Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.1/4815
Título: A neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperature. Sensors: Correction. 2012, 12, 15750–15777
Autor: Ferreira, P. M.
Gomes, João M.
Martins, I.
Ruano, A. E.
Palavras-chave: Temperature prediction
Genetic algorithms
Solar
Cloudiness estimation
Neural networks
Sensor fusion
Intelligent sensor
Data: 2013
Citação: Ferreira, Pedro; Gomes, João; Martins, Igor; Ruano, António. Correction: Ferreira, P.M., et al. A Neural Network Based Intelligent Predictive Sensor for Cloudiness, Solar Radiation and Air Temperature. Sensors 2012, 12, 15750–15777, Sensors, 13, 7, 9547-9548, 2013.
Resumo: Accurate measurements of global solar radiation and atmospheric temperature, 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 and portable sensor was developed, using artificial neural network models as the time-series predictor mechanisms. These have been identified with the aid of a procedure based on the multi-objective genetic algorithm. As cloudiness is the most significant factor affecting the solar radiation reaching a particular location on the Earth surface, it has great impact on the performance of predictive solar radiation models for that location. This work also represents one step towards the improvement of such models by using ground-to-sky hemispherical colour digital images as a means to estimate cloudiness by the fraction of visible sky corresponding to clouds and to clear sky. The implementation of predictive models in the prototype has been validated and the system is able to function reliably, providing measurements and four-hour forecasts of cloudiness, solar radiation and air temperature.
Peer review: yes
URI: http://hdl.handle.net/10400.1/4815
DOI: http://dx.doi.org/ 10.3390/s130709547
ISSN: 1424-8220
Versão do Editor: http://www.mdpi.com/1424-8220/13/7/9547
Aparece nas colecções:FCT2-Artigos (em revistas ou actas indexadas)

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