Intelligent use of energy in public buildings

Funder

Organizational Unit

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.

2013Journal article

Open Access

Neural models project for solar radiation and atmospheric temperature forecast

Publication . Martins, I.; Ruano, A. E.; Ferreira, P. M.

This work arises from the necessity of temperature and solar radiation forecast, to improve the Heating, Ventilating, and Air Conditioning (HVAC) systems e ciency. To do so, it was necessary to determine neural models capable of such forecast. The chosen characteristics were solar radiation and temperature because these two characteristics directly a ect the room temperature inside a building. This forecast system will be implemented on a portable computational device, so it must be built with low computational complexity. During this dissertation the various research phases are described with some detail. The applications were developed on Python programming language due to it library collection. In this task several algorithms were developed to determine the cloudiness index. The results of these algorithms were compared with the results obtained using neural models for the same purpose. In solar radiation and temperature forecast only neural models were used. The cloudiness index forecast was not implemented as this is only an intermediate step; instead measured values of cloudiness index were used for the solar radiation forecast. Regarding the solar radiation forecast two neural models were implemented and compared, one of the models has an exogenous input, the cloudiness index forecast, and the other one is simply a time series. This models were compared to determine if the inclusion of the cloudiness index forecast improves solar radiation forecast. In temperature forecast only one model will be presented, a Nonlinear AutoRegressive with eXogenous input (NARX) model, with solar radiation forecast as exogenous input. All the neural models are radial Basis Function (RBF) and there structure was determined using a Multi-Objective Genetic Algorithm (MOGA). The models were used to determine cloudiness index, forecast solar radiation and temperature.

2009Master thesis

Open Access

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.

2012Journal article

Open Access

A neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperature

Publication . Ferreira, Pedro M.; Gomes, João; Martins, Igor A. C.; 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.

2012-11Journal article

Open Access