A Neural Network Based Intelligent Predictive Sensor for Cloudiness, Solar Radiation and Air Temperature

Sensors

Pedro M. Ferreira¹  João M. Gomes²  Igor A. C. Martins² 

[1] Algarve Science and Technology Park, Campus de Gambelas, Pav. A5, 8005-139 Faro, Portugal;Department of Electronic and Informatics Engineering, University of Algarve, 8005-139, Faro, Portugal; E-Mails:

关键词: intelligent sensor;    sensor fusion;    neural networks;    cloudiness estimation;    solar radiation prediction;    temperature prediction;    genetic algorithms;   

DOI  :  10.3390/s121115750

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.