechanisms. These have been identified with the aid of a procedure based on themulti-objective genetic algorithm. As cloudiness is the most significant factor affecting thesolar radiation reaching a particular location on the Earth surface, it has great impact on theperformance of predictive solar radiation models for that location. This work also representsone step towards the improvement of such models by using ground-to-sky hemisphericalcolour digital images as a means to estimate cloudiness by the fraction of visible skycorresponding to clouds and to clear sky. The implementation of predictive models inthe prototype has been validated and the system is able to function reliably, providingmeasurements and four-hour forecasts of cloudiness, solar radiation and air temperature.<\/jats:p>","DOI":"10.3390\/s121115750","type":"journal-article","created":{"date-parts":[[2012,11,12]],"date-time":"2012-11-12T11:37:24Z","timestamp":1352720244000},"page":"15750-15777","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["A Neural Network Based Intelligent Predictive Sensor for Cloudiness, Solar Radiation and Air Temperature"],"prefix":"10.3390","volume":"12","author":[{"given":"Pedro M.","family":"Ferreira","sequence":"first","affiliation":[{"name":"Algarve Science and Technology Park, Campus de Gambelas, Pav. A5, 8005-139 Faro, Portugal"},{"name":"Centre for Intelligent Systems, IDMEC-IST, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal"}]},{"given":"Jo\u00e3o M.","family":"Gomes","sequence":"additional","affiliation":[{"name":"Department of Electronic and Informatics Engineering, University of Algarve, 8005-139, Faro, Portugal"}]},{"given":"Igor A. C.","family":"Martins","sequence":"additional","affiliation":[{"name":"Department of Electronic and Informatics Engineering, University of Algarve, 8005-139, Faro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6308-8666","authenticated-orcid":false,"given":"Ant\u00f3nio E.","family":"Ruano","sequence":"additional","affiliation":[{"name":"Centre for Intelligent Systems, IDMEC-IST, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal"},{"name":"Department of Electronic and Informatics Engineering, University of Algarve, 8005-139, Faro, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2012,11,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ferreira, P.M., Ruano, A.E., Silva, S., and Concei\u00e7\u00e3o, E. (2012). Neural networks based predictive control for thermal comfort and energy savings in public buildings. Energy Build.","DOI":"10.1016\/j.enbuild.2012.08.002"},{"key":"ref_2","unstructured":"Ferreira, P.M., Silva, S., and Ruano, A.E. (, January April). Model Based Predictive Control of HVAC Systems for Human Thermal Comfort and Energy Consumption Minimisation. W\u00fcrzburg, Germany."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ferreira, P.M., Silva, S., and Ruano, A.E. (2012, January 10\u201315). Energy Savings in HVAC Systems Using Discrete Model-Based Predictive Control. 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