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Publication
A neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperature
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Advisor(s)
Abstract(s)
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.
Description
Keywords
Temperature prediction Genetic algorithms Solar Cloudiness estimation Neural networks Sensor fusion Intelligent sensor
Citation
Ferreira, Pedro; Gomes, João; Martins, Igor; Ruano, Antóniio. A Neural Network Based Intelligent Predictive Sensor forCloudiness, Solar Radiation and Air Temperature, Sensors, 12, 11, 15750-15777, 2012.