Thermal Behaviour Of Buildings Research Articles

Neural network-based energy signatures for non-intrusive energy audit of buildings: Methodological approach and a real-world application

Energy Signatures (ES) are highly informative gray-box regression models. Thanks to their simplicity and interpretability, to their data-driven approach, and to their effectiveness in describing a building response to external weather variables, they are employed for (i) the determination of the Balance Point (BP) temperature of a building, (ii) the ranking of the efficiency of heating or cooling systems, (iii) the provision of building diagnostic information, and (iv) the development of strategies for more energy-efficient buildings and the estimation of potential savings. In this work, we propose an innovative energy audit tool, based on a Feed-Forward Neural Network (NN) to determine ES from aggregated (meter-level) electric load profiles of buildings. Multiple NN-based regression models are defined for each building and compared to provide the most accurate and informative one, by considering proper fit and significance indexes. This allows the eventual existence of multiple cooling regimes to be detected. Moreover, the energy audit methodology defines and applies an innovative Key Performance Indicator (KPI), called Temperature Unstandardized Beta Weight (βTemp∗), to account not only for the thermal behavior of buildings but also for the efficiency of the conditioning system and the internal heat generation. This ES approach has been applied to a dataset of electric consumption patterns from about eighty industrial buildings from a telecommunication (TLC) service provider in Italy. The useful outputs from the proposed methodology, together with its simplicity, effectiveness and applicability, are intended to support the diffused understanding of the thermal behavior of buildings and the analysis of their inefficiencies, in order to enhance energy retrofit actions and reduce consumption.

Cumulative Multi-Day Effect of Ambient Temperature on Thermal Behaviour of Buildings with Different Thermal Masses

In most studies, the effect of the thermal capacity of the building envelope on changes in internal temperatures is reduced to a 24 h period. During this period, daytime heat gains are balanced by nighttime heat losses. The maximum indoor temperature, the diurnal variation of the indoor temperature and the time lag between the occurrence of the maximum daily temperature determine the effect achieved. The aim of the article was to show that the effect of the thermal capacity of a building on the indoor temperature is not limited to 24 h but accumulates over a period of several days, mainly depending on the temperature and solar radiation history of the previous days. As a result, contrary to what some studies have suggested, the bedrooms of heavier buildings remained significantly colder at night during periods of prolonged high outdoor temperatures. The results obtained may fundamentally influence the perception of the effect of using the high thermal capacity of the building envelope to reduce high indoor temperatures in hot weather.

Transient Behavior Analysis of the Infiltration Heat Recovery of Exterior Building Walls

This research study investigated the transient behavior of the convection–diffusion model for the infiltration heat recovery (IHR) and the influence of the building envelope heat capacity, along with other factors. A transient numerical model was developed and validated to analyze the IHR under various conditions. The results highlight the role of heat capacity, thermal conductivity, wall thickness, airflow rate, airflow direction, and wall porosity on the temperature distribution and the heat recovery factor within the wall. Higher-heat-capacity walls displayed a delayed temperature rise, while low-thermal-conductivity walls reduced the conduction heat transfer and increased the IHR factor. The impact of heat capacity diminished with very low thermal conductivity walls but became evident for high-thermal-conductivity walls, particularly at higher Peclet numbers. Thicker walls enhanced the heat retention and improved the IHR, with a reduced influence of airflow rate. Higher IHR factors were associated with thicker walls, lower Peclet numbers, and higher heat capacities. The analysis also showed that the wall porosity affected the IHR with less significance than the other factors. Incorporating these findings into building energy modeling tools could improve the prediction accuracy of the thermal behavior of buildings. Accordingly, this study contributes to building physics by understanding IHR dynamics and thermal mass interactions, as well as improving building energy modeling accuracy for performance prediction. Future research can explore the impacts of additional factors on IHR and investigate the effect of IHR on the overall energy consumption of buildings.

Smart home energy management with mitigation of power profile uncertainties and model errors

The paper proposes a two-stage home energy management system accounting for optimal energy dispatch, power profiles predictability, and building thermal behaviour. The first look-ahead stage aims at minimizing the energy exchanges with the upstream grid based on load and weather predictions. The second step adapts the controls in real-time in order to mitigate forecast errors while remaining as close as possible to the look-ahead commitments. The originality of the paper lies on the use of different granularities of the building models. Especially, this allows to account for the approximations in the system equations embedded in the predictive controllers. Furthermore, a real-time tuning of those model equations is performed based on the deviations observed on the state variables – errors between controller predictions and measurements. Ultimately the proposed approach allows to cope with two generic drawback of traditional model predictive control: i) environment forecast errors and ii) models approximations. Several simulations are carried out to under different weather conditions, demonstrating an average energy exchange reduction of at least 25% and better system predictability. The work is completed with sensitivities analysis of the controller settings, the system parameters and the quality of weather/load forecasts.

Effect of a Material Based on Date Palm Fibers on the Thermal Behavior of a Residential Building in the Atlantic Climate of Morocco

The potential of natural materials is becoming more and more important as concerns about the environmental impact and energy efficiency of the construction sector grow. Incorporating waste from fiber sub-products seems like a wise choice, in line with the circular economy model. Despite the fact that date palm materials have been extensively researched and developed for use in modern buildings, the potential of using date palm fibers has not been widely explored. This research intends to examine how date palm fibers thermal insulation affects a building’s thermal efficiency in an Atlantic climate. An analysis using a numerical simulation using the TRNSYS software is conducted to determine the effect of this passive approach on cooling/heating loads and indoor comfort. This technique is measured against a hypothetical reference case of homemade traditional building materials without thermal insulation. The results show that insulation with date palm fiber materials has a significant effect on the indoor air temperature and the cooling and heating loads of the house. In comparison to the reference case, the studied house achieves better comfort conditions when thermal insulation is adopted since the indoor air temperature is increased by up to 3 °C in winter and decreased by up to 5 °C in summer. In addition, annual cooling and heating requirements can be reduced by 25% and 18%, respectively, by insulating the roof and walls with date palm fiber materials. On the other hand, it allows financial savings and a reduction in CO2 emissions.

Development of a white-box dynamic building thermal model integrated with a heating system

Development of a simplified building model can effectively mimic the transient building thermal behaviour which essentially depends on the weather profile and the building architecture under various operational strategies and occupant behaviour. This work presents the mathematical solution procedure based on the white-box physical building thermal model using resistance and capacitance network (i.e. lumped capacitance). The work takes into account the dynamic behaviour of radiator, and its heat flow adjustment via a thermostatic radiator valve, the solar heat gain (function of the time and location), the infiltration heat loss together with building model having multi-zone with multi-layer wall structure. The case study reveals that the integrated behaviour of the building components under a given control strategy paves the way for simulating different control strategies when large-scale district heating systems are considered.

A comprehensive study on thermal reinforcement of Saudi Arabia buildings considering CO2 emissions and capital cost using machine learning

Saudi Arabia can be called a country with a desert and hot climate. Buildings consume more than 80% of the total electricity in this country. About 66% of the electricity in residential houses is wasted for cooling, which consequently emitted a significant CO2. In this study, using computational fluid dynamic methods, the thermal behavior of buildings equipped with insulation was compared with conventional buildings in Saudi Arabia to determine how much adding insulation increases the capital cost and how much diminishes CO2 emissions. For eight important areas (average temperature of 19–31 °C), insulation with a thickness of 1 to 10 cm was added to the envelopes in different positions, and the best installation location was explored in different indoor temperature scenarios using machine learning. There is a trade-off between the capital cost and operational CO2 emissions as any insulation thickness rises, increases the former, and reduces the latter parameters. Using artificial neural network and Genetic algorithm, it was found that if the least emission of CO2 is required, it is recommended to install insulation with a thickness of 10 cm in the outer layers, provided that the inside temperature is regulated at 27 °C. For Abha, the inside temperature should be set at 22–23 °C. The economic results revealed that each dollar investment in envelopes thermal reinforcement could diminish CO2 emissions by up to 29.9 kg. Regardless of the economic issue, adding insulation by 10 cm led to CO2 reduction by 66 kgm2.year

Numerical and environmental analysis of post constructive application of PCM coatings for the improvement of the energy performance of building structures

In recent years, the post-constructive application of phase changing materials (PCMs) as coating to improve the thermal behavior of buildings, is gaining more ground. The question that arises concerns the required properties that these materials should have under different environmental conditions, in order to achieve optimal energy savings. Another issue concerns the overall evaluation of PCMs, considering sustainability assessment practices, a fact which is often neglected in PCM studies. This work investigates the post-constructive use of different PCM coatings under diverse environmental conditions, as well as for different structural elements. In the context of this work, a validated finite elements numerical model is utilized, as well as Life Cycle Assessment practices, for the holistic determination of the optimal application conditions of PCM coatings. The work examines the application of PCM coatings in 8 different structural elements, for diverse meteorological conditions in Europe, for all major buildings’ orientations. Life Cycle Assessment is also employed to determine the overall environmental performance of the investigated structural elements. In certain cases, the study revealed that PCM implementation can contribute to energy savings, varying from 0.24 up to 29,84 [kWh/m2a] and energy payback periods from 0.81 to 19.40 years. The purpose of this study is to provide informed answers with regard to the post-constructive application of PCM in existing structures for the energy upgrade of buildings.

Experimental evaluation of composite concrete incorporated with thermal energy storage material for improved thermal behavior of buildings

This study shows the effect of integrating eutectic Phase Change Material (PCM) in concrete on thermal, physical, and mechanical properties. Lauric acid and Myristic acid were used to prepare Eutectic PCM. Eutectic PCM was mixed in Zeolite to form Shape Stabilized Composite Phase Change Material (SSCPCM). SSCPCM-39% was found to be most stable and prevents the leakage of the eutectic PCM. SSCPCM-39% was chemical, physically and morphologically stable using Fourier Transform Infrared Spectroscopy (FTIR), X-rays Diffraction (XRD), and Scanning Electron Microscope (SEM). Additionally, SSCPCM-39% possess excellent thermal stability and thermal energy storage capacity which was investigated using Thermo Gravimetric Analysis (TGA) and Differential Scanning Calorimetry (DSC). SSCPCM was then integrated in concrete in varying percentage to form Thermal Energy Storage Concrete slab namely TES-C0, TES-C1, TES-C2, TES-C3, and TES-C4. These slabs were then tested in real outdoor environment for thermal behaviour. TES-C4 shows best performance in regulating indoor temperature showing maximum reduction of 14.51% in peak temperature and maximum time delay of 71 min in comparison to TES-C0. Compressive strength of 42 kN, 39 kN, 33 kN, and 29 kN was shown by TES-C1, TES-C2, TES-C3, and TES-C4 respectively after 28 days of hydration process.

Nano technology building insulation effect on the thermal behavior of building under different Algerian weather

The energy consumption of the building sector in Algeria represents the largest part of the energy produced in the country. Action to reduce the impact of this sector on the economy and the environment must be taken. The use of thermal insulation to reduce the energy needs of the building sector represents an effective and sustainable solution. The objective of our work is to conduct a study using the TRNSYS 17 software on the influence of the use of thermal insulation in different climates in Algeria. A typical apartment is chosen to run the calculations under three different climates of Algeria. The simulation results obtained for the city of Annaba show that the use of rock wool, cork, expanded polystyrene and airgel insulation allows a reduction of 42.81%, 43.08%, 43.31% and 50.90% respectively. While for the city of Setif the reduction obtained is 34.15%, 34.70%, 35.13% and 52.25% respectively for the same insulation. For the city of Biskra the reduction is 32.07%, 32.28%, 32.45% and 38.72% respectively for the same insulation. The improvement of the thermal performance of the structure and the glazed surfaces according to the Passivhaus standard allows a significant reduction of heating and air conditioning needs estimated at 72, 78.21 and 65.21% respectively for the cities of Annaba, Setif and Biskra. A low energy consumption building is obtained for the city of Annaba and Setif.

Data-driven simulation for energy and local comfort optimization: Case study of a laboratory

Recent control strategies for heating, ventilation and air conditioning (HVAC) in buildings consider ambient conditions, spatial temperature distribution and occupancy density to maximize occupant comfort at minimum energy. Simulators capturing the energy and thermal behaviour of buildings with HVAC systems provide a convenient platform for developing and testing such control strategies. Commonly used simulators are limited to zonal temperature simulation and are unable to model spatial distribution of temperature within the zones. Thus, these simulators cannot be used for control development that uses finer spatial temperature measurements. This paper proposes a simulation framework that emulates the HVAC behavior and energy consumption as well as temperature distribution in actual buildings. Data-driven techniques are used to facilitate model development and calibration. Since the simulator generates spatial thermal distribution, it is used to devise and test air conditioner (AC) set-point control policy that optimizes local comfort as opposed to the usual zonal comfort. The control thus designed is applied to optimize the AC setpoint schedules in a live academic setting and is shown to induce more energy savings than the control based on zonal comfort optimization.

Thermal simulation trained deep neural networks for fast and accurate prediction of thermal distribution and heat losses of building structures

• Thermal simulation of building structures was trained by deep learning method. • Thermal behavior of building was predicted by deep neural network in a fast time. • Thermal distribution image was trained by convolutional encoding–decoding network. • Heat loss of building was trained by multi-layer perceptron. In this study, state-of-the art deep neural networks to train and predict the heat transfer in building structures were proposed. Today, many of studies analyze thermal energy performance of buildings by analytical or numerical methods. Although building energy performance can be predicted effectively by finite element method, it is still time-consuming to calculate and solve the heat transfer problem. Moreover, expert engineer is required and complicate process to set simulation model is essential. In this work, a novel deep-learning method, which was pre-trained by the thermal simulation data, was developed to predict the thermal behavior of building structures in a fast time without complicated process. Heat transfer simulations of the slab wall building structure depending on its thermal properties and geometries were carried out to get training datasets for deep learning. The database of thermal simulation results was used for deep learning training. The image of temperature and heat flow distribution was trained by convolutional encoding–decoding network and the value of total heat loss through building and thermal bridge coefficient was trained by multi-layer perceptron. After train completed, the thermal behavior could be predicted in a second by just feeding information such as blueprint image and thermal properties of constructions into deep-learning architecture. There was no need to set a new simulation model at each time which consumes time and effort for modeling, meshing and calculating. With the developed network, the prediction of thermal behavior with high accuracy was possible in a super-fast time.

Numerical derivation and validation of the angular, hemispherical and normal emissivity and reflectivity of common window glass

The thermal radiative properties of glazing materials are fundamental inputs for evaluating the energetic performance and thermal behaviour of buildings. These properties are required for many practical applications ranging from infrared thermography to building performance simulation and thermal comfort evaluations. Soda–lime–silica glass is the most widely used glass, worldwide, for glazing and the derivation of its thermal and optical properties are the subject of this work. Based on experimentally acquired spectral reference measurement data for soda-lime glass, the complex-valued Fresnel equations are solved and Monte-Carlo integration is carried out over the black-body spectrum, as well as over the hemisphere. These calculations are performed for various radiative temperatures to establish their temperature dependencies, and additionally the temperature-dependent transmittance of soda-lime glass was analysed. By applying a non-linear optimisation algorithm, computationally efficient practical fitting formulae for the angular reflectivity, emissivity and absorptivity are derived. All equations are numerically solved with a high degree of accuracy. The determination of the hemispherical total emissivity for this type of glass confirms the current accepted value (∼0.2%) originally derived by Rubin (1985). A simple yet robust experimental design, based on thermography, is used to validate the results of the numerical reflectivity model. Additionally, we provide angular functions, fitting functions and additional constants that were hitherto unavailable. The practical benefits of the fitting formulae provided are considerable in relation to modelling the reflection of thermal radiation on glazed surfaces, and are demonstrated in two case-study applications in the appendix.

Accuracy of Simplified Modelling Assumptions on External and Internal Driving Forces in the Building Energy Performance Simulation

The recently issued EN ISO 52016-1 technical standard provides a new simplified dynamic method for the building energy performance assessment. Since an extensive validation of the EN ISO 52016-1 hourly method is still missing, the present work investigates the effect of the main modelling assumptions—related to the heat balance on the outdoor and the indoor envelope surfaces—on the building thermal behaviour. The model validation was carried out by assessing the accuracy variation consequent to the application of the EN ISO 52016-1 modelling assumptions to a detailed dynamic calculation tool (EnergyPlus). To guarantee a general validity of the outcomes, two buildings, two levels of thermal insulation, and two Italian climatic zones were considered, for a total of eight case studies. To explore different applications of the standard method, the analysis was performed both under a free-floating condition—to evaluate the accuracy of the model in predicting the indoor operative temperatures—and to assess the annual energy needs for space heating and cooling. Results show that the assumptions related to the definition of the external convective and the shortwave (solar) radiation heat transfer lead to non-negligible inaccuracies in the EN ISO 52016-1 hourly model.

A review of airflow rate estimation techniques for natural ventilation in buildings

As natural ventilation involves local and global interactions, the estimation of these interactions can be performed by many approaches. Such approaches, rather more experimental and numerical than analytical, often require a great deal of instrumentation and equipment, which results in higher demands on project budget and funding. The present work is devoted to comprehending the natural ventilation concept, and to assess the existing experimental techniques already implemented for past researchers in the estimation of the ventilation airflow rate due to the wind and thermal buoyancy effects. A brief review of modeling techniques is also presented. This will provide a strong theoretical grasp of the natural ventilation process as part of the main elements in the thermal behavior of buildings. Ultimately, these bases are intended to help choose the most suitable techniques to estimate the natural ventilation airflow rate. The adequate benefit-to-budget technique appears to be the airtightness tests (blower door tests), since empirical Equations relating the airflow directly to the pressure difference in the building for both cases: infiltrations (openings closed) and openings opened, can be obtained. Also, the location of the leakages can be identified without complications, and this technique has the potential to estimate in situ the airflow capacity and friction characteristics of the openings.

Automatic modelling of buildings and thermal substations for large district heating systems

Fast prediction of thermal behaviour of buildings and substation connected to district heating systems is an important aspect for implementing technique aimed at reducing primary energy consumption and consequently pollutant emission, such as demand side management, network expansion, renewable sources penetration, integration of waste heat. In this work, an approach to model the end-users connected to a district heating network (each building and each substation) is proposed. A physical model is used, which is calibrated through a proper approach using experimental data. The main strength of the work is that only data that are usually measured in district heating substations (temperature and mass flow rates in the substation) and building volume are required to obtain the model. This makes the model creation and its use automatic and applicable to a wide range of district heating systems also in case other data are not available. This characteristic is essential to make the model suitable for large networks including various thousands of substations. Results show that the model allows simulating the effects of modifications in district heating system operations in terms of building thermal request and temperature variation with an average errors lower than 5% for all the measures analysed.

Empirical validation of a multizone building model coupled with an air flow network under complex realistic situations

Building energy simulation (BES) assesses the energetic and thermal behaviour of buildings and can be used for the evaluation of energy conservation measures (ECMs). However, building modelling under realistic conditions can be challenging for simulation programs. The aim of this work is to test whether a multizone building model developed with a simulation tool can handle the complex situations of an in-use building. Monitoring data from the Twin Houses empirical validation experiment of the International Energy Agency, Energy in Buildings and Communities (IEA EBC) Annex 71 is used for that purpose. The multizone building model is developed with the energy simulation software TRNSYS coupled with an air flow network in TRNFLOW. The innovative aspect of this work lies in the validation of a combined multizone building model under stochastic occupancy gains, taking into account room-wise heating profiles, ventilation, and interior air flow currents due to window and door operation. The results of a coheating test showed that the errors in the simulated heating power for the whole house are a coefficient of variation of the root mean square error (CV(RMSE)) of 12.72% and a normalised mean bias error (NMBE) of −8.35%. Nevertheless, more difficulties were found in separately predicting the heating power of each individual room. However, the combined multizone air flow model was able to predict indoor temperatures in all rooms with root mean square error (RMSE) and mean absolute error (MAE) values of<1 °C.

Bio-inspired structure using random, three-dimensional pores in the polymeric matrix for daytime radiative cooling

Inspired by the structural white of natural fibers, this work here demonstrated a novel passive radiative cooling design that builds random, three-dimensional pores in polymer matrix via the phase separation-based method. The pore size and structure are controlled by the compatibility between the shape-supporting matrix and other soluble polymers, where the semi-crystalline polypropylene (PP) was used as the shape-supporting matrix that is insoluble in any organic solvent at room temperature, and the soluble polymers were styrene-butadiene-styrene block copolymer (SBS) and styrene-ethylene-propylene-styrene block copolymer (SEPS). The experimental results revealed that those macroporous PP sheets have high solar reflectance (~97%) and tunable thermal emissivity (0.81-0.67). The outdoor thermal measurement also shows that those macroporous PP film-covered devices displayed a temperature (44.0 °C), much lower than those of air (50.0 °C) and neat-PP-covered device (60.4 °C). The complete simulation of a building thermal behavior evaluation shows that the macroporous PP roof-covered buildings exhibit a higher cooling effect than the neat PP roof-covered building in summer. Therefore, the required cooling power of the macroporous PP roof-covered buildings saved about 200 W compared with the neat PP roof-covered building on a typical summer day. • The bio-inspired design of the random pore structure for radiative cooling. • Investigate the effect of the pores' size and shape on the optical properties. • Estimate the heat insulation effect of building covered by the structured material.

Comparative Study of a Clean Technology Based on DSF Use in Occupied Buildings for Improving Comfort in Winter

This paper presents a comparative study of a clean technology based on a DSF (double skin facade) used in winter conditions in the occupied buildings comfort improvement, namely the thermal comfort and air quality. The performance of a solar DSF system, the building’s thermal response, the internal thermal comfort and the internal air quality are evaluated. In this study, a DSF system, an air transport system and a HVAC (heating, ventilating and air conditioning) system based on mixing ventilation are used. The study considers a virtual chamber occupied by eight persons and equipped, in the outside environment, by three DSFs. A new horary pre-programming control methodology is developed and applied when the airflow rate is constant and the number of DSFs to operate is variable, when the airflow rate is variable and the number of DSFs to operate is constant and when the airflow rate is variable and the number of DSFs to operate is variable. This work uses a numerical model that simulates the integral building thermal behavior and an integral human thermal response. The internal air, provided by a mixing ventilating system, is warmed using the DSF system. The air temperature inside the DSF system and the virtual chamber, the thermal comfort level using the PMV index, the internal air quality using the carbon dioxide concentration and the uncomfortable hours are calculated for winter conditions. The results obtained show that the energy produced in the DSF, using solar radiation, guarantees acceptable thermal comfort conditions in the morning and in the afternoon. The indoor air quality obtained at the breathing level is acceptable. It is found that the airflow rate to be used is more decisive than the DSF operating methodology. However, when a solution is chosen that combines a ventilation rate with the number of DSF to operate, both variables throughout the day can obtain simultaneously better results for indoor air quality and thermal comfort according to the standards.

Evaluation of the effect of nanocoated window glazing films on the thermal behavior of buildings

AbstractIn this study, different coating samples of antimony tin oxide (ATO) films on clear glass were prepared and their optical and thermal properties were evaluated. Performance parameters such as glass U‐factor, solar heat gain coefficient, temperature distribution, and net heat transfer gain using the glazed walls inside the test room were determined through numerical simulations. The results obtained indicate that the nanocoating thickness and doping level concentration of ATO films have a pronounced effect on the thermal insulation of the window glass. When compared with normal window clear glass, the transmittance of solar radiation for ATO thin‐film coating of thickness 1140 nm reduced from 0.881 to 0.114 in the visible (VIS) region and 0.817 to 0.012 in the infrared (IR) region, whereas radiation in the ultraviolet (UV) region was almost completely blocked. Similarly, for the ATO films with doping level 15%, the transmittance value reduced from 0.742 to 0.432 in the VIS region and from 0.718 to 0.114 in the IR region, and radiation in the UV region was almost totally blocked. Finally from the analysis of results, it was observed that the ATO thin films show exceptional optical and thermal properties and exhibit improved solar blocking behavior when compared with commercially available glazing (eg, from Saudi Arabian glass companies).