Building Energy Demand Research Articles

Feasibility study of developing hollow-core vacuum insulated panels

Super-insulation materials have become more commonplace as highly insulated building envelopes are required to reduce the energy demand of buildings aligned with the net zero targets. While several super insulation materials are available, their environmental impacts and practical on-site limitations hindered their large-scale adoption. The following paper investigates the feasibility of developing hollow-core vacuum insulated panels supported by an internal structural array with different configurations. The designed panel was simulated and measured to evaluate its performance as a thermal insulator for building applications. Panel samples were manufactured from polished stainless-steel plates separated by a PTFE structural array. The change in temperature and heat flux through the sample was measured in a vacuum chamber at a pressure of 0.01 Pa. Thermal conductance was obtained from gradual measurements of heat flux and temperature across the sample after a rapid increase in temperature. Numerical methods that combine molecular and macroscopic solvers were used to model unsteady behaviour recorded in empirical tests. Direct Simulation Monte Carlo (DSMC) was used to calculate the thermal conductivity of the rarefied gas, which was then used to solve the enthalpy equation for the multi-region model. Thermal resistance from empirical tests and numerical methods are in agreement within error bands, the greatest accuracy observed in high conductance models. Thermal resistance as low as 0.17 [Formula: see text] and as high as 4.75[Formula: see text] was measured. Low conductance sample configurations were sensitive to thermal contact conductance from the structural array contact interfaces, accounting for at least 40% of transferred energy. Gas conduction at a pressure of 0.01 Pa transfers up to 4% of energy in low emissivity sample configurations. Radiative energy transfer in high conductance configurations was responsible for up to 95% of transferred energy. The paper provides a comprehensive feasibility study, providing a solid foundation for further design optimization of the technology.

The interactive indoor-outdoor building energy modeling for enhancing the predictions of urban microclimates and building energy demands

There is a lack of an urban building energy modeling framework that considers the influence of surrounding buildings and local urban climate on building thermal performance. This can lead to inaccurate results since the thermal performance of individual buildings is heavily influenced by their surrounding built and climatic environment. This study establishes an interactive indoor-outdoor building energy modeling method to enhance the predictions of urban microclimates and building energy demands by coupling an urban physics model with a physics-based building energy model. Validation of the interactive coupling scheme uses field measurement datasets. Parametric simulation and analysis are conducted to understand the influence of the roof-to-canyon width ratio, canyon orientation, and ground vegetation fraction on canyon temperature, building energy consumption, and energy demand. Furthermore, the impacts of building energy model complexity (e.g., detailed vs. simplified building models) and coupling approaches on canyon temperature and building energy profiles are demonstrated using two case study buildings. In comparison with the one-way coupling approach, cooling energy consumption predicted with the dynamic two-way coupling approach varies by 3.5 % and 0.5 % for the detailed medium office building model and high-rise building model, respectively, and peak cooling demand varies by 8.4 % and 7.0 % for the detailed medium office building model and high-rise building model, respectively. This study also suggests that adopting a complex two-way coupling approach with environmental data exchange at various elevations is necessary for modeling tall buildings at the urban scale.

A Fuzzy Logic Concept for Predicting the Seasonal Thermal Performance of Building Envelopes Based on Structural and Geographical Parameters

The current practice of building thermal retrofitting is based on the outcomes of energy audits that make use of standardized tabulated information mapped on a structure or structural elements under inherently very specific conditions. Therefore, it provides very limited outcomes for further analysis, especially when decision making procedures are required to particularize the retrofitting strategy. This paper introduces a novel fuzzy logic approach for predicting the thermal performance of building walls that can be used in practice to partially substitute time-consuming and costly energy audits or complex computational analyses. The objective of this concept is to forecast the annual heating energy demands of buildings and to identify the potential energy savings that could be achieved by applying thermal retrofitting measures based on limited resources and information obtained from maps, blueprints, and/or a simple site inspection. For this purpose, a sample knowledge base was created using a validated computational model of the heat and moisture transport in the multilayered wall assemblies. Then, the fuzzy logic model was introduced to predict the thermal performance of selected walls. Our comparison of the fuzzy model outputs with simulated data proved the potential of using the proposed concept as an efficient and straightforward tool for predicting the seasonal thermal performance of building envelopes and to partially replace the current practice, which requires the utilization of building energy audits.

Defining generation parameters with an adaptable data-driven approach to construct typical meteorological year weather files

Weather files play a critical role in building energy simulation by providing crucial information on the environmental condition. Typical meteorological year (TMY) weather files are often used in building energy simulation to approximate the long-term performance. These files are generated using long-term weather data and are meant to reflect the typical weather conditions within the dataset. Common generation methods used to construct TMY weather files typically rely on expert-defined universal generation parameters. However, the weather parameters and weighting factors used to generate these files, termed generation parameters, often overlook variations in climate and applications. This study introduces a data-driven approach to define the generation parameters used in the Sandia method to produce TMY weather files. The study aims to enhance the generation method by providing a framework to define the generation parameters which considers data availability, climate and application.The proposed methodology employs machine learning and feature importance to define the generation parameters used to construct TMY weather files. The generated TMY weather files are evaluated for a Montreal case study using building energy demand. The results show an improvement in the representativeness of the long-term average building energy demand for the weather file generated using the model-defined generation parameters.

Increasing Solar Reflectivity of Building Envelope Materials to Mitigate Urban Heat Islands: State-of-the-Art Review

The Urban Heat Island (UHI), a consequence of urban development, leads to elevated temperatures within cities compared to their rural counterparts. This phenomenon results from factors such as urban designs, anthropogenic heat emissions, and materials that absorb and retain solar radiation in the built environment. Materials commonly used in cities, like concrete, asphalt, and stone, capture solar energy and subsequently emit it as heat into the surroundings. Consequently, this phenomenon amplifies summertime cooling energy demands in buildings. To mitigate the UHI impacts, various mitigation strategies have emerged that include but are not limited to using higher solar reflectivity materials, known as “cool materials”, and increasing vegetation and greenery in urban areas. Cool materials have high reflectivity and emissivity, effectively reflecting solar radiation while emitting absorbed heat through radiative cooling. Increasing the solar reflectivity of building envelope materials is a promising sustainable solution to lessen the UHI effects. This state-of-the-art review summarizes the UHI causes and effects, states the mitigation strategies, describes the cool building envelope materials, explains the solar reflectivity index measurements, indicates the building and micro-climate simulations, highlights the performance evaluation of using cool building envelope materials, points out the research gaps, and proposes future research opportunities.

Annual Comparison of the Atmospheric Urban Heat Island in Rome (Italy): An Assessment in Space and Time

This study examined the atmospheric urban heat island (UHI) phenomenon within the city of Rome (Italy) and its effects on building energy demand. Weather data from 2020 and 2022 collected from six meteorological stations were considered. A Geographic Information System (GIS) was used to analyze the landscape, correlating the percentage of impermeable surfaces with UHI intensity values in each area. Dynamic simulations were conducted using different climatic data to estimate the heating and cooling energy demands for two representative residential buildings. The findings revealed significant differences in the climatic conditions between urban and rural areas, primarily due to temperature increases. The UHI intensities reached maximum values of 4.67 °C and 3.54 °C in 2020 and 2022. In urban areas, the UHI has positive effects on the heating energy demand but results in a significant increase in energy demand for cooling. Considering a building type constructed between 1900 and 1950, a variation of up to 33.03% in the heating energy demand in urban areas compared to rural areas was calculated, along with a variation of up to 81% for cooling. In contrast, considering a more recent building type constructed between 1991 and 2005, the corresponding values reached up to 36.47% and 75.7%.

Investigation of energy performance of nanotechnological material and double-skin facade system in office buildings in Turkiye

ABSTRACT Due to the increasing energy demand around the world, new concepts have emerged in order to reduce it in the building sector. In this context, double-skin facade systems have increasingly been used due to their effectiveness in reducing the heating load, cooling load, and primary energy demand of buildings. In this study, an office building located in the industrial zone in Kocaeli/Gebze was examined, and the effects of nano-technological materials to be added to the building and double-skin facade systems on the energy performance of the building were determined using DesignBuilder and EnergyPlus simulation programs. As a result; with the thermochromic glass and vacuum insulation panels used in the nano scenario, the heating load was reduced by 70% when compared with the base model. In the double skin scenario implemented on the east–west facade, heating load was decreased by almost 25% and cooling and primary energy demand were decreased by nearly 20% as compared to the base model. Whereas, an efficiency of 20% in heating load, 40% in cooling load and almost 30% in primary energy demand was observed in the nano double skin scenario when compared with the base model.

Decarbonising heating and cooling using temperature setback and geothermal energy

The lion’s share of buildings’ energy consumption is used for maintaining a thermally comfortable indoor environment. Strategies of reducing heating and cooling demand can thus be crucial for buildings to achieve net zero. This research aims to investigate the extent to which an occupancy-based temperature setback strategy and geothermal energy supply can decarbonise an office building. The objectives include: 1) exploring the optimal setback temperature for maximum energy savings, both in present time and under the future climate scenarios, and 2) evaluating the extent to which a geothermal borehole can meet the building’s energy demand. The outcome shows that a temperature setback strategy coupled with geothermal energy supply can decarbonise heating and cooling by around half. As for overall building energy demand, temperature setback can make demand reduction by over a tenth while the geothermal energy can meet the demand by a minimum of a fifth.

Assessing the impact of morphed CMIP6 climate model outputs on building energy performance simulations

In this paper we utilise the latest generation of climate change models from Climate Model Intercomparison Project 6 (CMIP6) to morph typical meteorological year (TMY) weather files. These morphed files are used in conjunction with a standard set of reference models for the climate zones in the United States. Building energy demand, comfort, and photovoltaic (PV) yield are all simulated and evaluated for how they can be expected to change under each climate scenario. We find that the shifts in various preference metrics may vary seasonally in both magnitude and direction. Additionally, the overall distribution of the change can be highly variable across the US climate zones. The results indicate that buildings, as designed now, may not be able to provide comfortable conditions in the future. Furthermore the impact of increased cooling demand will change how building-based PV electricity yield is utilised.

Veggies and PV: Optimization of Building-Integrated Agriculture in an Energy Hub

Building-integrated Agriculture (BIA) is the concept of utilizing façade surfaces for crop production. The potential is to reduce land-use and to increase the utility of built-up area. If outdoor building surfaces were to be used for farming, it results in a competition for sunlight between crops, solar renewable energy (such as photovoltaics, PV), and daylight access to illuminate indoor spaces. We therefore propose a coupled BIA and multi-energy systems model that can represent various energy sources (such as sunlight) and their conversion and storage (such as façade based crop production, PV, or electric batteries) in order to optimally meet demands for building energy and food. It is formulated as a mixed integer linear program (MILP) optimization model that describes the energy flows as an annual hourly time series. The model conducts a bi-objective minimization of monetary cost and carbon emissions, resulting in a Pareto front of optimal solutions. The model meets building energy demands for cooling, heating and electricity by an optimized energy technology portfolio, as well as nutritional demands for leafy vegetables of all occupants by either BIA or supermarket purchases. We apply our model to a residential case study in Singapore, which serves as an example for a high density city with already numerous community-driven and practiced BIA initiatives ongoing. Our results show that when minimizing cost, utilizing PV is more cost efficient on highly exposed surfaces such as the roof than BIA. However, for more shaded façade surfaces, crop production can be a cost and environmentally efficient addition to building design, as the annual vegetables demand of all occupants can be covered entirely by self-grown produce.

Gothenburg Digital Twin. Modelling and communicating the effect of temperature change scenarios on building demand

The article presents the first prototype of a web-based City Digital Twin (CDT) viewer developed for the city of Gothenburg to promote the understanding of the interrelation between the spatial, energy and climate change domain related to building stocks and to support stakeholders’ collaboration. The focus is on the development process of the viewer and decisions taken to target the users. 3d spatial data about the current city combined with building energy demand modelling following the Intergovernmental Panel on Climate Change scenarios provide the basis that potentially allows local actors to take informed decisions about decarbonization measures in a long-term perspective. First, future climate conditions are modelled for Gothenburg following the IPCC pathways 2.6, 4.5, and 8.5 for the year 2050. Second, a Building Stock Model is employed to simulate the energy use of buildings by using the climate scenarios as climate boundary conditions. Finally, results are visualized in the CDT viewer showing that future climate conditions contribute to an overall decrease in final energy demand up to 18% in spring and autumn months and an increase up to 1.5% in summer months.

Estimating residential buildings’ energy usage utilising a combination of Teaching–Learning–Based Optimization (TLBO) method with conventional prediction techniques

Among the most significant solutions suggested for estimating energy consumption and cooling load, one can refer to enhancing energy efficiency in non-residential and residential buildings. A structure's characteristics must be considered when estimating how much heating and cooling is required. To design and develop energy-efficient buildings, it can be helpful to research the characteristics of connected structures, such as the kinds of cooling and heating systems needed to ensure sui interior air quality. As an important part of energy consumption and demand of buildings, the assessment of cooling load conditions from the envelope of large buildings has not been comprehensively understood yet. In the present paper, a new conceptual system has been developed to anticipate cooling load in the sector of residential buildings. Also, the paper briefly describes the major models of the developed system to maintain continuity and concentrate on the prediction model of the cooling load. To predict cooling load, authors have modelled two methods of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in conjunction with teaching-learning-based optimization (TLBO). This article aims to illustrate how artificial intelligence (AI) approaches play an essential role in addressing the mentioned necessity and help estimate the optimal design parameters for various stations. The value of the multiple determination coefficient is also determined. The values of the training R2 (coefficient of multiple determination) are 0.96446 and 0.97585 for TLBO-MLP and TLBO-ANFIS in the training stage and 0.95855 and 0.9721 in the testing stage, respectively, with an unknown dataset which is acceptable. The training RMSE values for TLBO-MLP and TLBO-ANFIS are 0.0685 and 0.11176 for training and 0.07074 and 0.12035 for testing, respectively, for the unknown dataset, which is acceptable. The lowest RMSE value and the higher R2 value indicate the favourable accuracy of the TLBO-MLP technique. According to the high value of R2 (97%) and the low value of RMSE, TLBO-MLP can predict residential buildings’ cooling load.

A phase change material (PCM) based novel retrofitting approach in the air conditioning system to reduce building energy demand

Improvement in air quality and reduction of airborne diseases can be effectively achieved by inducing fresh air into the building. However, this challenges the building’s energy consumption. As a proposed solution, the present study dwells into the use of phase change materials (PCM), a heat-based exchanger, as a retrofit with a Heating Ventilation and Air-conditioning system (HVAC), to extract thermal energy from fresh air and consequentially diminish the energy demand. Calcium chloride hexahydrate (CaCl2·6H2O) is used as PCM, selected by TOPSIS method based on entropy weights. The energy saved in the augmented PCM thickness (from 20 mm to 100 mm) and added longitudinal fins (5 mm in width and 50 mm in length) is assessed in comparison to the standard HVAC systems utilizing R134a. For summers in composite climate, the HVAC system retrofitted with a heat exchanger with 100 mm PCM thickness 48 fin configuration achieved peak and average energy savings of 12 % and 9 %, respectively, when the system operated for 6 h. For PCM thicknesses of 75 mm, 50 mm, and 20 mm, the maximum energy savings were 6.64 % with 48 fins, 5.22 % with 24 fins, and 3.22 % with 12 fins, respectively. The study provides policymakers with energy-efficient and sustainable solutions for HVAC systems, which can reduce energy demand and help combat climate change.

Electrical consumption forecasting in sports venues: A proposed approach based on neural networks and ARIMAX Models

Building energy demand accounts for an important part of global energy consumption and emissions. Load forecasting is a crucial functionality for building management systems (BMSs) in order to optimize the energy supply and use with the goal of achieving economic and environmental benefits. Furthermore, if the building is a sports/event venue (one of the most energy-intensive facility type), load forecasting acquires an even greater importance.This paper presents a new forecasting hybrid technique that combines an ensemble of Artificial Neural Networks (ANNs) with an AutoRegressive Integrated Moving Average with eXternal inputs (ARIMAX) model. The proposed methodology is designed for the 24-hour-ahead electrical consumption forecasting of sports venues. These types of buildings are generally characterized by large fluctuations in their load caused by the hosted events, and therefore they are challenging for forecasting tools. The ANNs ensemble is composed of the average output of several multi-layer perceptrons, while the ARIMAX is used to correct the prediction provided by the ANNs working on the ensemble residual. The proposed approach can be easily implemented within a BMS that can be crucial for an efficient facility management. The hybrid methodology has been tested and validated on the load data of a real football arena. The proposed methodology achieves a MAPE of 9.03 % on an open dataset, and 9.38% on a closed dataset, outperforming standard machine learning techniques, hybridization techniques involving time-series and machine learning methods, as well as a state of the art event venue load forecasting technique. Furthermore, a feature importance study reveals that all the selected inputs are crucial in achieving the result. Finally, the inclusion of results related to an open dataset encourages further studies and comparisons.

An Assessment of Long-Term Climate Change on Building Energy in Indonesia

This paper reports on modelling outcomes for improvements to building energy performance in Indonesia. Long-term climate effects due to building energy demand and carbon emissions are also considered. The global change assessment model (GCAM) was used to generate the related end-user building energy data, including socioeconomics, for urban areas of Indonesia. As a comprehensive study, the total life cycle of carbon in the building sector and the concept of zero-carbon buildings, including energy efficiency, zero-emissions electricity and fuel-switching options, were considered. Building shell conductance (U-value) of the building envelope, floor area ratio (FAR), air conditioner (AC) efficiency, electrical appliance (APL) efficiency, rooftop photovoltaic (PV) performance and ground source heat pump (GSHP) systems were considered as parameters to mitigate carbon emissions under the operational energy category in the GCAM. Carbon mitigation associated with the cement production process was considered in the raw material category. Urban population and labour productivity in Indonesia were used as base inputs with projected growth rates to 2050 determined from the available literature. Low growth rate ‘LowRate’ and high growth rate ‘HighRate’ were considered as variable inputs for U-value, FAR, AC efficiency, APLs efficiency and PV capacity factor to model emissions mitigation. The energy consumption of the GSHP was compared to the conventional reverse cycle ACs to identify the potential of the GSHP as a fuel-switching option. In the GCAM, the benchmark (base case scenario) data set was generated based on input parameters (urban population and labour productivity rate) only for the residential building sector in Indonesia. Total potential carbon emissions mitigation was found to be 432 Mt CO2-e for the residential building sector in Indonesia over 2020–2050. It was found that an average of 24% carbon emissions mitigation could be achieved by 2020–2030 and 76% by 2031–2050.

Direct-expansion solar-assisted heat pump coupled with crystallisation-controlled supercooled PCM for shifting building electricity demand

This paper proposes a novel approach for improving the performance of a direct-expansion solar-assisted heat pump (DX-SAHP) system by integrating a crystallisation controllable phase change material (PCM) and building energy demand shifting strategy. The proposed system aims to reduce buildings' energy consumption while utilizing solar energy efficiently. Sodium acetate trihydrate (SAT) as the crystallisation controllable supercooled PCM has a promising feature of releasing the stored latent heat when it is externally triggered, allowing the user to control the stored heat whenever needed. In the study, the PCM can store the thermal energy generated by the DX-SAHP during peak solar hours in an efficient way. This stored energy can be released when solar energy is unavailable, but the heating is requested. This approach not only reduces the load on the heat pump system but also makes efficient use of the stored thermal energy by crystallisation controllable PCM. A detailed controlling methodology of the system is given, and heating output is controlled considering the level of solar irradiance. The proposed system was modelled and simulated using MATLAB, and the results show that the integration of crystallisation controllable PCM and building demand shifting strategy can significantly reduce the energy consumption of the buildings. On the low solar radiation day, the COP improvement compared to the air source heat pump system was found 9.4%, this improvement value can reach 77% on high solar radiation days. The system can also effectively utilize solar energy and reduce the overall carbon footprint of buildings.

A comprehensive review of artificial intelligence and machine learning applications in energy consumption and production

The energy industry worldwide is today confronted with several challenges, including heightened levels of consumption and inefficiency, volatile patterns in demand and supply, and a dearth of crucial data necessary for effective management. Developing countries face significant challenges due to the widespread occurrence of unauthorized connections to the electricity grid, resulting in substantial amounts of unmeasured and unpaid energy consumption. Nevertheless, the implementation of artificial intelligence (AI) and machine learning (ML) technologies has the potential to improve energy management, efficiency, and sustainability. Therefore, this study aims to evaluate the potential influence of AI and ML technologies on the progress of the energy industry. The present study employed the systematic literature review methodology to examine the challenges arising from frequent power outages and limited energy accessibility in various developing nations. The results of this study indicate that AI and ML possess significant potential in various domains, including predictive maintenance of turbines, optimization of energy consumption, management of power grids, prediction of energy prices, and assessment of energy demand and efficiency in residential buildings. This study concluded with a discussion of the necessary measures to enable developing nations to harness the advantages of AI and ML in the energy sector.

Evaluation of Two Chilean Native Macroalgae: “Pelillo” (Gracilaria chilensis) and “Lamilla” (Ulva sp.) for Thermal Insulation Application

Energy consumption in the residential sector and air pollution are relevant topics for the global population. One of the causes, especially in cold climate cities, is that buildings maintain a high energy consumption for heating and cooling, primarily using low-efficiency biomass combustion for heating, which releases a significant amount of particulate matter into the environment. In this context, thermal insulation materials play a crucial role in reducing the energy demand of buildings, requiring advancements in the sustainable development of such materials within the context of climate change. This study carried out an evaluation of two algae species found along the Chilean coasts, with the aim of characterizing them and creating a prototype of a sustainable material. Their physicochemical properties were analyzed, and the results demonstrate that the algae exhibit excellent thermal insulation properties, with an average thermal conductivity of 0.036 [W/mK]. This result is comparable to expanded polystyrene (EPS), a widely used material in the Chilean and global markets, which has an average thermal conductivity value of 0.038 [W/mK]. Additionally, the algae show a good thermal stability, and their morphology contributes to the development of a bulk material, as they possess a porous structure with air chambers between the fibers.

Challenges in benchmarking whole-life GHG emissions from renovation cases: Evidence from 23 real-life cases

While political initiatives focus on energy efficiency to reduce greenhouse gas emissions (GHGe), there is currently a lack of knowledge about GHGe from energy-efficiency measures versus other renovation measures. Recognizing the multitude of purposes and functions in play within real-life renovations is central to the ongoing development in benchmarking and regulating the life-cycle GHGe of building projects, to ultimately limit emissions from the growing number of renovation projects. This study therefore investigates lifecycle-based GHGe from the multitude of changed functions in 23 real-life cases of renovation and considers how the results contribute to discussions on benchmarking renovation projects. The results show that, from a lifecycle perspective, energy efficiency actions in renovation produced operational savings of around 50 % from 13.5 kg CO2-eq/m2/year to 7.0 kg CO2-eq/m2/year on average, indicating the significance of reducing the energy demand of buildings. The material-related, embodied GHGe contributed to an average of 2.8 kg CO2-eq/m2/year in the 23 renovation cases. A remarkable 54 % of these lifecycle-embodied impacts from the renovation cases are associated with other functions than energy efficiency, such as spatial adjustments, changes in interior layout, or the construction of balconies. The results contribute to discussions of three benchmarking approaches suggested in literature. First, single benchmarks for the whole building. This approach does not encompass the large variation in impacts and functions that are showcased in the renovation cases. Second, benchmarks on a smaller scale, such as building elements. This approach can be explored further, and the study provides pointers to the significance of different elements. Finally, benchmarks based on GHGe “savings” from energy reductions. The approach only considers one function and not the significance of the multitude of other functions added in renovation projects.

Influence and sensitivity evaluation of window thermal parameters variations on economic benefits of insulation materials for building exterior walls–a case study for traditional dwelling in China

The energy consumption intensity of rural buildings in China shows an increasing trend. Adding insulation materials for exterior walls can lower the building energy demand (ED), and evaluating its economic benefits (EB) can provide valuable references for decision-makers. Although many studies have evaluated the EB of wall insulation, the effect of window thermal parameters is rarely discussed. The variations of window thermal parameters can change the ED and the investment cost of the wall insulation, then change the final EB of the wall insulation. In this work, the impacts of four window thermal parameters (U-value, solar heat gain coefficient, window-to-wall ratio, air changes per hour) on the ED and EB of wall insulation are investigated. Then they are ordered according to the global sensitivity analysis results. Meanwhile, the effects of building orientations on these results also be discussed. Results show: (1) The optimum insulation thickness is 50 mm for traditional dwellings when considering the window system; (2) The EB of wall insulation is most sensitive to the air changes per hour, followed by window-to-wall ratio, U-value, and solar heat gain coefficient; meanwhile, the variation of building orientation cannot change this sensitivity order; (3) The insulation costs cannot be balanced out when the air changes per hour exceeds 1.3 h−1 and insulation is not thicker than 5 mm.