1. An Hybrid Solar Façade Model: Preliminary Assessment of the Adequacy of Neural Networks
Luís Filipe Martinsª, Fernando Netoª,b. 
ªDepartment of Mechanical Engineering, University of Aveiro
b TEMA, Centro de Tecnologia Mecânica e Automação, University of Aveiro
Abstract
European legislation outlines a set of very ambitious objectives for the 2020 horizon concerning general energy efficiency and consumption in the buildings sector, which represents roughly 40%) of final energy consumption. The legislative goals are aimed at higher energy efficiency levels in buildings and at the promotion of the use of renewable energies. Modelling and simulation are often required for preliminary assessment of performance of proposed renewable energy capturing devices. An evaluation of modelling and simulation strategies for an existing solar façade used for air and water heating and ventilation purposes was conducted. Several modelling techniques were studied including an approach using Artificial Intelligence (AI), more specifically Artificial Neural Networks (ANNs). Because of their usage growth and good results attained on Solar Thermal Energy Systems (STES) modelling, good results are expected from the use of this technique.
Introduction
The abundant energy provided by the sun can be selectively used by façades by reflecting, absorbing or reusing this energy. New solar façades are being developed with several purposes including heating, ventilation, thermal isolation, shading, electricity generation and lighting of buildings [1].
The development of a solar thermal façade is a costly and complicated process so that preliminary modeling and simulation are essential to anticipate performance and enhance economic results from façade usage. The creation of a prototype and the monitoring of its behavior along time can help improve the knowledge about the apparatus. In addition, the data obtained from this monitoring can be used for the creation of an artificial intelligence (AI) model based on an artificial neural network (ANN). The ANN approach is faster when compared to conventional techniques, robust in noisy environments and can solve a wide range of problems. Hence, ANNs have been widely used in real-time applications [2]. Results obtained from the application of an ANN can sometimes be even more precise than those obtained from numerical models as ANN use real-life system performance resulting in a closer to reality model.
Modelling methods
Modelling techniques are commonly used to describe the dynamic behavior of a system. A physical/mathematical model of the system introduced in a simulation environment can help to understand the behavior of a system. Some of the works developed so far on solar thermal collectors used a heat network model approach to calculate heat and mass transport [3]. The collector is divided into subparts in which a uniform temperature distribution is assumed. The same authors used an alternative modelling technique: the Hottel-Vhillier (H-V) model was employed to calculate the temperature distribution along the length of the collector.
According to the suggested technique three physical models should be created for the hybrid façade, each one characterizing one work mode (air heating, water heating and ventilation). One advantage of the physical model approach is the fact that the system characteristics must be known beforehand in order to create the model, but no working data is needed since the model will provide data about the working behavior of the façade.
Figures 1 and 2 show the results for the H-V simulation of an air and water collector, respectively.
ANN models may be used as an alternative method in engineering analysis and prediction. ANNs operate like a “black box”, requiring no detailed information about the system. Instead, they learn relationships between input parameters and the controlled and uncontrolled variables by analyzing previously recorded data, in a similar mode as a non-linear regression. Another advantage of using ANNs is their capability to handle large and complex systems with many interrelated parameters. They seem to simply ignore excess data that are of minimal significance and focus instead on the important inputs [4].
The above mentioned authors [3] used an ANN for modelling an air and water collector. From a minimal set of data, the proper ANN training and the choice of the adequate neural network architecture resulted in a close correspondence between the measured and calculated values for the outlet temperatures as shown in figure 3 (air collector) and 4 (water collector).
As opposed to physical modelling, only one ANN model is needed to replicate the three modes in which the façade works as the network can identify in which mode the façade is working and respond accordingly.
Conclusions
Artificial neural networks are a powerful tool that can be successfully used to characterize a solar thermal façade behavior if an appropriate network architecture is chosen. This modelling technique requires actual operating data and is sensitive to the size of the training data set, the number of network layers, number of hidden neurons, network activation functions and training algorithms. However, once these issues are overcome, the ANN approach has a series of advantages over physical models, namely a conceptual simplicity, the improved calculation speed and its ability to learn from collected data. Furthermore, the accuracy of an ANN is very high, in some cases better than the accuracy obtained with physical models as ANN. From this point of view, an ANN seems to be an adequate tool to model the operation of a multipurpose solar façade.
References