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This paper proposes a new method for an optimal control of the heating system at the building scale. This control is a new approach of energy planning that aims to decrease the heating consumption/expenses over a defined prediction horizon while respecting the occupants’ thermal comfort. It employs a simplified building thermal model to simulate the building thermal behavior taking into consideration the weather predictions. This approach is based on the application of Monte Carlo method, i.e., a random generator for the heating system scenarios. The aim is to determine the optimal heating plan for the prediction horizon that fulfills the constraints regarding the thermal comfort of occupants and the minimization of the energy consumption/expenses together with achieving a load shedding. [ABSTRACT FROM AUTHOR]

In many fields, such as in the agri-food industry or in the building industry, it is important to be able to monitor the thermophysical properties of granular materials. Regular thermal probes allow for the determination of one or several thermophysical factors. The success of the method used depends in part on the nature of the signal sent, on the type of physical model applied and eventually on the type of probe used and its implantation in the material. Although efficacious for most applications, regular thermal probes do present some limitations. It is the case, for example, when one has to know precisely the thermal contact resistance or the nature of the signal sent. In this article is presented a characterization method based on thermal impedance formalism. This method allows for the determination of the thermal conductivity, the thermal diffusivity, and the contact thermal resistance in one single test. The application of this method requires the use of a specific probe developed to enable measurement of heat flux and temperature at the interface of the probe and the studied material. Its practical application is presented for dry sand. [ABSTRACT FROM AUTHOR]

• This paper presents a methodology to assist building managers detect and identify operating anomalies. • The data generated consist of three consumption sectors (water, gas and electricity). • The methodology employed is based on combining data mining with machine learning, in the aim of creating a model that allows defining every day's status (atypical vs. frequent). • The expert flow manager receives an alert and analyzes the situation, with the possibility of implementing actions to correct the problem should an anomaly be detected. Building facility managers are increasingly equipping their buildings with extensive sets of sensors. This article aims to develop an analysis decision-making methodology based on the production of statistical indicators. The tracking of such indicators allows detecting any systems performance problems. The automatic pinpointing of malfunctions can serve to activate alerts. Our approach focuses on the processing of data stemming from secondary schools managed by departmental services in the Pas-de-Calais, where 117 secondary school buildings have been instrumented with various sensors and supplying data since 2015. This article starts with a close-up on data mining for water, gas and electricity consumption. Data mining and machine learning methods, including the Clustering approach (K-Means), have been used to extract information from the measurements conducted in 2015 and 2016. This information is used to classify the 2017 measurements according to supervised approaches (SVM). The specificity of this work is to delve deeper into the analysis by combining into the same algorithm a set of various sensors related to both energy use and building occupancy. The data classification results have allowed highlighting "atypical" operations during the daytime, through interpreting data classification results in an effort to define the status of every day in year 2017. [ABSTRACT FROM AUTHOR]

This study presents a NDT method using infrared thermography associated with a microwave excitation. The advantages of such stimulation lie in the volumic absorption of incoming waves which lead to a greater sounded depth. This method is applied to two types of samples. The first is a concrete slab reinforced with CFRP on which a bonding failure is inserted and the second is a wooden plate on which a metallic insert is placed on the back face. The device generating the microwaves is made of a commercial magnetron associated with a pyramidal horn antenna. An infrared camera is placed on the same side as the stimulated surface and thermograms are recorded at regular intervals. The whole assembly is placed in a protective room against high frequencies. The incident power density leads to heating of less than 1 °C of the surface of the samples. The thermograms show a higher temperature rise in front of the defect area. The non-uniformity of the beam, leads us to treat the thermograms with an algorithm of contrast. These first results show the interest of the microwave excitation to detect defects deeper than in the case of surface excitation. [ABSTRACT FROM AUTHOR]

Highlights: [•] In situ thermophysical properties characterization of a homogeneous wall by inverse method. [•] Original experimental device for active solicitation of the wall to characterize. [•] Injected heat flux measured by a fluxmeter used in the boundary condition of the numerical model. [•] Non-contact measurement of temperature evolution on the opposite surface by infrared thermography. [ABSTRACT FROM AUTHOR]

Thermal characterization of materials, especially civil engineering materials, in the way of non-destructive methods, are more and more widespread. In this article, we show an original point of view to describe the used method, the thermal waves, to obtain the thermal impedance of the studied system, using a specific sensor - a fluxmeter. The identification technique, based on a frequential approach, is optimized by applying a random input to the system. This kind of random heating is shown to provide a frequency range where the thermal effusivity is able to be identified and not correlated to another parameter. The strength of the method is also the determination of the contact resistance of the system, that allows to validate the identification process. Experimental results obtained from a sample with well-known thermal properties (polyvinyl chloride) are used to validate the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, the coupled heat and mass transfer within porous media has been studies. First, the studied materials have been characterized experimentally and than evaluated their thermal properties, namely thermal conductivity and specific heat in different states (dry-wet). The hygroscopic properties, namely water vapour permeability, water vapour sorption. At second time, we present and validate the mathematical model describing heat and mass transfer within bio-based materials, by the confrontation with the experimental results. The materials properties obtained from the characterisation part are used as model's input parameters. Moreover, a test facility is mounted in the laboratory in order to compare the numerical and experimental data. The founded results show a good concordance between the simulated and measured data. According to this results the mathematical model of Philip and de Vries gives a good prediction of hygrothermal behaviour of biobased material. This model will allow us to save money and time of the experimental part in the future. [ABSTRACT FROM AUTHOR]

This paper presents the results of the validation of an experimental method for studying the thermal benefits of green façades. The main aim is to evaluate the impact of plants occultation on energy performance of a vertical wall in the temperate climate of the city of Lille in northern France. An experimental platform consisting of three identical prototypes (three boxes as thermal labs) has been designed and instrumented. Each prototype has been highly insulated from each face except from the vertical southern wall which was realized with a poor thermal conductivity material. The following measurements were taken: ambient temperature (outside and inside thermal labs), external and internal surface temperatures of southern walls, heat fluxes transferred through south verticals walls (reference and vegetalized walls), solar incident radiation and wind velocity variation under instantaneous real climatic conditions. This enabled the analysis of the variation of energy balance according to the presence or not of a shading effect on the south vertical walls as well as their coverage rate. Several tests were performed on the three experimental thermal labs by imposing a leaf coverage rate which differs during each measurement. The results have shown that masking reduces the outside temperature and enhances the interior thermal comfort especially during summer. [ABSTRACT FROM AUTHOR]

This paper presents a NDT by a CO 2 Laser infrared thermography applied to defect detection in CFRP. The CO 2 Laser is an infrared laser with the wavelength of 10.6 μm. This excitation has a controllable heating beam by a geometric relation D = 0.01575· d , which allows to heat the samples at a specific position (placed at the distance “ d ”) and area (of a diameter “ D ”). The PPT interpretation principle was used to reduce the non-uniformity’s effect of the excitation causing inhomogeneous heat. The test with this excitation is much faster than the tests with conventional lock-in thermography method. [ABSTRACT FROM AUTHOR]

This paper presents a NDT by infrared thermography with a microwave excitation system. The advantages of such stimulation lie in the volumetric absorption of incoming waves which lead to a greater sounded depth. The device generating the microwaves was made of a commercial magnetron 800W at 2.45GHz associated with a pyramidal horn antenna. This method was applied to detect the steel reinforcements (deformed bar 12mm of diameter placing at every 10cm) in a concrete specimen (1m×1m×6.5cm) with a concrete cover of 3.8cm. The specimen was heated with an average power of 600W for 5min. An infrared camera was placed on the same side as the stimulated surface and thermograms were recorded at regular intervals. The whole assembly was placed in a protective enclosure against high frequency electromagnetic fields. The method based on a contrast algorithm was used to analyze the thermogram series. The result shows a higher temperature rise in front of the steel reinforcement areas. The effect of microwave caused an increase in moderate temperature does not lead to alteration of inspected concrete. [ABSTRACT FROM AUTHOR]

[Display omitted] To take advantage of the data generated in buildings, this document proposes a methodology based on a machine learning model to improve thermal comfort and energy efficiency. This methodology uses measured data (e.g., indoor/outdoor temperature, relative humidity, etc.) and forecast data (e.g., meteorological data) to train a multiple linear regression model to forecast the indoor temperature of the space under study. Using the genetic algorithm optimization method, this model is then used to evaluate the different heating strategies generated. For each strategy, a score is assigned according to user-defined criteria in order to prioritize them and select the best one. By studying an office building simulated under the TRNSYS software, a multiple linear regression model was implemented with errors less than 1% and an adjusted R2 coefficient close to 0.9. Compared to a conventional heating strategy, this methodology can improve thermal comfort by up to 43%. [ABSTRACT FROM AUTHOR]

In a period of surging energy prices, resource depletion, and concerns over the use of nuclear power, energy savings are paramount and a major component of ongoing sustainable development. Geothermal energy is the energy stored in the form of heat beneath the surface of the Earth. Related to this, the thermal properties of soils are of great importance, particularly with regard to the modern trends of utilizing the subsurface for transmission of either heated fluids or high power currents. For example, in geothermal hydrology or geotechnical engineering applications, the thermal conductivity must be determined to assess the energy potential of the soil. The presence of water (groundwater, rainfall, natural moisture) improves both the thermal conductivity and thermal capacity fields. We present an original method-based on a thermal study and the use of non-integer order models-to determine the thermophysical parameters of different soils in near-surface layers, and link them to the water content variations of different soils. The results are compared with those obtained using a capacitive profile probe. [ABSTRACT FROM AUTHOR]