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A neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperature
Pedro M Ferreira1, João M Gomes, Igor A C Martins
1Algarve Science and Technology Park, Campus de Gambelas, Faro, Portugal. pmf@fc.ul.pt
Sensors (Basel, Switzerland)
|December 4, 2012
Summary
A new portable sensor accurately measures solar radiation and temperature, predicting changes using artificial neural networks. It improves solar radiation models by estimating cloudiness from sky images, enhancing renewable energy and building management.
Area of Science:
- Environmental Science
- Artificial Intelligence
- Sensor Technology
Background:
- Accurate solar radiation and atmospheric temperature data are crucial for agriculture, renewable energy, and building thermal comfort.
- Predicting these variables is essential for effective energy management and resource planning.
- Cloudiness significantly impacts solar radiation, posing a challenge for predictive modeling.
Purpose of the Study:
- To develop an intelligent, light-weight, and portable sensor for measuring and predicting solar radiation and atmospheric temperature.
- To improve the accuracy of solar radiation prediction models by incorporating cloudiness estimation.
- To validate the performance and reliability of the developed sensor system.
Main Methods:
- Utilized artificial neural network models for time-series prediction of solar radiation and temperature.
- Employed a multi-objective genetic algorithm for identifying and optimizing the predictive models.
- Developed a method to estimate cloudiness using ground-to-sky hemispherical color digital images to determine the fraction of visible sky covered by clouds and clear sky.
Main Results:
- The developed sensor system reliably provides accurate measurements of global solar radiation and atmospheric temperature.
- The system successfully forecasts cloudiness, solar radiation, and air temperature for up to four hours ahead.
- The integration of cloudiness estimation from sky images demonstrated an improvement in predictive model performance.
Conclusions:
- The intelligent, portable sensor system offers a reliable solution for real-time environmental monitoring and forecasting.
- The novel approach to cloudiness estimation enhances the accuracy of solar radiation prediction models.
- This technology has significant implications for optimizing renewable energy systems, agricultural practices, and building energy management.
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