Building Energy Simulation Tools Research Articles

Assessing the impacts of air-sealing on the sizing, operation, and economic feasibility of ground-source heat pumps for electrifying single-family houses in the US

According to recent studies and reports, in single-family houses (SFHs), air-sealing can significantly lower the thermal loads for space heating and cooling. Thus, air-sealing in SFHs could reduce the required size and cost of ground source heat pump (GSHP) systems for electrifying SFHs. This study investigated the costs and benefits of integrating air-sealing with GSHPs for retrofitting existing SFHs when compared with air-source heat pumps. A whole building energy simulation tool integrated with an advanced design tool for modeling ground heat exchangers was used to calculate changes in required GSHP capacity, total borehole length, and building energy consumption with and without air-sealing in SFHs in 16 US climatic regions. The results from this study showed that reducing outdoor air infiltration from 0.8 air changes per hour (ACH) to the minimum ventilation requirement (0.35 ACH) can significantly reduce borehole length (up to 55%), GSHP capacity (up to 48%), and total heating electricity reduction, especially in cold climates (up to 44%). The results also showed that for airtight homes (0.03 ACH infiltration) with a direct outdoor air system, the minimum required borehole length, GSHP capacity, and total heating electricity consumption can be reduced up to 70%, 68%, and 67%, respectively, when compared with SFHs with 0.8 ACH infiltration. Moreover, the life cycle cost analysis showed that air-sealing in conjunction with a GSHP is more profitable than replacing the existing system with an air-source heat pump, even without any incentives for most climatic regions in the US (except for some hot regions).

Research on the Design of Green Roofs for Office Buildings in Xuzhou Based on Building Energy Consumption Evaluation

The roof is the part of a building that is exposed to solar radiation for the longest period, making green roofs particularly effective in reducing air conditioning energy consumption during the summer. This study aims to assess the advantages of modular green roofs in terms of energy savings and cost reduction during the summer in Xuzhou. By conducting field measurements and surveys under both air-conditioned and non-air-conditioned conditions and utilizing building energy simulation tools, the performance of green roofs with different parameters was compared. Using EnergyPlus, factors such as soil thickness, thermal conductivity, and leaf area index were simulated. The results indicated that green roofs have superior thermal performance in summer, with the daily cooling load per unit area for top-floor rooms being 1.05 kWh/m2, 0.21 kWh/m2 lower than that for bare roofs, achieving an energy saving rate of 16.7%. It is recommended that soil thickness not exceed 0.3 m and insulation thickness not exceed 0.05 m or be set to 0 m. Take building no. 2 of the Xuzhou material market as an example: with the optimized green roof, the energy saving rate increased to 27.0%, which is 12.4% higher than that of the original green roof. The suggested cost for modular green roofs is 204 RMB/m2.

Advancing energy efficiency in livestock building: Simplified building energy simulation tool for geometric design of pigsty

Concerns related to food security and animal welfare have led to increasing interest in analyzing the energy demands of heating, ventilation, and air conditioning (HVAC) systems in livestock buildings. However, building energy simulation (BES) model, which facilitates precise HVAC systems energy analysis, is difficult to use for non-experts, and particularly, its usability drops significantly in livestock buildings, which are often constructed without involvement of building engineers. This study, therefore, developed a tool that can analyze the energy demand of pigsties by changing geometric design variables with only several numerical inputs without separate 3D modeling. Pigsty geometric design types were classified by examining pigsty architectural design manual and web portal map search. A simplified tool, utilizing EnergyPlus and MATLAB graphical user interface, was developed to analyze energy demand in pigsties by leveraging simple user inputs for geometric design modeling. This tool simplifies exploring potential energy savings through geometric design changes in the early design stages of livestock buildings. The methodology introduced in this study lays the groundwork for future enhancements, including the integration of various design-phase variables beyond geometric design.

From White to Black-Box Models: A Review of Simulation Tools for Building Energy Management and Their Application in Consulting Practices

Buildings consume significant energy worldwide and account for a substantial proportion of greenhouse gas emissions. Therefore, building energy management has become critical with the increasing demand for sustainable buildings and energy-efficient systems. Simulation tools have become crucial in assessing the effectiveness of buildings and their energy systems, and they are widely used in building energy management. These simulation tools can be categorized into white-box and black-box models based on the level of detail and transparency of the model’s inputs and outputs. This review publication comprehensively analyzes the white-box, black-box, and web tool models for building energy simulation tools. We also examine the different simulation scales, ranging from single-family homes to districts and cities, and the various modelling approaches, such as steady-state, quasi-steady-state, and dynamic. This review aims to pinpoint the advantages and drawbacks of various simulation tools, offering guidance for upcoming research in the field of building energy management. We aim to help researchers, building designers, and engineers better understand the available simulation tools and make informed decisions when selecting and using them.

An integrated computational method for calculating dynamic thermal bridges of building facades in tropical countries

Identifying thermal bridges on building façades has been a great challenge for architects, especially during the conceptual design stage. This is not only due to the complexity of parameters when calculating thermal bridges, but also lack of feature integration between building energy simulation (BES) tools and the actual building conditions. For example, existing BES tools predominantly calculate thermal bridges only in steady state without considering the temperature dynamic behaviour of building outdoors. Consequently, relevant features such as thermal delay, decrement factor, and operative temperature are often neglected, and this can lead to miscalculation of energy consumption. This study then proposes an integrated method to calculate dynamic thermal bridges under transient conditions by incorporating field observations and computational simulations of thermal bridges. More specifically, the proposed method employs several measurement tools such as HOBO data logger to record the actual conditions of indoor and outdoor room temperature and thermal cameras to identify the surface temperature of selected building junctions. The actual datasets are then integrated with the simulation workflow developed in BES tools. This study ultimately enables architects not only to identify potential thermal bridges on existing building façades but also to support material and geometric exploration in early design phase.

FaÇade system design process on the basis of energy simulation tools

The façade is not only the outer layer of a building that brings aesthetic beauty to the structure but also serves the function of regulating temperature, natural lighting, and ventilation for the building. Belonging to the group of passive design solutions aimed at achieving energy efficiency for the building, optimizing façade design has increasingly garnered attention and investment of time and resources from investors and architects alike. With the aid of building energy simulation tools based on heat exchange calculations and Computational Fluid Dynamics (CFD) modeling, the authors have developed a process to optimize façade systems based on parameters such as position, orientation, ventilation area, and materials. This calculation process will serve as a valuable tool for architects in implementing their design ideas, helping achieve optimal results in terms of aesthetics and energy savings for the building. The paper presents the calculation process and its application to a two-story wooden modular building at the Institute of Building Science and Technology.

An empirical model of a split-type inverter air conditioner for building energy simulation

The increasing adoption of air conditioning prompted by climate change and the fourth Industrial Revolution is poised to greatly influence the demand for energy consumption, especially in rapidly developing economies situated in the planet's hottest regions. The overwhelming majority of air conditioners (ACs) in use today around the globe are split systems, and the inverter technology is taking over the market for this kind of equipment, so it is extremely important for researchers to be able to simulate inverter air conditioners (IACs) behavior properly and demonstrate its energy efficiency benefit to help reduce energy consumption demand in the world. For energy performance assessment of buildings equipped with IACs, building energy simulation (BES) tools usually employ empirical models based on data provided by manufacturers to be able to simulate performance, however, even with the most updated standards in place, the data provided is not enough to provide a robust empirical model. The models available to predict IAC performance today are not well suited to be integrated with BES tools therefore this work aims to fulfill this gap by providing an empirical model built by adapting a methodology suggested by ASHRAE and based on 96 experimental tests variations to be able to simulate IAC performance more accurately, considering a wide range of temperature and humidity conditions and different compressor rotation speed values (30 Hz, 60 Hz and 90 Hz). All tests were carried out in a psychrometric calorimeter working according to an ISO standard and following the Air-Enthalpy Method. The paper provides a comprehensive breakdown of the calculation procedures used to ascertain and forecast total cooling capacity, sensible cooling capacity, and energy efficiency ratio, along with their associated uncertainties. This detailed explanation aims to facilitate the replication of the methodology by fellow researchers. The presented model exhibits a strong predictive capability for the tested IAC, boasting mean absolute errors of less than 5 % within an internal wet-bulb temperature range of 10–24 °C and an external dry-bulb temperature range of 25–40 °C. This level of accuracy makes it a suitable candidate for integration into various BES tools.

Predicting Building Energy Demand and Retrofit Potentials Using New Climatic Stress Indices and Curves

Building energy requalification in Italy and Europe has been much discussed in recent years due to the high percentage of existing buildings with poor energy performance. In this context, it is useful to obtain a user-friendly and fast tool to predict the thermal energy demand (TED) for space conditioning and the related primary energy consumption (PEC) as a function of climatic stress. In this study, the SLABE methodology (simulation-based large-scale uncertainty/sensitivity analysis of building energy performance) is used to simulate representative Italian buildings, varying parameters such as geometry, envelope and HVAC (heating, ventilating and space conditioning) systems. MATLAB® in combination with EnergyPlus is used to analyze 200 buildings belonging to two structural types (multi-family buildings and apartment blocks) built in 1961–1975. Nine scenarios (as-built scenarios and eight retrofit ones) are investigated in 63 climatic locations. A regression analysis shows that the classical HDDs (heating degree days) approach cannot give an accurate prediction of TED because solar radiation is not accounted for. Thus, new climatic indices are developed alongside solar radiation, including the heating stress index (HSI), the cooling stress index (CSI) and the yearly climatic stress index (YCSI). The purpose of our work is to obtain climatic stress curves for the prediction of TED and PEC. Testing of this novel approach is performed by comparison with another building energy simulation tool, showing a low discrepancy, i.e., the coefficient of variation of the root mean square error is between 12% and 28%, which confirms certain reliability of the approach here proposed.

Surrogate Models for Efficient Multi-Objective Optimization of Building Performance

Nowadays, the large set of available simulation tools brings numerous benefits to urban and architectural practices. However, simulations often take a considerable amount of time to yield significant results, particularly when performing many simulations and with large models, as is typical in complex urban and architectural endeavors. Additionally, multiple objective optimizations with metaheuristic algorithms have been widely used to solve building optimization problems. However, most of these optimization processes exponentially increase the computational time to correctly produce outputs and require extensive knowledge to interpret results. Thus, building optimization with time-consuming simulation tools is often rendered unfeasible and requires a specific methodology to overcome these barriers. This work integrates a baseline multi-objective optimization process with a widely used, validated building energy simulation tool. The goal is to minimize the energy use and cost of the construction of a residential building complex. Afterward, machine learning and optimization techniques are used to create a surrogate model capable of accurately predicting the simulation results. Finally, different metaheuristics with their tuned hyperparameters are compared. Results show significant improvements in optimization results with a decrease of up to 22% in the total cost while having similar performance results and execution times up to 100 times faster.

Quantitative Evaluation of the Effects of Heat Island on Building Energy Simulation: A Case Study in Wuhan, China

The climate data used for dynamic energy simulation of buildings located in urban regions are usually collected in meteorological stations situated in rural areas, which do not accurately represent the urban microclimate (e.g., urban heat island effect), and this might affect the simulation accuracy. This paper aims at quantitatively evaluating the effects of heat island on a high-rise building’s energy performance based on the microclimate simulation tool ENVI-met and the building energy simulation tool COMFIE. However, the computation of microclimate models is time consuming; it is not possible to simulate every day of a year in a reasonable time. This paper proposes a method that generates hourly “site-specific climate data” to avoid long microclimate simulation times. A coupling method of ENVI-met and COMFIE was developed for more precise building energy simulation, accounting for the heat island effect. It was applied to a high-rise building in Wuhan, China. The results showed that the yearly average urban heat island effect intensity at the height of 3 m was estimated to be 0.55 °C and decreased with height. Compared to the simulation considering the outdoor temperature variation with the height and orientation, using the original climate data collected in rural areas led to an overestimation of the heating load by around 5.8% and an underestimation of the cooling load by around 8.7%. Compared to the weather file at the height of 3 m near the north facade neglecting the temperature variation along the height, the heating load was overestimated by 8.2% and the cooling load was underestimated by 10.8%. The methods proposed in this paper can be used for the more precise application of urban building energy simulation.

Evaluating the energy-saving potential of earth-air heat exchanger (EAHX) for Passivhaus standard buildings in different climates in China

The significance of constructing low-carbon buildings has been on the rise because of the growing public concern about global warming and energy prices. The Passive House (or Passivhaus) standard is widely applied throughout Europe to reduce building energy consumption. However, a large percentage of the world’s population lives in regions with not only heating demand in winter but also high cooling and dehumidification energy consumption in the hot and humid summer, such as in several regions in China. The energy consumption of buildings in these regions could not be reduced by designing or retrofitting the buildings to Passivhaus standard only, but applying appropriate low energy cooling and dehumidification technologies are also necessary. The earth-air heat exchanger (EAHX) system is a suitable solution for utilizing low-temperature soil in summer, but the performance of the EAHX system in different climates was not previously evaluated. Thus, this research aims to evaluate the energy-saving potential of the EAHX system in a multi-storey Passivhaus standard building in different cities in China with different climatic conditions. A model of the Passivhaus building with the EAHX system was developed and verified using the Passive House Institute (PHI) prediction tool, PHPP and the commercial building energy simulation tool, IES Virtual Environment (VE). The results have shown that the EAHX system achieved an annual building energy load saving of up to 12.8 kWh/m2 in regions with hot and humid summers and cold winters, such as Beijing and Shanghai. The present study shows the potential of the addition of the EAHX in enhancing the building performance to satisfy the Passivhaus standard in hot and humid climates.

Multi-domain model-based control of an adaptive façade based on a flexible double skin system

Adaptive envelopes have the potential to significantly reduce energy use in buildings while ensuring high performance. These envelopes interact with multiple interconnected domains, such as daylight, indoor air quality, thermal comfort, and energy use, which can often conflict with one another. Identifying and developing suitable control strategies that can optimally manage the envelope’s impact on many domains and avoid sub-optimal operations is an open challenge. Conventional approaches commonly adopted in buildings and building envelope control based on schedules or relatively simple decision trees may be unable to tackle the dynamic behaviour of adaptive envelopes. Due to their complexity, more advanced control approaches based on simulation-informed decision-making are scarce in both research and practice. In this work, we propose a multi-domain model-based control (MBC) algorithm for an adaptive façade concept based on a flexible Double Skin Façade (DSF). The proposed method, which aims for a balanced performance over different comfort domains and energy use, employs a co-simulation approach where the DSF is modelled in a Building Energy Simulation (BES) tool and the control algorithm to manage the simulation and optimize the control of the façade is developed in a generic programming language. To the best of our knowledge, this is one of the first attempts to design and demonstrate the effectiveness of a simulation-informed control strategy that can handle and optimise the behaviour of a complex façade by considering multiple performance objectives. The innovation of this approach lies in the MBC algorithm that selects the optimal façade state among over seventy possible states at each timestep, the practical demonstration of the feasibility in a BES tool, and the complexity of the controlled façade system. To verify the effectiveness of the proposed control approach, we compared the innovative MBC to more traditional control strategies, such as schedule and rule-based controls, revealing how it enabled the façade to achieve a better performance in all the analysed domains. By applying the MBC to three different year periods, we showed that the energy and environmental performance was within the selected comfort criteria for all the domains for>80% of the occupied hours, and an energy reduction of up to 70% was simultaneously obtained if compared to more traditional approaches. The control approach presented in this study and the simulation method employed can be used not only to improve the performance of advanced adaptive façades by providing an effective solution to the challenge of balancing multiple conflicting performance domains but also for more conventional building envelope systems that exhibit a certain degree of dynamic behaviour.

A statistical quantitative analysis of the correlations between socio-demographic characteristics and household occupancy patterns in residential buildings in China

Occupancy patterns that describe occupant presence or absence can result in a significant impact in energy use of residential buildings; thus, they are critical to the energy modeling of residential buildings. Occupancy patterns currently embedded in building energy simulation tools or recommended in the standards are uniform, ignoring that different occupancy patterns exist for households with different socio-demographic characteristics. Consequently, this study objective is to extract the typical occupancy patterns of households and quantify the correlations between household occupancy patterns and socio-demographic characteristics based on statistical approaches. Clustering analysis was used to identify representative types of occupancy patterns based on the occupancy profiles of daily occupant presence data. Then, correlations between occupancy patterns and household characteristics were identified by investigating three statistical methods: nonparametric analysis, decision trees, and association rule mining. To illustrate the quantitative analysis, datasets collected by a large-scale questionnaire survey in two Chinese cities were used for the case study. The results revealed that socio-demographic characteristics, such as occupant age, employment status and household composition, have statistically significantly associated with occupancy patterns. The statistical quantitative analysis can be used to estimate household occupancy patterns to further reduce the performance gap between the simulation and actual energy consumption of residential buildings.

Assessment of Building Energy Simulation Tools to Predict Heating and Cooling Energy Consumption at Early Design Stages

Recent developments in dynamic energy simulation tools enable the definition of energy performance in buildings at the design stage. However, there are deviations among building energy simulation (BES) tools due to the algorithms, calculation errors, implementation errors, non-identical inputs, and different weather data processing. This study aimed to analyze several building energy simulation tools modeling the same characteristic office cell and comparing the heating and cooling loads on a yearly, monthly, and hourly basis for the climates of Boston, USA, and Madrid, Spain. First, a general classification of tools was provided, from basic online tools with limited modeling capabilities and inputs to more advanced simulation engines. General-purpose engines, such as TRNSYS and IDA ICE, allow users to develop new mathematical models for disruptive materials. Special-purpose tools, such as EnergyPlus, work with predefined standard simulation problems and permit a high calculation speed. The process of reaching a good agreement between all tools required several iterations. After analyzing the differences between the outputs from different software tools, a cross-validation methodology was applied to assess the heating and cooling demand among tools. In this regard, a statistical analysis was used to evaluate the reliability of the simulations, and the deviation thresholds indicated by ASHRAE Guideline 14-2014 were used as a basis to identify results that suggested an acceptable level of disagreement among the outcomes of all models. This study highlighted that comparing only the yearly heating and cooling demand was not enough to find the deviations between the tools. In the annual analysis, the mean percentage error values showed a good agreement among the programs, with deviations ranging from 0.1% to 5.3% among the results from different software and the average values. The monthly load deviations calculated by the studied tools ranged between 12% and 20% in Madrid and 10% and 14% in Boston, which were still considered satisfactory. However, the hourly energy demand analysis showed normalized root mean square error values from 35% to 50%, which were far from acceptable standards.

Modelling double skin façades (DSFs) in whole-building energy simulation tools: Validation and inter-software comparison of naturally ventilated single-story DSFs

Building energy simulation (BES) tools offer the possibility to integrate double skin façade (DSF) technologies into whole building simulation through dedicated modules or possible workarounds. However, the reliability of such tools in predicting the dynamic heat and mass transfer processes within the DSFs is still to be determined. Therefore, this paper aims to assess the performance of four popular BES tools (i.e. EnergyPlus, TRNSYS, IDA-ICE and IES-VE) in predicting the thermal behaviour of one-storey naturally ventilated DSF in three different ventilation modes. To evaluate their capability to predict thermophysical quantities, we compared the simulation results with experimental data. The results show that it is not possible to identify a tool that outperforms the others for all the analysed quantities, especially for the cavity air temperature, which is the least accurate parameter in all software due to underestimation of the daytime peak. IES-VE seems to be most accurate for Supply Air and Thermal Buffer modes when shading is deployed, while EnergyPlus appears most accurate for Outdoor Air Curtain mode. When it comes to surface temperatures and transmitted solar radiation, TRNSYS appears to be the best-performing software. In addition, this study investigated the challenges that designers may face when modelling a naturally ventilated DSF using whole-building simulation tools. Moreover, the investigation elucidates the challenges that have a more significant effect on the performance of the BES tools in order to reinforce their reliability.

Cosimulation of Integrated Organic Photovoltaic Glazing Systems Based on Functional Mock-Up Unit

This study presents an approach to simulating building-integrated photovoltaic glazing systems composed of semitransparent organic photovoltaic (ST-OPV) elements. The approach consists of a mathematical cosimulation model based on the energy balance of complex glazing systems, considering heat transfer as conduction, mixed convection, and radiation effects. The cosimulation method is based on a functional mock-up unit (FMU) developed in Python and the building simulation program Domus. This work aims at presenting a cosimulation technique that can be easily applied to building energy simulation tools for the assessment of photovoltaic energy generation in glazing systems. The cosimulation glazing model was verified according to ANSI/ASHRAE Standard 140-2011, and the zone temperature was kept within with a root medium square error (RMSE) of 1.45 °C. The simulated building with an ST-OPV system showed promising results and could be applied to near-zero energy buildings since each 6-m2 glazing has a power generation of around 77 W, equivalent to 9% of available solar resource.

Development of Virtual Human Agents with Different Thermal Preferences for Energy and Thermal Comfort Simulation

Researchers have used building energy simulation tools such as EnergyPlus to evaluate building energy and occupant thermal preference. EnergyPlus is a powerful tool for modeling buildings, However, when controlling the indoor set temperature using EnergyPlus, it is common to assume that all occupants’ thermal preference is same. Therefore, it is difficult to know dynamic personal thermal preferences and to implement occupant centric set-point control. In this study, we propose the various human agents with different thermal preference to realize the reliability of the simulation. First, we make an agent generation algorithm by referring the characteristics of various existing individual thermal preference models. And then, create agents with a virtual thermal preference to suit our needs. Through that, it is expected to allow human agents to feel different preference in one space during simulation. The final significance of the study is to contribute to the evaluation of building energy and thermal comfort closer to reality through the agent created in this method.

가스히트펌프를 이용한 온실 난방 및 배가스 탄산시비 시스템의 경제성 및 온실가스 배출 영향 평가

The area of domestic horticulture continuously enlarged and the heating costs also increased to about 30 ~ 40% of the total operating expenses. To enhance the profitability of production, it is increasingly important to estimate the energy costs of greenhouses properly. This study aimed to compare the economic feasibility and the greenhouse gas emission of greenhouse energy systems according to heating methods with different fuel types. The annual power consumption was calculated by using TRNSYS, a commercial Building Energy Simulation(BES) tool. First, the model validation was performed using the experimental data obtained from the greenhouse that uses a gas engine drive heat pump. The results were compared with the reference kerosene heating case (CASE 1), which is widely used in domestic greenhouses. The analysis showed that gas heating case (CASE 2) has reduced total energy costs by 20% and greenhouse gas emissions by 10%. Whereas, the exhaust gas from gas engine driven heat pump can be used as a CO₂ source for enhancing photosynthesis. Lastly. the results from the GHP heating case (CASE 3) showed a 46% reduction in total cost and a 63% reduction in greenhouse gas emissions.

Estimating cooling loads of Arizona State University buildings using microclimate data and machine learning

In hot arid urban climates, building cooling equipment consumes the greatest share of energy out of all building end-use equipment. For existing buildings, dynamic system-based building energy simulation tools have provided valuable information on the impact of microclimates on cooling energy use. However, such models require in-depth information on building model parameters and often suffer from modeler bias even when sufficient calibration indices are satisfied. This study presents a data-driven approach for predicting the cooling loads of three university buildings in Arizona using simulated microclimate data. A microclimate model ENVI-met generated the input micro-scale weather data for each building. The ENVI-met simulation was validated using in-situ observations during the summer of 2018. Multiple machine learning algorithms were implemented. A final model was selected and used as baseline to predict cooling loads for each building in the dataset. The model predicts chill water tons per square meter using microclimate variables that include mean air temperature, mean absolute humidity, shading levels, and direct shortwave radiation. The black-box model was explained using an advanced machine learning model interpretation library in Python: SHAP. The baseline model predicted cooling loads with a prediction accuracy score of 0.98 using the tree-based algorithm Random Forest. Sensitivity analyses and scenario results showed that cooler microclimates reduced cooling loads for the modeled buildings. The developed framework will be used in future study extensions to explore the impacts of simulated microclimate scenarios generated by ENVI-met.

A Novel Temperature-Independent Model for Estimating the Cooling Energy in Residential Homes for Pre-Cooling and Solar Pre-Cooling

Australia’s electricity networks are experiencing low demand during the day due to excessive residential solar export and high demand during the evening on days of extreme temperature due to high air conditioning use. Pre-cooling and solar pre-cooling are demand-side management strategies with the potential to address both these issues. However, there remains a lack of comprehensive studies into the potential of pre-cooling and solar pre-cooling due to a lack of data. In Australia, however, extensive datasets of household energy measurements, including consumption and generation from rooftop solar, obtained through retailer-owned smart meters and household-owned third-party monitoring devices, are now becoming available. However, models presented in the literature which could be used to simulate the cooling energy in residential homes are temperature-based, requiring indoor temperature as an input. Temperature-based models are, therefore, precluded from being able to use these newly available and extensive energy-based datasets, and there is a need for the development of new energy-based simulation tools. To address this gap, a novel data-driven model to estimate the cooling energy in residential homes is proposed. The model is temperature-independent, requiring only energy-based datasets for input. The proposed model was derived by an analysis comparing the internal free-running and air conditioned temperature data and the air conditioning data for template residential homes generated by AccuRate, a building energy simulation tool. The model is comprised of four linear equations, where their respective slope intercepts represent a thermal efficiency metric of a thermal zone in the template residential home. The model can be used to estimate the difference between the internal free-running, and air conditioned temperature, which is equivalent to the cooling energy in the thermal zone. Error testing of the model compared the difference between the estimated and AccuRate air conditioned temperature and gave average CV-RMSE and MAE values of 22% and 0.3 °C, respectively. The significance of the model is that the slope intercepts for a template home can be applied to an actual residential home with equivalent thermal efficiency, and a pre-cooling or solar pre-cooling analysis is undertaken using the model in combination with the home’s energy-based dataset. The model is, therefore, able to utilise the newly available extensive energy-based datasets for comprehensive studies on pre-cooling and solar pre-cooling of residential homes.