A neural-network based intelligent weather station

Author

Ruano, A.E. ; Mestre, G. ; Duarte, H. ; Silva, S. ; Pesteh, S. ; Khosravani, H. ; Ferreira, P.M. ; Horta, R.

Author_Institution

Univ. of Algarve, Faro, Portugal

fYear

2015

fDate

15-17 May 2015

Firstpage

1

Lastpage

6

Abstract

Accurate measurements of global solar radiation and 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 algorithm and artificial neural network 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 three atmospheric variables, over a prediction horizon of 48-steps-ahead.

Keywords

agriculture; atmospheric humidity; atmospheric techniques; atmospheric temperature; neural nets; renewable energy sources; solar radiation; time series; agriculture; artificial neural network model; atmospheric temperature measurement; building; energy management; forecasting performance; global solar radiation measurement; nearest-neighbor algorithm; neural-network based intelligent weather station; portable sensor; relative humidity measurement; renewable energy; thermal comfort; time-series predictor mechanism; Artificial neural networks; Atmospheric measurements; Economic indicators; Humidity; Solar radiation; Temperature measurement; MOGA design; neural networks; prediction; self-powered device; weather station; wireless communications;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Signal Processing (WISP), 2015 IEEE 9th International Symposium on

Conference_Location

Siena

Type

conf

DOI

10.1109/WISP.2015.7139169

Filename

7139169