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An Intelligent Weather Station

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 comf...

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Detalles Bibliográficos
Autores principales: Mestre, Gonçalo, Ruano, Antonio, Duarte, Helder, Silva, Sergio, Khosravani, Hamid, Pesteh, Shabnam, Ferreira, Pedro M., Horta, Ricardo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4721761/
https://www.ncbi.nlm.nih.gov/pubmed/26690433
http://dx.doi.org/10.3390/s151229841
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author Mestre, Gonçalo
Ruano, Antonio
Duarte, Helder
Silva, Sergio
Khosravani, Hamid
Pesteh, Shabnam
Ferreira, Pedro M.
Horta, Ricardo
author_facet Mestre, Gonçalo
Ruano, Antonio
Duarte, Helder
Silva, Sergio
Khosravani, Hamid
Pesteh, Shabnam
Ferreira, Pedro M.
Horta, Ricardo
author_sort Mestre, Gonçalo
collection PubMed
description 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.
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spelling pubmed-47217612016-01-26 An Intelligent Weather Station Mestre, Gonçalo Ruano, Antonio Duarte, Helder Silva, Sergio Khosravani, Hamid Pesteh, Shabnam Ferreira, Pedro M. Horta, Ricardo Sensors (Basel) Article 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. MDPI 2015-12-10 /pmc/articles/PMC4721761/ /pubmed/26690433 http://dx.doi.org/10.3390/s151229841 Text en © 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mestre, Gonçalo
Ruano, Antonio
Duarte, Helder
Silva, Sergio
Khosravani, Hamid
Pesteh, Shabnam
Ferreira, Pedro M.
Horta, Ricardo
An Intelligent Weather Station
title An Intelligent Weather Station
title_full An Intelligent Weather Station
title_fullStr An Intelligent Weather Station
title_full_unstemmed An Intelligent Weather Station
title_short An Intelligent Weather Station
title_sort intelligent weather station
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4721761/
https://www.ncbi.nlm.nih.gov/pubmed/26690433
http://dx.doi.org/10.3390/s151229841
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