title: An Intelligent Weather Station.
abstract: Accurate measurements of global solar radiation, atmospheric temperature and relative humidity, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight, self-powered and portable sensor was developed, using a nearest-neighbors (NEN) algorithm and artificial neural network (ANN) models as the time-series predictor mechanisms. The hardware and software design of the implemented prototype are described, as well as the forecasting performance related to the three atmospheric variables, using both approaches, over a prediction horizon of 48-steps-ahead.
authors.affiliation: EasySensing-Intelligent Systems, Centro Empresarial de Gambelas, Pav A5, Campus de Gambelas, University of Algarve, 8005-139 Faro, Portugal. goncalomestre@easysensing.pt.
authors.forename: Gonçalo
authors.initials: G
authors.lastname: Mestre
authors.affiliation: Faculty of Science and Technology, Campus de Gambelas, University of Algarve, 8005-139 Faro, Portugal. aruano@ualg.pt.|Centre for Intelligent Systems, IDMEC, Instituto Superior Técnico, 1049-001 Lisboa, Portugal. aruano@ualg.pt.
authors.forename: Antonio
authors.initials: A
authors.lastname: Ruano
authors.affiliation: Faculty of Science and Technology, Campus de Gambelas, University of Algarve, 8005-139 Faro, Portugal. hsduarte@ualg.pt.
authors.forename: Helder
authors.initials: H
authors.lastname: Duarte
authors.affiliation: EasySensing-Intelligent Systems, Centro Empresarial de Gambelas, Pav A5, Campus de Gambelas, University of Algarve, 8005-139 Faro, Portugal. sergiosilva@easysensing.pt.
authors.forename: Sergio
authors.initials: S
authors.lastname: Silva
authors.affiliation: Faculty of Science and Technology, Campus de Gambelas, University of Algarve, 8005-139 Faro, Portugal. hkhosravani@csi.fct.ualg.pt.
authors.forename: Hamid
authors.initials: H
authors.lastname: Khosravani
authors.affiliation: Faculty of Science and Technology, Campus de Gambelas, University of Algarve, 8005-139 Faro, Portugal. spesteh@csi.fct.ualg.pt.
authors.forename: Shabnam
authors.initials: S
authors.lastname: Pesteh
authors.affiliation: LaSIGE, Faculdade de Ciências, Universidade de Lisboa, Portugal. pmf@ciencias.ulisboa.pt.
authors.forename: Pedro M
authors.initials: PM
authors.lastname: Ferreira
authors.affiliation: Rolear SA, 8001-906 Faro, Portugal. ricardohorta@rolear.pt.
authors.forename: Ricardo
authors.initials: R
authors.lastname: Horta
keywords: MOGA design; neural networks; prediction; self-powered device; weather station; wireless communications