• Energy Research

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In building construction, it is very important to reduce energy consumption and provide thermal comfort. In this regard, defects in insulating panels can compromise the capability of these panels of reducing the heat flow by conduction with the surroundings. In recent years, both experimental techniques and numerical methods have been used for investigating the effect of defects on the thermal behavior of building panels. The main novelty of this work regards the application of both numerical and experimental approaches based on infrared thermography techniques for studying the effects of defects such as debonding on the insulation properties of cork panels. In particular, the effects of defects were investigated by using the Long Pulse Thermography technique and then by analyzing the thermal behavior of the panel during the cooling phase. Results show the capability of the proposed approaches in describing the effects of defects in cork panels such as detachments and the benefit effect of a shield coating in improving the insulation properties of the panel.

Photovoltaic (PV) cells are a major part of solar power stations, and the inevitable faults of a cell affect its work efficiency and the safety of the power station. During manufacturing and service, it is necessary to carry out fault detection and classification. A convolutional-neural-network (CNN)-architecture-based PV cell fault classification method is proposed and trained on an infrared image data set. In order to overcome the problem of the original dataset’s scarcity, an offline data augmentation method is adopted to improve the generalization ability of the network. During the experiment, the effectiveness of the proposed model is evaluated by quantifying the obtained results with four deep learning models through evaluation indicators. The fault classification accuracy of the CNN model proposed here has been drawn by the experiment and reaches 97.42%, and it is superior to that of the models of AlexNet, VGG 16, ResNet 18 and existing models. In addition, the proposed model has faster calculation, prediction speed and the highest accuracy. This method can well-identify and classify PV cell faults and has high application potential in automatic fault identification and classification.

The present review describes the up-to-date state of the evaluation of thermophysical properties (TP) of materials with three different procedures: modeling (also including inverse problems), measurements and analytical methods (e.g., through computing from other properties). Methods to measure specific heat and thermal conductivity are described in detail. Thermal diffusivity and thermal effusivity are a combination of the previously cited properties, but also for these properties, specific measurement and calculation methods are reported. Experiments can be carried out in steady-state, transient, and pulse regimes. For modeling, special focus is given to the inverse methods and parameter estimation procedures, because through them it is possible to evaluate the thermophysical property, assuring the best practices and supplying the measurement uncertainty. It is also cited when the most common data processing algorithms are used, e.g., the Gauss–Newton and Levenberg–Marquardt least squares minimization algorithms, and how it is possible to retrieve values of TP from other data. Optimization criteria for designing the experiments are also mentioned.

To achieve net zero emissions in the building and construction sector, there is a growing interest in how buildings can be digitalized to improve energy efficiency through optimal operational strategies to reduce energy consumption during the operational phase. The validity of the savings scenarios is highly dependent on the accuracy of the digitalized building model. However, considering the accuracy of the developed model determines the validity of the energy-saving scenarios, creating accurate models is difficult owing to the limited amount of physical data collected from buildings. Hence, in this study, a hybrid modeling method is proposed to improve the prediction accuracy by integrating the physical model results and operational data to improve the prediction accuracy for actual operating buildings where only partial data collection is provided, mainly for air conditioners. The hybrid model predicts the next day's room temperature by learning the difference between the simulated room temperature based on the laws of physics and historical measurement data. The results showing that the coefficient of variance of root mean squared error (CVRMSE) was 1.5% for the training period, a significant improvement compared to the existing RC model; moreover, the R2 was 0.93 for the hybrid model, indicating high explanatory power. In addition, an average CVRMSE of 3.8% in the period outside the training area was obtained, resulting in a model with improved prediction accuracy compared with the existing RC model. Similar results were obtained for design models without calibration.

In the last years, the importance of integrating the production of electricity with the production of sanitary hot water led to the development of new solutions, i.e. PV/T systems. It is well known that hybrid photovoltaic-thermal systems, able to produce electricity and thermal energy at the same time with better energetic performance in comparison with two separate systems, present many advantages for application in a residential building. A PV/T is constituted generally by a common PV panel with a metallic pipe, in which fluid flows. Pipe accomplishes two roles: it absorbs the heat from the PV panel, thus increasing, or at least maintaining its efficiency; furthermore, it stores the heat for sanitary uses. In this work, the thermal and electrical efficiencies of a commercial PV/T panel have been evaluated during the summer season in different days, to assess the effect of environmental conditions on the system total efficiency. Moreover, infrared thermographic diagnosis in real time has been effected during the operating mode in two conditions: with cooling and without cooling; cooling was obtained by natural flowing water. This analysis gave information about the impact of a non-uniform temperature distribution on the thermal and electrical performance. Furthermore, measurements have been performed in two different operating modes: 1) production of solely electrical energy and 2) simultaneous production of thermal and electrical energy. Finally, total efficiency is largely increased by using a simple solar concentrator nearby the panel.

The addition of insulating layers on vertical walls of buildings is a common practice for providing a higher thermal insulation of the envelope. Workmanship defects, however, might influence the effectiveness of such insulation strategy. Damaged materials, incorrect installation, use of aged or weathered materials might alter the capability of reducing heat transfer through the envelope, whether vertical or sloped. In this work, drawbacks caused by the wrong installation of insulating material and by damaged material are assessed. A specimen wall was investigated by experimental and numerical approaches, the latter carried out by using COMSOL Multiphysics®. Results are compared and discussed.