Building Load Research Articles

Method for Determining Design Heating Load of Rural Residential Buildings Considering Indoor Temperature Uncertainty

In rural locations, the application of clean heating technologies requires an appropriate design heating load. However, the variation characteristics of indoor temperatures in rural residential buildings are rarely taken into consideration by traditional techniques of calculating the design heating load, which may result in over- or under-design. As a result, a new method that took the uncertainty of the indoor temperature into account was presented to calculate the design heating load for rural residential buildings. First, for the “part-time, part-space” heating mode in rural residential buildings, an indoor temperature stochastic model was established to generate multiple indoor temperature scenarios; on the basis of this, heating loads under these scenarios were simulated and their probability distributions were counted; lastly, the design heating load was selected from the load probability distribution based on a predetermined confidence level. When the new method and the traditional method were compared, it was discovered that the new method can offer a more thorough guide to the determination of the design load value for the design of heating systems in rural residential buildings, while the traditional method’s result might not satisfy the reliability requirements.

Load Scheduling of Smart Net-Zero Residential Buildings Based on Pandemic Situation

Load scheduling is an effective way of utilizing loads of smart residential buildings according to the preferences of the inhabitants or grid demands, while reducing the cost of energy. This work proposes objective functions for load scheduling to confine the cost of energy within the consumers’ preference range while keeping the load consumption closer to the load demand as possible, to minimize system loss during normal and pandemic condition such as COVID-19 periods, fulfilling the unique features of a net-zero energy building. The proposed objective function is implemented by considering the realistic grid power cost, levelized cost of renewable sources, battery, and incentives offered by the utility system existing in California, USA. In addition to three different types of days such as normal working days, weekends and pandemic situations, brown out power outages are considered as operating conditions. Particle swarm optimization (PSO) is utilized in all considered operating conditions. Two terms that account for the total energy cost savings and the total delayed/scheduled load over a fixed time horizon are formulated as performance indices to illustrate the effectiveness of the proposed objective functions for load scheduling. All of the cases are optimized by the Paticle Swarm Optimization (PSO) and non-optimized systems are simulated in the MATLAB environment. It is evident from the simulation results that the proposed objective function is very efficient in tackling the energy resources, loads and grid power to maximize cost savings and minimize shifting of loads for later hours for normal and pandemic situations in net-zero energy buildings. Moreover, it is equally effective in responding to any emergency situations such as brown out energy crisis situations, which are not considered in the literature so far. In all cases, the performance index also validates the effectiveness of the proposed objective function-based scheduling system for net-zero energy buildings.

Analysis of the factors influencing heat transfer and control strategy optimization for Medium-Shallow array borehole heat exchangers

Medium-shallow array borehole heat exchangers (MSABHEs) exhibit high heat transfer capacity and low initial investments. However, limited research has been conducted on utilizing medium-shallow geothermal energy utilization for heating and cooling. In this study, a heat transfer model for MSABHEs was established. Second, the suitability of the heat transfer model was validated using experimental data. Subsequently, the model was used to analyze the impacts of the borehole spacing, terrestrial heat flow, circulating fluid flow rate, and ground depth on heat transfer in MSABHEs. Finally, an optimization strategy for the circulating fluid was proposed based on dynamic building loads, and the impacts of various circulating liquid control strategies on the heat pump performance were compared. The results indicated that the heat transfer capacity of the MSABHEs increased with increasing borehole spacing. When the spacing was greater than 8 m, thermal interference between the boreholes could be ignored. Under the condition of imbalanced cooling and heating loads, the variable flow strategy could increase the heating coefficient of performance (COP) and cooling COP of the heat pump by approximately 11.12 % and 14.28 %, respectively. Through this control method, the power consumption of the water pump could be reduced from 15840 kW to 8527 kW. These findings could provide a theoretical basis and technical guidance for MSABHE applications.

A numerical and closed-form analytical solution for the global buckling critical load of tall buildings considering soil flexibility: A coupled shear-flexural model

The critical global buckling load is a fundamental parameter governing the performance of tall buildings, however, the existing literature lacks an efficient solution that comprehensively captures the behavior of tall structures with uniform or variable properties. In response to this gap, this study introduces a novel and generalized solution, employing the coupled shear-flexural model. This solution is designed for determining the critical global buckling load in tall buildings subjected to vertical loads with diverse profiles, while also incorporating soil flexibility. The analytical solution, designed for the uniform continuous model, offers a breakthrough by providing graphical representations facilitating the direct determination of eigenvalues based on a single dimensionless parameter. Addressing the unique challenge posed by tall buildings with variable properties, a numerical transfer matrix method based on a Laplacian approach is proposed. This method enables the direct calculation of the transfer matrix for each level, displaying exceptional convergence in just three iterations. The study sheds light on a crucial finding that emphasizes the adverse impact of rotational flexibility on soil, leading to a decrease in eigenvalues as soil flexibility increases. Importantly, the proposed solution methods not only show excellent accuracy but also serve as invaluable tools for performing parametric analyses. Furthermore, they present cost-effective alternatives to exact methods, providing a solid foundation for studying the overall stability of tall buildings. This research contributes significantly to the existing literature by introducing efficient methodologies that improve our understanding and analysis of the behavior of tall buildings under vertical loads.

A predictive model of long-term performance assessment of Ground Source Heat Pump (GSHP) systems in Japanese regions

This study predicts the long-term performance of ground source heat pump (GSHP) systems in different climatic regions of Japan. The research model calculates the dynamic heat load of a typical Japanese building in various cities using their meteorological data, considering different schedules for heating and cooling seasons. The model aims to analyze the performance degradation of the GSHP system after 15 years of operation due to the imbalanced heating and cooling loads. Additionally, it compares ground temperature changes, coefficient of performance (COP) variations, and annual operation times based on different thermal imbalance ratios (TIR) for the case studies. The results demonstrate that TIR serves as an appropriate factor for comprehensively evaluating system aspects, including GHE length, ground temperature, and COP degradation, across various climates. It was found that ground temperatures increased by a maximum of up to 4 °C in cooling-dominated regions due to higher thermal imbalance accumulative loads, while it decreased to around 1 °C in heating-dominated regions. The model also revealed that the COP decreased in the dominant operation for each case study due to induced thermal imbalance and soil temperature change. The thermal imbalance caused the system cooling COP decreased by 0.015/year in the worst case (Okinawa City) and 0.006/year in average for other cases.

Industrial Building Structure Planning With Cranes

Industrial building structure planning complies with standards and regulations, such as Procedures for Earthquake Resistance Planning, Minimum Loads for Building Design, and Specifications for Structural Steel Buildings. Vertical and lateral loads are fundamental to the structure's load flow process. Industrial buildings handle roof, wind, earthquakes, crane, and wall loads. Structural design must consider these elements, including the choice of lateral force-resisting systems such as X-bracing. In determining building loads, including earthquake and wind loads, various parameters such as wind speed, exposure category, and other factors must be considered by standard requirements such as SNI 1727-2020 and SNI 1726:2019. Selection of site class, wind direction, topographic, and ground surface elevation factors are essential in determining wind loads. This research method focuses on supporting structures consisting of purlins and girts in the context of industrial buildings. Purlins, made from hot or cold rolled steel, generally use C and Z profiles. Girts, made from cold or hot rolled steel, have profile variations such as C, Z, or hollow square sections. The girt structure design process determines dimensions, rod sag, internal requirements, and sag rod capacity.

Integrated Systems of Light Pipes in Buildings: A State-of-the-Art Review

Artificial lighting comprises nearly one-third of the total electrical load of buildings, resulting in significant carbon emissions. Reducing the carbon emissions caused by artificial lighting is one of the ways to achieve low-carbon buildings. To meet the demand for high-efficiency, energy-saving, and comfortable lighting, light pipes are increasingly used in buildings. This paper reviews the research and development of light pipes and integrated technology. Sky conditions as a dynamic factor always affect the performance of light pipes. The combination of light pipes and an artificial lighting system can effectively solve this problem. A light pipe can be integrated with a ventilation stack to achieve the ventilation and cooling or heating of a building. A lighting-heating coupled light guide can improve the energy efficiency and sustainability in buildings, such as where antimony tin oxide nanofluid is introduced to absorb additional heat and then provide domestic hot water. The application of a photocatalyst to light pipes can realize air purification and self-cleaning. The use of light pipes does not consume electricity and can reduce the time spent using artificial lighting, thus allowing for power savings. From a whole life cycle perspective, the use of light pipes can be a balance of cost and benefit. In conclusion, such information could be useful for engineers, researchers, and designers to assess the suitability of applying integrated light pipes in different building types and examine the potential of energy and cost savings.

Exploring the distribution of seismic loads in tunnel-form concrete buildings using the probable area method

Equivalent static analysis is a simplified method that is widely used by designers and structural engineers in seismic design codes. However, the accuracy of this approach is affected by various parameters and uncertainty sources. Moreover, its reliability for different seismic demand and performance levels is not well-established, which raises doubts about its application for performance-based analysis. In this study, a new method called the probable area approach is proposed to address the shortcomings of the equivalent static method. The suggested method is based on multi-level response modification factors and probabilistic distribution of corresponding intensities, providing a more reliable estimate of the lateral loads at different levels of the structure compared to the current deterministic lateral forces. To evaluate the effectiveness of the probable area method, the lateral load distribution for three multi-story tunnel-form concrete buildings with 5, 7, and 10 stories under the design basis hazard level (10% probability of occurrence in a 50-year service life of the building, and a return period of 475 years) is computed using both the equivalent static and probable area methods. The attained results show that the equivalent static method is not suitable for estimating the base shear and lateral load distribution of seismic forces and results with an average error of 80% and 60%, respectively, compared to near-exact responses derived from time-history analysis. On the other hand, the proposed method significantly reduces the errors in estimating the base shear and lateral story forces, with maximum error values of 18% and 9%, respectively. These findings highlight the potential of the probable area method to improve the accuracy and reliability of the simplified equivalent static analysis methods.

Photovoltaic-wind-battery and diesel generator-based hybrid energy system for residential buildings in smart city Coimbatore.

The building consumes almost 40% of the energy generated in the building. Investigating the photovoltaic system, wind, battery, and diesel generators for residential buildings can reduce energy utilization. In this work, various energy sources are combined to form hybrid energy sources, which are designed based on the load of the residential building. The Hybrid Optimization of Multiple Energy Resources tool optimizes various energy sources such as photovoltaic (PV), wind, diesel generator (DG), and battery. An investigation on the residential load in the smart city of Coimbatore, Tamil Nadu, India, is being carried out. This article examined the technological and economic feasibility of solar photovoltaic, wind, diesel generators, and batteries combined to form a hybrid energy source (HES). With 2kW of photovoltaic, 1kW wind, 1kW of DG, 1kW of the power converter, and five batteries, system case 1 (photovoltaic/wind/diesel generator/battery model) had the best results in the simulation and was recommended for use in the proposed residential building. As a result, it has a minimum net present value of $14,568 and an energy cost of $0.312/kWh, which is about 39% cheaper than system base cases. The sensitivity and environmental analysis are carried out to analyze the system's feasibility.

A hybrid prediction model of improved bidirectional long short-term memory network for cooling load based on PCANet and attention mechanism

Accurate and reliable cooling load forecasting is a prerequisite for air-conditioning system control and the basis for building-side energy management. Therefore, a hybrid prediction model of an improved bidirectional long short-term memory (BiLSTM) network based on principal component analysis network (PCANet) and attention mechanism (CNN-IBiLSTM-Attention) is proposed to predict the cooling load of large commercial buildings. First of all, the PCANet algorithm is used to analyze the sensitivity of the influencing factors. Then, the hybrid strategy improved whale optimization algorithm (HSIWOA) is used to optimize the hyperparameter of BiLSTM. At last, the performance of the proposed algorithm is verified by using the actual data of two commercial buildings in Xi'an. The results show that using the PCANet algorithm for sensitivity analysis avoids feature redundancy. HSIWOA is suitable for hyperparameter optimization of BiLSTM. Compared with the other three prediction models, CNN-IBiLSTM-Attention reduced the mean absolute percentage error (MAPE) of Building 1 and 2 test sets by 31.55 %, 55.59 %, and 60.58 % and 56.49 %, 60.3 %, and 67.37 %, respectively. The proposed prediction model has superior hyperparameter optimization ability, better model complexity, and stronger generalization ability. Therefore, the proposed prediction model becomes a reliable tool for predicting the cooling load of large commercial buildings.

Impact of future climate change on energy consumption in residential buildings: A case study for representative cities in Japan

This study examines the impact of future climate change on energy consumption in residential buildings in Japan, taking into account both the current and future typical meteorological year (TMY) database. TMY data is widely used in the building sector for energy performance assessments, system sizing, and optimization. To investigate the impact of current and future TMY databases on energy consumption of buildings, the study predicts the cooling and heating loads of residential buildings in four cities of Japan under the current (2020 ver.) and future (2086 ver.) TMY databases. Results show that the use of future TMY database leads to significant changes in air-conditioning heat loads of residential buildings in Japan. It finds that the annual cooling load in residential buildings of four cites of Japan increases by approximately 12% for Tokyo, approximately 9% for Toyohashi, approximately 8% for Osaka, and approximately 7% for Nagoya, respectively, however the annual heating load decreases by approximately 8% for Tokyo, approximately 9% for Toyohashi, approximately 6% for Osaka, and approximately 5% for Nagoya, respectively, compared to the current TMY database. This finding suggests that climate change will have a significant impact on future building energy consumption, and proposes energy-efficient building design as a solution to mitigate the impact of climate change on building energy consumption.

Proposing hybrid prediction approaches with the integration of machine learning models and metaheuristic algorithms to forecast the cooling and heating load of buildings

Accurate prediction of heating and cooling loads in residential buildings is crucial for both researchers and practitioners. This study employs advanced forecasting techniques, utilizing support vector regression and extreme gradient boosting methods, enhanced by state-of-the-art metaheuristic algorithms. The metaheuristic optimizers applied include the Satin Bowerbird Optimizer, Ant Lion Optimizer, Artificial Ecosystem-Based Optimizer, Slime Mold Optimizer, Moth-Flame Optimizer, and Particle Swarm Optimizer. These optimizers are strategically used to refine the forecasting models, optimizing their parameters for increased precision. To evaluate the models, a suite of statistical indices is used, including mean square error, root mean square error, Mean Absolute Percentage Error, Mean Absolute Error, Relative Absolute Error, coefficient of correlation, Coefficient of Determination, and Normalized Mean Square Error. This comprehensive analysis assesses the effectiveness of the forecasting methods. The study's findings highlight that the combination of the Satin Bowerbird Optimizer and extreme gradient boosting is particularly effective, achieving high coefficients of determination for cooling and heating load predictions (0.95826 and 0.938048, respectively). This hybrid algorithm shows remarkable performance, with minimal error values and consistent convergence across various datasets. In summary, this research underscores the efficiency of integrating sophisticated machine learning models with metaheuristic optimization techniques, particularly identifying a potent hybrid algorithm for the accurate prediction of heating and cooling loads in residential buildings.

Development of changes to the construction code of rules in part of updated specific electrical loads

THE PURPOSE. Develop changes to the building code of rules regarding updating the specific calculated electrical loads of multi-apartment residential buildings. Perform a comparative analysis of the power of technological connection of multi-apartment residential buildings, calculated according to regulatory and technical documents and according to those developed on the basis of actual measured data with an assessment of the effect of updating specific design electrical loads.METHODS. To achieve this goal, mathematical and statistical methods of analysis were used.RESULTS. Statistical processing of the specific electrical load of apartments in multi-apartment residential buildings in the Moscow region of a cluster of 11–18 floors for twenty-three months of 2021–2023 was carried out, which made it possible to form statistically significant samples by excluding outliers from them and obtaining their basic descriptive statistics. Based on the results obtained, changes were developed to the regulatory and technical document in terms of updating the specific calculated electrical loads of apartments in multi-apartment residential buildings in the Moscow region of the cluster 11 - 18 floors.CONCLUSION. The introduction of updated values of the specific calculated electrical load will lead to the selection of power transformers with optimal loading, which, on the one hand, will reduce the “locked electrical power”, resulting in reduced electricity losses, and on the other hand, will reduce the cost of technological connection.

PERENCANAAN ULANG STRUKTUR GEDUNG LIMA LANTAI ASRAMA HAJI SEMARANG

Due to the increasing number of Hajj pilgrims, the need for accommodating them has also risen. Therefore, a redesign is necessary to enhance the capacity of accommodating the pilgrims. The Analysis and Structural Planning of the Hajj Dormitory Semarang using ETABS software in compliance with the Indonesian National Standard (SNI). The redesign of the Hajj dormitory aims to augment the housing capacity for prospective Hajj pilgrims. The Revised Structural Planning for the Five-Story Hajj Dormitory Building Semarang aims to analyze and redesign the structure of the dormitory. This involves utilizing concrete quality of 24.9 MPa for the upper structure and 30 MPa for the lower structure, with main reinforcement using 420 MPa, while secondary reinforcement uses 280 MPa. The structural analysis approach involves inputting geometric data, loads, and material characteristics, including desired concrete and steel qualities, into the ETABS software. The analysis adheres to the SNI guidelines governing building loads, seismic resilience, and other structural safety factors. The analysis results provide insight into the structural response spectrum, with a mass participation of UX 99.98% and UY 99.19%. Control Base Shear Vdynamic > Vstatic, with Eq Dx > Eq Sx and Eq Dy > Eq Sy, is 8623.266 > 8563.3067 and 7928.0409 > 7722.4166. The maximum story displacement due to dynamic seismic motion in the X and Y directions is 14.33 mm and 15.95 mm. Meanwhile, the story drift with an Inelastic Drift value less than 61.5 mm for safety building permissible deviation. The largest permissible deviations obtained for the X and Y directions are 18.816 mm and 20.554 mm.

Optimal Design Method for Lightweight Buildings to minimize the Cooling Load with Phase Change Materials using Orthogonal Experimental Design

This studypresentsan investigation of the annual cooling load in buildings by analyzingthe influenceof parameters of phase change materials(PCMs)integrated into the envelopes of the buildings. For the use cases, well-knownCases 600 and 650 of ASHRAE Standard 140were considered. Wemodified vertical walls of the use cases incorporating various PCM layers. The impact of variousfactors of PCM layers in four climates was assessed. These factors were the thickness, melting temperature, latent heat of fusion, density, specific heat capacity, and thermal conductivity. The results showed that the variation of the density, latent heat of fusion, and the thickness of PCMs had a high impact on the reduction ofthe annual cooling energy. However, the level of thickness, latent heat of fusion, and density stuck inthe maximum value, whereas the level of thermal conductivity and specific heat capacity stuckinthe minimum value. Generally, during the globaland multi-objective optimization problems, theseparameters may beexcludedfrom the variable settingsexcept for thickness whereby the penalty function can be set. The general thermodynamic pattern of the results concludes that buildings with lightweight envelopes require as much heat storage as possiblepreventing it from theflow ofheat to the surrounding.

Steady- and Transient-State CFD Simulations of a Modified Barra–Costantini Solar System in Comparison with a Traditional Trombe–Michel Wall

Passive solar systems are one of most important strategies to reduce the heating loads of buildings. The Trombe–Michel (TM) wall and its variants are some of the better-known structures in the field of solar systems. An alternative to the TM wall is the Barra–Costantini (BC) system. In the present paper, CFD numerical simulations, both in steady and transient states, of modified BC and TM walls were carried out in the winter season. Different interspace thicknesses were simulated in order to evaluate their effects on the temperature field and air velocity, and the numerical results were compared among them. It was found that the BC system offers greater hot air flow compared with the TM wall; the mass flow rate increased up to 43% in the BC system and up 28% in the TM system when the interlayer thickness was increased by 500%. The transient simulations (100 h simulated) demonstrated that the dynamic response of the BC wall was shorter than that of the TM wall, even when the TM wall was simulated with initial thermal conditions that were more advantageous than those for the BC wall. The BC system reached a periodic stabilized regime within 24 h, whereas the TM system failed to stabilize in 100 h. The results show that for both TM and BC structures, the interlayer thickness scarcely influenced the temperature of the environment reached (the temperature peak increased up to 3–4% as the interlayer thickness was increased by 500%), while larger air speed changes were observed in the BC system in the transient state compared with the TM system. Thus, in the TM system, the outlet air velocity was practically constant as the interlayer thickness was increased; in contrast, the outlet velocity peak increased up to 50% in the BC system. Moreover, the BC wall presented a quicker response to satisfy the ambient thermal loads.

Nearly Zero-Energy Building Load Forecasts through the Competition of Four Machine Learning Techniques

Heating, ventilation and air conditioning (HVAC) systems account for approximately 50% of the total energy consumption in buildings. Advanced control and optimal operation, seen as key technologies in reducing the energy consumption of HVAC systems, indispensably rely on an accurate prediction of the building’s heating/cooling load. Therefore, the goal of this research is to develop a model capable of making such accurate predictions. To streamline the process, this study employs sensitivity and correlation analysis for feature selection, thereby eliminating redundant parameters, and addressing distortion problems caused by multicollinearity among input parameters. Four model identification methods including multivariate polynomial regression (MPR), support vector regression (SVR), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost) are implemented in parallel to extract value from diverse building datasets. These models are trained and selected autonomously based on statistical performance criteria. The prediction models were deployed in a nearly zero-energy office building, and the impacts of feature selection, training set size, and real-world uncertainty factors were analyzed and compared. The results showed that feature selection considerably improved prediction accuracy while reducing model dimensionality. The research also recognized that prediction accuracy during model deployment can be influenced significantly by factors like personnel mobility during holidays and weather forecast uncertainties. Additionally, for nearly zero-energy buildings, the thermal inertia of the building itself can considerably impact prediction accuracy in certain scenarios.

Energy profiles and electricity flexibility potential in apartment buildings with electric vehicles – A Norwegian case study

Energy flexibility in buildings has the potential to reduce the grid burden of neighbourhoods, yet its practical implementation remains limited. This paper presents a data-based case study from Norway, examining the electricity flexibility potential of electric vehicles, within the context of apartment building loads and PV generation. The results highlight the significant electricity flexibility potential in apartment buildings with EVs, where EV charging can be shifted in time by means of a shared energy management system. Energy profiles are presented, showing how EV charging can increase the average electricity use in apartments by a factor of 1.5 and the power use by a factor of 3.5 to 8.6. Furthermore, the study demonstrates how electricity flexibility KPIs of optimised EV charging in apartment buildings are affected by different energy tariffs, PV generation, V2G technology, and the location of the billing meters. The simulated scenarios showed a maximum reduction of peak loads of 45 %, while a maximum of 38 % of the EV charging was covered by PV generation. The study confirms that residential EV charging emerges as a viable frontrunner in the practical realization of end-user flexibility, paving the way for effective solutions in real-life applications.

A Techno-Economic Comparison of a Stand-alone Hybrid System for a Household: A Case Study for the Royal Commission in Saudi Arabia

It’s exciting today to use renewable off-grid sources for driving applications in the home sector (micro-grids). The current tendency is to use renewable energy sources to increase building sector energy efficiency and protect the environment. The economic element and the expense of building these grids is the biggest barrier to these applications. How to best choose the ratings and mix of certain renewable sources based on the location of the house is one of the crucial ways to lower the cost and boost the reliability of these grids. An efficient techno-economical design for a standalone hybrid system is presented in this research. A solar energy using photovoltaic (PV), wind energy, energy storage, and diesel generator are all parts of the system. This design is carried out by a thorough analysis of the techno-economical design of a standalone system to supply the required energy load for a typical residential building for the Saudi Arabian cities of Yanbu and Jubail. Yanbu, which is at latitude 24.0097 degrees north, and Jubail, which is at latitude 27.0726 degrees north, are the two cities’ respective geographical positions. These two cities were chosen for the study of the impact of metrological influences on building performance based on total energy cost, load needs, and energy consumption of the differences in their geographic positions. To improve the performance of the suggested hybrid system, some restrictions were chosen, including Total Annual Cost (TAC) and Net Present Cost (NPC). Results show that the proposed stand-alone hybrid systems can be used to increase the economics and energy efficiency of existing buildings. For Yanbu and Jubail cities, respectively, the optimal integration of a diesel generator, PV array, wind turbine, and energy storage results in a minimum energy cost of roughly $0.483/kWh and $0.487/kWh.

Application of arc elasticity analysis method combined with CO2 storage technology in building load and energy saving retrofit

Analyzing building loads and energy-efficient retrofits is crucial for effective energy management and sustainable development. Strengthening the identification and analysis of factors that impact energy consumption is informative for devising appropriate measures. In order to address the current limitations in analyzing building energy consumption, the present study proposes a combination of the arc elasticity analysis method with CO2 storage technology. By introducing sensitivity and arc mean elasticity indices, the cooling and heating loads of building energy consumption can be analyzed. Subsequently, a CO2 refrigeration system and compression process can be designed to optimize the storage of CO2 and facilitate its role in energy conservation and reduction of energy consumption. Thus, the research aims to fulfill the potential of CO2 in promoting energy efficiency and reducing consumption. The study utilized software simulation and experimental analysis to examine the impact of the proposed method. The findings revealed a positive correlation between the heat transfer coefficient and the building's annual heat load. In addition, the maximum data variation of the building's cold load was a mere 0.05 per cent, and the relative discrepancy between the software simulation and the actual load values was less than 10 per cent. The exterior building's total cold load value showed a difference of more than 25 per cent when cooled with CO2 intervention compared to the non-intervention result. Furthermore, the building's maximum load reduction reached 3.4 per cent when uniformly varying the thickness of the insulation layer from 40 mm to 100 mm. The proposed method demonstrates a positive impact on building energy sensitivity analysis. Its energy loss design for various parts of the building could offer useful references for practical engineering designs.