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Research Project
Computational intelligence methods in time-series analysis and forecasting with application to energy management systems
Funder
Authors
Publications
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
Publication . Ferreira, P. M.; Gomes, João M.; Martins, I.; Ruano, Antonio
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.
A neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperature
Publication . Ferreira, P. M.; Gomes, João M.; Martins, I.; Ruano, Antonio
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.
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Description
Keywords
Contributors
Funders
Funding agency
European Commission
Funding programme
FP7
Funding Award Number
239451