Building Energy Performance Simulation Research Articles

Multi-objective optimization of energy and daylight performance for school envelopes in desert, semi-arid, and mediterranean climates of Iran

Considering global warming as a critical challenge to human life, performance optimization of the building envelope can play a noticeable role to reduce a building's environmental footprint. Meanwhile, shading systems are considered as sustainable passive solutions to contribute to building energy efficiency, and daylight control in early-stage design. Nonetheless, a paucity of research has examined the various shading strategies in diverse climates, particularly arid desert and steppe regions, in conjunction with educational buildings. The present study aims to evaluate the role of fixed exterior shading systems (FESSs), and window-to-wall ratio (WWR) on building’ thermal and daylight performance in Iran with desert, semi-arid, and Mediterranean climates. Pareto Frontier and weighted-sum method for multi-objective optimization, and sensitivity analysis were used to study the optimum solutions, and the relationship between the design variables and the performance metrics. Three objective metrics including Spatial Daylight Autonomy (sDA), Annual Sunlight Exposure (ASE), and Energy Use Intensity (EUI), were defined as performance metrics. Shadings include vertical louver, horizontal louver, light shelf, overhang, and egg-crate. The results showed that EUI was reduced via FESS-integrated façade by 25.22%, 20.84%, 19.14%, 14.06%, and 13.47%, in Yazd, Bushehr, Kerman, Rasht, and Mashhad as selected studied cities, respectively. Based on ASE, the horizontal louver surpasses the other systems by 100% ASE value reduction in all climate zones except for Rasht. sDA level is reduced in all climate zones considering five studied FESSs excluding horizontal louver in Yazd and Rasht and overhang in Mashhad by 100% sDA level. Building energy performance simulation is validated by ASHRAE140-2020.

Sensitivity Assessment of Building Energy Performance Simulations Using MARS Meta-Modeling in Combination with Sobol’ Method

Large discrepancies can occur between building energy performance simulation (BEPS) outputs and reference data. Uncertainty and sensitivity analyses are performed to discover the significant contributions of each input parameter to these discrepancies. Variance-based sensitivity analyses typically require many stochastic simulations, which is computationally demanding (especially in the case of the large number of input parameters involved in the analysis). To overcome these impediments, this study proposes a reliable meta-model-based sensitivity analysis, including validation, Morris’ method, multivariate adaptive regression splines (MARS) meta-modeling, and Sobol’ method, to identify the most influential input parameters on BEPS prediction (annual energy consumption) at the early building design process. A hypothetical building is used to analyze the proposed methodology. Six statistical metrics are applied to verify and quantify the accuracy of the model. It is concluded that the cooling set-point temperature and g-value of the window are the most influential input parameters for the analyzed case study.

Enhancing energy efficiency and comfort with a multi-domain approach: Development of a novel human thermoregulatory model for occupant-centric control

Occupant Centric Control (OCC) strategies aims to achieve a more personalized and comfortable indoor environment, translating occupants' comfort requirements into real environmental conditions. This strategy relies on a multilevel non-linear programming optimization procedure to determine optimal thermo-hygrometric setpoints. The objective is to minimize space heating and cooling requirements while addressing multi-domain comfort concerns related to individual thermal sensations and perceived air quality. To facilitate this process, this study adopts a novel Personal Comfort Model (PCM), incorporating physiological factors to predict occupants' thermal sensations and CO2 intakes. The PCM's reliable predictive capabilities are confirmed through validation with real experimental data from a test room at the University of Perugia. For detailed energy analyses, considering occupants' subjective comfort preferences, the PCM is seamlessly integrated into DETECt, an in-house building energy performance simulation tool developed by the University of Naples Federico II, for the design of advanced control algorithms and energy efficiency strategies. To effectively manage the HVAC system, a model predictive control is implemented, using the determined setpoints to ensure mechanical ventilation and maximize cost savings. The proof of concept for the developed methodology involves simulating experimental tests using the thermal model of the human body and the new facility management system, to be simulated and optimized by means of the enhanced building energy performance simulation tool, exploited for the design and operation of more occupant-centric and sustainable buildings. The proposed study demonstrates that through the developed OCC strategy enables a significant reduction in thermal discomfort (40 % and 60 % less than the occupation time for test 1 and test 2, respectively) compared to reference scenarios. Additionally, energy analysis reveals high efficiency, achieving savings of 12.8 % and 7.8 % of electricity consumed for HVAC system operation.

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.

A comprehensive model considering multiple types of occupant behavior for building energy performance prediction and simulation – taking a university campus as an example

Occupant behavior, encompassing occupant numbers and actions, significantly contributes to the uncertainty of building energy performance simulation. By characterizing the regularity of occupant behavior, it becomes feasible to evaluate its impact on building energy consumption and mitigate disparities between simulated and measured outcomes. This paper presents a comprehensive model that integrates two key aspects of occupant behavior to predict and simulate energy performance in campus buildings. The model introduces two distinct categories of equipment, namely environment-related equipment (e.g. lighting, air conditioning) and time-related equipment (e.g. water dispenser, printer), to account for the diverse factors that influence occupant behavior. The impact of occupant perception on the environment is considered to enhance simulation accuracy. Furthermore, this study compares the proposed model with models that consider only occupant number or occupant energy use behavior individually to assess its effectiveness in capturing the complexities of occupant behavior in energy performance simulations.

A New Approach in Daylighting Design for Buildings

Daylighting is a future building design trend, subjected to all the global energy efficiency standards, but its design still largely depends on the architect and it is difficult for electrical engineers to precisely quantify the energy efficiency of the solution. This study presents an innovative integration process of design and simulation, rooted in the parameters of existing standards and regulations, and leveraging architectural design and simulation software tools. Within this novel integrated design methodology, the peripheral zones of the building envelope and the central core areas of the structure are discretely conceptualized and designed. Daylight intensity in the building envelope’s peripheral zones is calculated based on the average daylight factor and the building’s optimized window-to-wall ratio. In contrast, the central core areas are designed to attract daylight from open spaces on the roof, roof structure, reflective roofs, and clerestories. A building energy performance simulation tool is utilized to validate the energy efficiency of these new design techniques. This fresh approach is tested on a complex pilot-scale building in Nhon Trach, Dong Nai, Vietnam, to evaluate the method’s feasibility and scientific soundness. The simulation results corroborate the accuracy of the proposed approach in quantifying the efficiency of reducing a building’s lighting system energy consumption by 34%, equivalent to an annual CO2 saving of over 4,000 tons. Through the proposed daylighting design, an approximate 7% increase incurs in total initial investment, promising substantial profitable returns.

An Integrated Decision-Making Framework for Mitigating the Impact of Urban Heat Islands on Energy Consumption and Thermal Comfort of Residential Buildings

Urban heat island (UHI) is a zone that is significantly warmer than its surrounding rural zones as a result of human activities and rapid and dense urbanization. Excessive air temperature due to the UHI phenomenon affects the energy performance of buildings and human health and contributes to global warming. Knowing that most of the building energy is consumed by residential buildings, therefore, developing a framework to mitigate the impact of the UHI on residential building energy performance is vital. This study develops an integrated framework that combines hybrid micro-climate and building energy performance simulations and multi-criteria decision-making techniques. As a case study, an urban area is analyzed under the Urban GreenUP project funded by the European Union’s Horizon 2020 Programme. Four different strategies to mitigate the UHI effect, including the current situation, changing the low-albedo materials with high-albedo ones, nature-based solutions, and changing building façade materials, are investigated with a micro-climatic simulation tool. Then, the output of the strategies, which is potential air temperature, is used in a dynamic building energy simulation software to obtain energy consumption and thermal comfort data of the residential buildings in the case area. Finally, a multi-criteria decision-making model, using real-life criteria, such as total energy consumption, thermal comfort, capital cost, lifetime and installation flexibility, is used to make a decision for decreasing the UHI effect on residential energy performance of buildings. The results showed that applying NBSs, such as green roofs and changing existing trees with high leaf area density ones, have the highest ranking among all mitigation strategies. The output of this study may help urban planners, architects, and engineers in the decision-making processes during the design phase of urban planning.

A Numerical Study on the Exergy Performance of a Hybrid Radiant Cooling System in an Office Building: Comparative Case Study and Analysis

This research investigated the exergy enhancement performance of a hybrid radiant cooling system adapting to a hot and humid summer conditions through comparative case studies and analyses. This study suggested three cooling systems: a general all-air system (AAS), a conventional radiant cooling system (CRCS), and a hybrid radiant cooling system (HRCS). As a case study, an office building with cooling systems was examined in the summer season in four different cities: Beijing, Shanghai, Chengdu, and Guangzhou, China. This study utilized the building energy performance simulation program to analyze the cooling loads of office space in a building with numerical approaches. The comparison analysis using the four different weather datasets showed simple and rational exergy efficiency and the overall impact ratio. According to the results, the ambient conditions, i.e., the surrounding temperature and the humidity ratio, significantly impacted the cooling systems’ exergy efficiency ratio. On the basis of the calculated energetic and exergetic performance, the HRCS had a higher exergy efficiency and a higher overall impact ratio. The HRCS system released an additional 20–30% of cooling output, and it could adapt well in extreme hot and humid weather conditions compared to the AAS and the CRCS system. The overall cooling impact ratio of the HRCS with an airbox convector was approximately 185% higher than that of the AAS and 8.5% higher than that of the CRCS. This study can provide the design references for the hybrid radiant cooling system and other cooling systems in hot and humid summer conditions.

Modelling of underlying social psychological effects on occupant energy-related behaviours

Occupant energy-related behaviour research practices based on objective factors may not provide helpful insights that can derive from social sciences perspectives. This study examined how motivation (i.e., attitudes, personal norms), opportunity (i.e., subjective norms, organisational support, behavioural interventions, accessibility to control), and ability (i.e., perceived behavioural control, perceived and actual knowledge) explain the occupant energy-related behaviours in offices. In-person and online questionnaires were distributed across the general population of office occupants in New Zealand, and 294 valid answers were achieved. The social psychological effects on the choices of occupant energy behaviours are evaluated using a partial least squares structural equation modelling (PLS-SEM) analysis. The results showed that improving energy-saving opportunities through subjective norms, organisational support, behavioural interventions, and accessibility to control increases the occupants' perceived behavioural control and perceived knowledge to perform occupant energy-related behaviours. These opportunities and ability drivers then improve the occupants' motivation, building a strong attitude and obligation to save energy in the office environment. Understanding these social psychological factors are essential, as they utilise the development of occupant energy-related behavioural tools and integrate them with building energy performance simulations. Furthermore, the study assists in designing buildings to suit occupants’ comfort preferences and actions.

Working with Different Building Energy Performance Tools: From Input Data to Energy and Indoor Temperature Predictions

Energy consumption calculations and thermal comfort conditions assessment are crucial issues in building simulations when using Building Energy Performance Simulation (BEPS) tools. The available software has been separately validated under different boundaries and operating conditions. Consequently, the predicted output of the same building simulated with two separate software can disagree. This issue is relevant not only for research purposes but also for professionals who need to compare the energy performance of the same building with different simulation engines. This work aims at contributing to the field in two ways. Above all, it clarifies the preparation of the building model and the correct definition of input data and boundary conditions when different software are used (IDA ICE and Design Builder/Energy Plus). In addition, it compares the output (energy and indoor temperatures) of two BEPS for the same building (in different configurations) exposed to the same weather conditions. The study shows that the two most significant differences are represented by the temperature values, while the energy predictions agree.

Influence of using clay block with increassed mass on energy performance of an office builidng in Nis

The objective of the research was to compare various types of clay blocks in terms of construction thermal inertia parameters and the influence they would have on the energy performance of an office building located in Nis. For this, a new type of clay block with increased mass is proposed, and a custom approach for determining all relevant indicators is described, intensively relying on building energy performance simulations. Fourteen configurations of external walls made of clay blocks, including the newly proposed block with increased mass, were investigated using EnergyPlus with a custom weather file to obtain construction thermal storage indicators, i.e., time lag and decrement factor. The results show the average decrement factor of less than 1% and the average time lag of approximately 9 hours for the newly proposed clay block, which is very similar to the values obtained for commercially available clay blocks. In addition, the same model of the building was used to check the influence that this increased mass has on the energy performance of the building served by a low temperature radiant and fan coil system. The results indicate the possibility of reducing heating energy consumption by 3.65% by using the increased mass clay block, while maintaining similar wall U-values, when compared with regularly used clay blocks, with a negligible change in cooling energy consumption.

A novel approach to account for shape-morphing and kinetic shading systems in building energy performance simulations

This paper proposes an innovative approach to analyse the energy behaviour of complex kinetic shading systems. Although several studies have analysed this topic, many are focused only on certain aspects or on simple shading systems due to a lack of tools for running reliable energy simulations on complex systems. This study aims to develop and validate a tool based on Python and EnergyPlus that can consider the continuous nature of the energy simulation and analyse complex kinetic systems. Simply providing an EnergyPlus model and a model of the shading configurations, the algorithm provides as output a comparison sheet to evaluate the performance of the system. The paper provides a description of the tools and studies focused on this topic; subsequently, a methodological insight is presented to explain the workflow, its validation, and the algorithm developed. Finally, the algorithm is tested on a case study to analyse a kinetic shading system. Abbreviations: DSF: Dynamic Shading File; EDSM: Equivalent Dynamic Shading Model; EMS: Energy Management System; ESSM: Equivalent Static Shading Model; Gf: Incident irradiance; Gf max: Incident irradiance threshold; PV: Photovoltaic; ST1/2/3: State 1/2/3; SSM: Static Shading Model; SF: Sunlit Fraction; SSF: Static Shading File; To: Outdoor temperature; To max 1/2: Outdoor temperature threshold 1/2; Tsol: Solar transmittance

Enhancement of phase change material hysteresis model: A case study of modeling building envelope in EnergyPlus

Nowadays, buildings are expected to offer demand side services to the power grid to enhance the electrical load flexibility, which leads to the concepts of grid-interactive efficient buildings (GEBs). Phase change material (PCM)-based thermal energy storage has seen increasing attention in recent years for peak load shifting of grid-interactive efficient buildings (GEBs). Numerical models are critical tools for design and evaluation of PCM-integrated systems. Most industrial-grade PCMs are reported to melt/freeze over a temperature range instead of at a unique temperature. Such thermal hysteresis effect significantly affects the reliability of simulation results because not only the heat transfer process depends on melting and freezing temperatures, the PCM thermal properties change significantly during the phase change process as well. This study is aimed to develop a model for the PCMs used in the building envelope with the capability to accurately simulate hysteretic behaviors. This model is based on a two-phase assumption and is implemented in a whole building energy performance simulation program (i.e., EnergyPlus). A comparison between numerical results and experimental data shows that during a complete phase transition, the two-phase model could achieve a good agreement with the experimental data. During a partial phase transition, the two-phase model could lead to significant improvements compared to other alternative PCM models, including the existing PCM model in EnergyPlus. Last, whole building simulations were performed to study this model's performance regarding heating/cooling loads and zone mean air temperature of a given building. The results show that the difference in hourly heating/cooling loads introduced by the models was less than 1% in design conditions, while significant changes were observed in both hourly heating/cooling loads and zone mean air temperature when the PCM envelope underwent partial phase transition processes.

Numerical Assessment of Different Phase Change Materials as a Passive Strategy to Reduce Energy Consumption in Buildings under Tropical Climates

The building envelope design constrains how much HVAC systems must work to provide comfort. High thermal mass in walls is preferable to delay heat gain, as well as reduce it. Phase Change Materials (PCMs) seem to proportionate more thermal mass without increasing wall thickness because of their high latent heat. Thus, this work studies various PCM-based envelope layouts in four case studies, H060, H100, H200, and OB, under the tropical climate of Panama City, via building energy performance simulation. Energy and thermal comfort performance were used as criteria to determine an optimal PCM-based layout for such a climate through optimization analysis and to compare PCM-based and non-PCM-based envelope layouts. Results showed that among the considered combinations, PCM-based roof configurations provide more optimum solutions than PCM-based wall configurations. The PCM layout with a melting temperature of 27 °C allowed completion of the PCM cycle throughout the year. Although other PCM layouts did not present a complete charge/discharge cycle, such as the most frequent options at H060, H100, and H200, it suggests that PCM on liquid or solid phase provides better thermal performance than other considered combinations.

MARTINI: Smart meter driven estimation of HVAC schedules and energy savings based on Wi-Fi sensing and clustering

HVAC systems account for a significant portion of building energy use. Nighttime setback scheduling is an Energy Conservation Measure (ECM) where cooling and heating setpoints are increased and decreased respectively during unoccupied periods with the goal of obtaining energy savings. However, knowledge of a building’s real occupancy is required to maximize the success of this measure. In addition, there is the need for a scalable way to estimate energy savings potential from ECMs that is not limited by building specific parameters and experimental or simulation modeling investments. Here, we propose MARTINI, a sMARt meTer drIveN estImation of occupant-derived Heating, Ventilation and Air Conditioning (HVAC) schedules and energy savings that leverages the ubiquity of energy smart meters and Wi-Fi infrastructure in commercial buildings. We estimate the schedules by clustering Wi-Fi derived occupancy profiles and, estimate energy savings by shifting ramp-up and setback times observed in typical operational/static load profiles that are obtained by clustering smart meter energy profiles. Our case-study results with five buildings over seven months show an average of 8.1%–10.8% (summer) and 0.2%–5.9% (fall) chilled water energy savings when HVAC system operation is aligned with occupancy. We validate our method with results from Building Energy Performance Simulation (BEPS) and find that estimated average savings of MARTINI are within 0.9%–2.4% of the BEPS predictions. In the absence of occupancy information, we can still estimate potential savings from increasing ramp-up time and decreasing setback start time. In 51 academic buildings, we find savings potentials between 1%–5%.

Implementation of BIM Energy Analysis and Monte Carlo Simulation for Estimating Building Energy Performance Based on Regression Approach: A Case Study

The energy performance prediction of buildings plays a significant role in the design phases. Theoretical analysis and statistical analysis are typically carried out to predict energy consumption. However, due to the complexity of the building characteristics, precise energy performance can hardly be predicted in the early design stage. This study considers both building information modeling (BIM) and statistical approaches, including several regression models for the prediction purpose. This paper also highlights a number of findings of energy modeling related to building energy performance simulation software, particularly Autodesk Green Building Studio. In this research, the geometric models were created using Autodesk Revit. Based on the energy simulation conducted by Autodesk Green Building Studio (GBS), the energy properties of five prototype and case study models were determined. The GBS simulation was carried out using DOE 2.2 engine. Eight parameters were used in BIM, including building type, location, building area, analysis year, floor-to-ceiling height, floor construction, wall construction, and ceiling construction. The Monte Carlo simulation method was performed to predict precise energy consumption. Among the regression models developed, the single variable linear regression models appear to have high accuracy. Although there exist some limitations in applying the equation in EUI prediction, the rough estimation of energy use was realized. Regression model validation was carried out using the model from the case study and Monte Carlo simulation results. A total of 35 runs of validation were performed, and most differences were maintained within 5%. The results show some limitations in the application of the linear regression model.

Accurate identification of influential building parameters through an integration of global sensitivity and feature selection techniques

The development of building energy performance simulation models often requires significant time and effort to achieve an acceptable degree of prediction accuracy. As such, energy modelers introduce various simplifications and assumptions that require a high degree of modeling literacy to avoid any errors in energy predictions. Previous studies relate these simplifications to the identification of influential building parameters using engineering judgment techniques that are often subjective and differ based on experts’ opinion. The proposed methodology accurately defines influential and non-influential building parameters to formulate a guideline minimum dataset in the context of residential building energy models. The methodology integrates two feature selection techniques (Bayesian Information Criteria and Least Absolute Shrinkage with Selection Operator) with parametric analysis to determine the set of influential parameters. The study uses Irish residential archetypes to compare and validate the subsets of influential parameters using sensitivity rankings and established validation metrics. The predicted annual energy use lies within 10% of measured data for both subsets of influential parameters. Thereby, energy modelers could significantly reduce the time and effort spent on model development while maintaining the desired accuracy. The formulated datasets represent only influential features and hence, could be used by urban planners and energy policymakers to estimate energy retrofit investment costs, emission reductions and energy savings.

Developing a window behaviour model incorporating A/C operation states

There have been much research focus on modelling various types of occupant thermal adaptive behaviour with an aim to improve the reliability of building energy performance simulation (BEPS) tools. However, most existing studies exploring occupant interaction with residential building systems limit their scope to a single, isolated behaviour (e.g. window opening only), which fails to consider that occupant reactions are determined by the interconnected effects of series of adaptive actions. This study aimed to develop a stochastic window operation model to address the interdependency between occupant's window and A/C operation. Longitudinal field observations were conducted in a sample of 41 Brisbane region households over a one-year period. The sample households' occupancy status, indoor environmental conditions, use of windows and air-conditioning (A/C) were continuously recorded. The cut-off temperature beyond which the probability of windows being open begins to decrease with an increase of the outdoor temperature, was found to be approx. 27 °C in both living and sleeping areas within the monitored households. This paper proposes a window operation model that captures diverse window opening patterns observed amongst the sample households, with a particular focus on the effect of A/C operation state on window opening and closing actions. The proposed model was tested by running 1000 simulations on a validation dataset. A good agreement observed between 1000 simulations and actual window use patterns indicates that the model can be applied to dynamic building energy performance and indoor environment simulations.

Future-Proof Energy-Retrofit strategy for an existing Dutch neighbourhood

In the EU, buildings account for about 40% of the final energy consumption. Space heating (SH) accounts for 65% of the energy demand in buildings. There is thus a large potential in energy savings, simply by focusing on the reduction of space heating demand. In the Netherlands, there are about 6 million residential buildings constructed before 2005 where thermal performance is far from optimal. All these buildings need to reduce their demand as the first step to meet nZEB requirements. For large scale energy retrofit of buildings, the main challenge is to classify each housing type by the severity of the demand reduction.A Dutch district in the city of Apeldoorn has been chosen to investigate the energy-saving potential and the robustness of different retrofit packages. Using a bottom-up approach, the energy-saving potential of the existing building types in the mentioned district was analysed by the parametric study of building energy performance simulations in IES VE. Subsequently, the uncertainty in building (energy) performance was assessed using a minimax regret method to compare the robustness of designs. For the covered building types and construction periods, the results suggest that after the upgrade to insulation level from current regulations, implementation of Heat Recovery Ventilation (HRV) reduces the space heating demand by 50–65% while insulation upgrade to passive house standards on yields an average heat demand reduction of only 15%. In contrast, provided with sufficient ventilation, the results suggest that sufficient ventilation eliminates the demand for space cooling in passive building envelopes (excluding climate change effects). In this regard, detached housing is the most and semi-detached is the least prone to the overheated indoor environment. Relying on natural ventilation as the only source of cooling in the summer increases the risk of overheating. Furthermore, upgrading to the nZEB standard building envelope appears to be around 40–50% less economical (€/unit saved energy) than current requirements for building envelope. Lastly, the results show that packages with HRV are about twice as robust as packages without HRV thus highlighting its optimal cost.

Two novel resistance-capacitance network models to predict the dynamic thermal behavior of active pipe-embedded structures in buildings

Active pipe-embedded structures (APES) are promising low-energy systems for reducing cooling and heating loads in buildings. These structures are usually multilayered, having a main concrete layer where pipes are located. Simplified heat transfer models of these systems are often required for building energy performance simulations. The majority of the simplified models available in the literature show limitations to capture accurately the dynamic thermal behavior of these systems, especially when they have large thermal mass. The goal of the present effort is to introduce two new Resistance-Capacitance (RC) network models, the Delta and the Umbrella, for the main concrete layer of a prototypical APES system. The parameters of the models are obtained through a genetic algorithm, which is dynamically coupled with the models. This algorithm minimizes the error between the RC networks and a baseline dataset that was generated through a frequency-domain finite-difference (FDFD) model of an APES system. To assess the performance of the proposed models, they are compared with two others from the literature for three different thicknesses of the main layer. The results show that the Delta RC network has, in general, similar performance to the literature models. The overall nRMSE (normalized root mean square error) values for these models is between 6% and 10%. They are able to predict accurately the thermal response of the system to some of the boundary conditions analyzed, but show limitations for massive concrete layers under high frequency thermal fluctuations. The Umbrella RC network, on the other hand, was shown to have remarkable accuracy at higher frequencies than the other models, even for the massive structures. The overall nRMSE value for this model was of 3%.