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Sensors 2015, 15(12), 31005-31022; doi:10.3390/s151229841

An Intelligent Weather Station

1
EasySensing—Intelligent Systems, Centro Empresarial de Gambelas, Pav A5, Campus de Gambelas, University of Algarve, 8005-139 Faro, Portugal
2
Faculty of Science and Technology, Campus de Gambelas, University of Algarve, 8005-139 Faro, Portugal
3
Centre for Intelligent Systems, IDMEC, Instituto Superior Técnico, 1049-001 Lisboa, Portugal
4
LaSIGE, Faculdade de Ciências, Universidade de Lisboa, Portugal
5
Rolear SA, 8001-906 Faro, Portugal
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Received: 20 October 2015 / Revised: 20 November 2015 / Accepted: 25 November 2015 / Published: 10 December 2015
View Full-Text   |   Download PDF [5666 KB, uploaded 10 December 2015]   |  

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. View Full-Text
Keywords: weather station; self-powered device; neural networks; prediction; wireless communications; MOGA design weather station; self-powered device; neural networks; prediction; wireless communications; MOGA design
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Mestre, G.; Ruano, A.; Duarte, H.; Silva, S.; Khosravani, H.; Pesteh, S.; Ferreira, P.M.; Horta, R. An Intelligent Weather Station. Sensors 2015, 15, 31005-31022.

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