Large Commercial Buildings Research Articles

Simulation and optimization design of air distribution in a cigarette production factory

Air-conditioning energy consumption accounts for a significant proportion of the total energy usage in large commercial buildings. This study utilizes computational fluid dynamics (CFD) to examine the airflow distribution in a cigarette factory with a high ceiling. Numerical simulations were performed to assess the effectiveness of various air supply methods. Using FLUENT, the researchers analyzed the uniformity of airflow, temperature and humidity fields under summer conditions. The findings indicate that employing an upper supply and lower return air method can decrease air-conditioning energy consumption by 7.25%. Field measurements were carried out to validate the numerical simulations, with the measured values used as initial parameters. The average deviation between the measured and simulated temperature and humidity in critical work areas was within 2%. The comparative analysis of the simulation and measurement results confirms the reliability and effectiveness of airflow organization simulations in large commercial buildings. These results offer a scientific foundation and computational guidance for designing and implementing energy-efficient upgrades to air-conditioning systems in similar industrial facilities.

A novel dynamic predictive control model for variable air volume air conditioning systems using deep learning

Model-based predictive control has proven effective for managing indoor temperature and humidity in variable air volume (VAV) air conditioning systems. However, delays in temperature and humidity adjustments in response to changes in internal and external environmental conditions can impede high-performance operation. This study introduces a dynamic predictive control model for the multi-zone indoor temperature and humidity of VAV systems, designed to predict and control these parameters in real-time. Utilizing the RC network two-node wall structure method and backward finite difference scheme, a multi-zone building model is developed. Coupled with a deep fuzzy cognitive map (DFCM) for temperature and humidity prediction, and controlled by a PID controller, the model dynamically regulates the opening degrees of terminal air dampers and chilled water valves. Implemented on a networked control platform in a large commercial building, the model consistently maintained indoor temperature and humidity within ±0.5 °C and ±0.1 g/(kg dry air) of the target values under varying conditions, demonstrating substantial improvements in stability and operational efficiency. The research enhances the predictability and control of HVAC systems through advanced algorithmic integration, improving real-time indoor climate management in complex building environments. This method offers a practical improvement in HVAC control technologies, which could be beneficial for applications in commercial building management systems, providing a useful tool for supporting energy efficiency and sustainability in modern infrastructures.

ANALYSIS OF ENGINEERING ECONOMICS OF THE NEW ERA COMPARED WITH THE CONVENTIONAL MATERIALS USED IN HVAC DUCT APPLICATION

Selecting suitable materials for air ductwork is essential to ensure efficient airflow distribution and reduce economic costs, as it directly impacts an air conditioning system’s overall performance and functionality. The PVC-coated polyester canvas and foam materials have revolutionized the construction of air ductwork, offering numerous benefits and advantages over conventional materials. For instance, these materials have been successfully used in the construction of air ducts for large commercial buildings, resulting in significant energy savings and improved indoor air quality. Using these new-era materials has design flexibility, durability, energy efficiency, and cost-effectiveness. This research has provided a comprehensive analysis of the economic cost of the new era of air ductwork compared with conventional air ductwork in Thailand, and the thorough assessment of total service life costs has improved on the financial implications of adopting this new technology. The economic analysis of PID ducts reveals their cost-effective excellence compared to other investment types. The benefit-cost ratio of 4.73 and a payback period of 1.48 years show the financial advantages offered by PID ducts. With their affordability, efficiency, and simplicity, PID ducts emerge as the most attractive option for investors seeking long-term profitability and sustainability in construction projects.

Modelling and performance evaluation of a parabolic trough solar water heating system in various weather conditions in South Africa

In recent years, various energy sources and methods have been used to heat water in domestic and commercial buildings. Water heating methods for large households or commercial buildings include electrical heating elements, gas heaters, and solar energy (solar concentrators, flat plate collectors, evacuated tube collectors, etc.). In recent decades, the focus of water heating has shifted to solar energy, which is abundantly available in most African countries. The weather condition of a region has a huge impact on the system's performance. South Africa is characterised by four different weather seasons (winter, spring, summer, and autumn), unlike most African countries. Therefore, this study focuses on the impact of weather seasons on the system's performance for water heating through the combined use of solar energy and solar concentrator techniques. The system performance was modelled by using Matlab Simulink®, where historical weather data for Pretoria, South Africa, was fed into the model. Based on the weather data input, the system behaviour varied per season due to the change in solar intensity. The average outlet temperatures of the absorber, in the order of magnitude, were 333, 332, 328, and 325 K during the autumn, summer, spring, and winter seasons, respectively. Similarly, the average storage tank temperatures, in the order of magnitude, were 366, 364, 363, and 360 K in spring, summer, autumn, and winter, respectively. From this study, it is concluded that the different weather seasons in South Africa, have a direct impact on the performance of the system. Irrespective of the season, the system produced the required volume of hot water required throughout the year.

Unveiling overlooked aspects of model predictive control for building air conditioning systems

The ongoing research on model predictive control (MPC) for building air conditioning systems predominantly centers on improving the predictive capabilities of system models. In this paper, the impacts of three additional pivotal factors on MPC performance are assessed by examining a generic MPC design for a typical variable air volume (VAV) system that serves large commercial buildings. The three factors encompass the nuanced reformulation of optimization, the judicious relaxation of constraints, and the meticulous tuning of parameters. Detailed case studies with an integrated Modelica and EnergyPlus model of the US Department of Energy's Commercial Reference Building are conducted. The results confirm that the optimization formulation, along with relaxation methods, significantly affects MPC performance in terms of energy savings, zonal thermal comfort level, and computational demand. They also reveal that the impact of the MPC control parameters on the energy savings and thermal comfort may vary by season and can be non-monotonic.

Evaluation of the Demand Flexibility Potential Through Joint Optimization of Building Thermal Response and Indoor Air Quality in Commercial Buildings

Abstract Large commercial buildings may display demand flexibility, which reduces electric energy expenses for the building owner and carbon emissions from grid operations, provides distributed energy resources, and increases the penetration of renewable energy sources. Demand-controlled ventilation (DCV) and building thermal mass control can individually and jointly provide such flexibility. The performance and financial payback of these technology options can be dramatically improved if based on hourly electric prices and carbon emissions rates. In this study, a modeled but actual large office building, simulated using New York City hourly electric prices, hourly CO2e emissions rates, and weather data for the summer 2019 cooling season is based on these dynamic driving parameters. A joint optimization of a building’s thermal mass and indoor CO2 content is presented. Superior energy savings and carbon emissions reductions are found for the joint optimization scenario when compared to both the baseline operation and individual optimization of building thermal mass and indoor CO2 content. These findings motivate the development of a real-time joint control system that utilizes closed-loop model predictive control (MPC) to optimally harness both sources of demand flexibility, a system that would require the future development of forecasting algorithms for external and control-oriented system models.

Solar Power Generation System at Household Scale

Solar power plants are renewable energy systems that utilize sunlight as a power source to generate electricity. The conversion of light energy into electrical energy is achieved through the photovoltaic effect. The process of converting solar energy into electrical energy is made possible by photovoltaic solar panels, which convert solar light energy into electricity. This technology began to be widely known and began to be widely used. Its use is widespread, ranging from large factories, office buildings, commercial buildings, shopping centers, hospitals, hotels, and households The purpose of this study is to know and analyze household-scale solar power generation systems. The method used in this study is a literature review, data used in the form of articles in electronic databases such as Google Scholar, in the period 2006 to 2023. The results showed that solar power plants (PLTS) are the right choice to overcome the energy crisis in Indonesia. Solar Power Plant Energy (PLTS) is abundant sunlight. Solar Power Plant System (PLTS) at the household level using Solar Home System (SHS) which is increasingly adopted in Indonesia (especially in households). The implication in this study is to provide a deeper understanding of household-scale solar systems, including the associated advantages and challenges. This can help individuals, institutions, and governments make better decisions regarding the use of renewable energy.

Prediction of sound and vibration levels from heavy-hard impacts in a cross laminated timber—Concrete hybrid building

Heavy-hard weight impacts in fitness spaces often result in elevated noise and vibration levels, which can lead to complaints in sensitive adjacencies. Previous works have demonstrated a method to predict the resulting noise and vibration levels in fitness space adjacencies. This method involves the analysis of an in situ transfer function in combination with laboratory force data and sound pressure level measurements from a shotput dropped on a calibrated reference rubber impact sheet. It was originally developed for use in concrete structures, which are typical for large multi-residential, commercial, and mixed-use buildings that commonly feature fitness amenity rooms or commercial gyms. The growing popularity of mass timber as a sustainable building material provides an opportunity to expand upon the previous research by examining the validity of the prediction method in another structural type. This paper serves as a case study of heavy-hard impacts in a residential hybrid mass timber structure, where weight drops from a fitness center at the top level of the concrete podium were disturbing apartment residents occupying the first CLT level directly above.

Experimental study on combustion characteristics of billboard materials

Billboards are permanent facilities in large commercial buildings, indoor and outdoor public places, and in fire accidents, billboards will become the main cause of the expansion and spread of fires. To reduce the fire accidents caused by the burning of billboards, this paper conducted experimental tests on 12 commonly used billboard material types of Polyethylene glycol terephthalate (PET) and Polyvinyl chloride (PVC). Among the 12 materials, only PVC4 belongs to the range of flame-retardant materials. PVC6 has the lowest calorific value, less heat release, and a stronger fire effect than other materials. The flammability experiment shows that the ignition time of the material is positively correlated with the combustion height under the same ignition method, and the ignition time is also positively correlated with the combustion width. Under the same ignition time conditions, the flame aspect ratio using edge ignition is greater than or equal to the flame aspect ratio using surface ignition, and the fire hazard is greater. It is necessary to avoid the presence of combustibles around 250 mm to cause fire spread. The monomer combustion experiment shows that the flame spread area of PVC material is much larger than that of PET material. Among all materials, the most dangerous is PVC6, which releases the largest CO concentration and the fastest rate after combustion, produces the most flue gas within 100 after combustion and has poor flame-retardant performance. The combustion of all advertising materials releases less CO and CO2 concentration, which can cause physiological adverse reactions in the human body but will not cause death.

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.

A data-driven framework for thermal comfort assessment method based on user interaction

The rapid development of smart home systems requires a transition of thermal comfort studies. A feasible, user-friendly, consumer-oriented thermal comfort assessment method for large-scale residential applications is extremely urgent. However, most studies of thermal comfort models have focused on large commercial buildings. The application and deployment of such methods often rely on multiple prerequisites like relatively steady thermal conditions, regular energy consumption schedule, and additional sensor information. While situations are different in residential scenarios. In this paper, over 100 thousand air conditioner users were analyzed and a fundamental but overlooked question in thermal comfort research was established. In the household environment, the thermal condition is more complicated. Difference in age, gender, and usage preference leads to diversity in individual thermal comfort. Furthermore, introducing more sensors for better comfort prediction performance is not acceptable both from the cost perspective and the user perspective. To bridge the above missing gap, a novel thermal comfort assessment framework combining user interaction and metric learning was constructed. The proposed framework can be used to construct thermal comfort assessment systems by exploiting user interaction actions, which is a low-cost alternative and complementary to the traditional methods based on thermal equilibrium. The ease of construction makes the framework easy to integrate into smart home systems, addressing the difficulty of applying thermal comfort studies in residential scenarios.

Deployment of real-time building automation system-integrated inverse-model-based fault detection and diagnostics algorithms

The complex operation of HVAC systems in large commercial buildings warrants regular implementation of advanced analytical approaches to operations and maintenance, and subsequent corrective measures to improve and maintain optimal energy performance. Despite the established capabilities of data-driven fault detection and diagnostics (FDD) to identify suboptimal controls policies and mechanical faults resulting in poor energy performance, few attempts have been made to deploy scalable solutions around these approaches. Furthermore, real-time BAS-integrated FDD methods are predominantly rule-based, offering limited insights to faults with gradual negative impacts to energy performance. This paper demonstrates the application of various established data-driven, inverse-model-based FDD methodologies in a BAS-integrated environment. Traditionally implemented sparingly, the novelty of recursive and automatic execution of advanced FDD methodologies, facilitated through a direct data pipeline to an existing BAS, capitalizes on the BAS’s real-time monitoring capabilities to enable continuously refreshed inverse model generation that can capture the gradual degradation of building performance, and provide up-to-date actionable visualizations and key performance indicators (KPI) to building operators. Since deployment, the application has successfully identified a scheduling fault on two separate occasions in a case study building in Ottawa, Canada, and the visualizations were presented to the building operators who resolved the issues.

Transfer Learning Prediction Performance of Chillers for Neural Network Models

Building automation systems installed in large commercial buildings record sub-hourly measurements from hundreds of sensors. The use of such large datasets are challenging because of missing and erroneous data, which can prevent the development of accurate prediction models of the performance of heating, ventilation, and air-conditioning equipment. The use of the transfer learning (TL) method for building applications attracted researchers to solve the problems created by small and incomplete datasets. This paper verifies the hypothesis that the deep neural network models that are pre-trained for one chiller (called the source chiller) with a small dataset of measurements from July 2013 could be applied successfully, by using TL strategies, for the prediction of the operation performance of another chiller (called the target chiller) with different datasets that were recorded during the cooling season of 2016. Measurements from a university campus are used as a case study. The results show that the initial hypothesis of this paper is confirmed.

Prediction model of the large commercial building cooling loads based on rough set and deep extreme learning machine

Efficient prediction of building air conditioning cooling loads contributes to optimizing and controlling heating, ventilation, and air conditioning (HVAC) systems. In this paper, the K-means++ algorithm is used to classify the air conditioning cooling loads to determine the number of discretization intervals and influencing factors of cooling loads. Then, the rough set (RS) theory is used to screen out the critical influencing factors of cooling loads to reduce the input dimensions of the prediction model. Finally, the particle swarm optimization (PSO) algorithm is used to optimize the weights and thresholds between input layers and hidden layers of the deep extreme learning machine (DELM) neural network. Based on the above methods, a K-means++-RS-PSO-DELM prediction model is established for building air conditioning cooling loads. In this paper, a total of 50 large commercial buildings are investigated to find the critical influencing factors of air conditioning cooling loads. Data from a large commercial building in Guilin is collected to train and test the proposed model. Meanwhile, the comparisons between the proposed model and the K-means++-RS-BP, the K-means++-RS-PSO-BP, the K-means++-RS-Elman, the K-means++-RS-PSO-Elman, the K-means++-RS-ELM, the K-means++-RS-PSO-ELM, and the K-means++-RS-DELM models are conducted to validate the applicability of the prediction model. Results show that the prediction deviation of the proposed model is 1.0016 and 0.9942 in short-term and medium-term predictions, which are smaller than that of other prediction models. The proposed model has a strong generalization ability to predict the air conditioning cooling load in large commercial buildings, which is beneficial for online optimal control of the HVAC system.

Research on the organization of public space relations in commercial complex buildings in the context of big data

Abstract As an emerging type of large commercial building, commercial complexes play an increasingly important role in contemporary urban development and construction trends. In order to better study the organization of public space relations in commercial complex buildings, this paper selects eight commercial complexes as sample cases to analyze their spatial design in terms of both content public space and external public space based on big data technology. Regarding the design of the commercial complexes’ internal public space movement, the highest accessibility degree reached 95.3%, while the average was 82.5%. Regarding visibility, the highest level was 96.0%, and the average was 83.3%. For large complexes, better spatial relationships can be designed to improve the circulation degree and thus enhance customers’ consumption experience. Regarding the convenience of traffic connection to the external space of commercial complexes, an average of 41.4% of customers consider it very convenient, 23.1% consider it relatively convenient, 27.9% consider it somewhat inconvenient, and 18.0% consider it very inconvenient. Commercial complexes can effectively attract more repeat customers if they can improve the convenient design of transportation connections. The study of public relations of commercial complexes based on big data helps to discover the defects of their public space design and plays a significant role in optimizing spatial design and relationship organization and strengthening their integrated functions.

Seismic isolation analysis and design of a plane irregular building structure in high earthquake intensity area

Taking the large commercial complex building of Changzhou Golden Eagle Plaza as the research object, this paper analyzes various parameters of typical special-shaped space structure under different isolation schemes under the conditions of earthquake protection, rare earthquake and extremely rare earthquake, compares three isolation layer design combinations of rubber isolation bearing, lead isolation bearing, rubber isolation bearing + lead isolation bearing, and studies the influence of different isolation layer arrangement schemes on the steel core and mass center of the building structure. The results show that the reasonable isolation design can obtain the reasonable arrangement pattern and rule of the plane irregular building structure in a high seismic intensity area by comparing and analyzing the arrangement schemes of different types and forms of isolation bearings in the isolation structure model.

A comparison of optimal peak clipping and load shifting energy storage dispatch control strategies for event-based demand response

Buildings, specifically large commercial buildings, are key contributors to the increasing electrical energy demand that is taxing the reliability of an ageing U.S. power grid. Through utility sponsored demand response programs and electrical energy storage systems, large buildings can simultaneously save money on their electricity bill and improve power grid reliability – with little to no change in their operations. Few studies have explored demand response benefits and appropriate control strategies for large commercial buildings. In this study, optimal peak clipping and load shifting control strategies of a Li-ion battery energy storage system are formulated and analyzed over 2 years of 15-minute interval demand data for a large commercial building in the Southwest United States. Furthermore, this analysis assesses the discounted payback period of a Li-ion battery energy storage system while considering cases with and without enrollment in the local utility’s event-based demand response program. Degradation in the Li-ion battery energy storage system’s rated power and capacity are considered throughout this analysis. Key findings in this study show that enrollment in event-based demand response can provide a reasonable (<10 years) discounted payback period of Li-ion battery energy storage systems.

Hybrid prediction model for cold load in large public buildings based on mean residual feedback and improved SVR

In order to control the energy efficiency of air conditioning systems and design energy management strategies, accurate and effective prediction of building cooling demands is a crucial step. We propose a time-granular residual feedback-based and improved sparrow algorithm for optimizing support vector regression models (TGRF-ISSA-SVR). To begin, select the input features using Random forest. Second, a method (TGRF) is proposed to calculate and feedback the mean residual with time granularities of hour, day, and month. Then, three strategies are used to improve the Sparrow Algorithm (ISSA) and apply it to SVR parameter optimization. Finally, the measured data of a large commercial building in Xi'an are used for experimental verification. The RMSE of TGRF-ISSA-SVR is 4.33 and the MAPE is 0.66, indicating that the model has low error. The RMSE and MAPE of TGRF-ISSA-SVR are 75% and 74% lower than those of TGRF-SVR. The RMSE and MAPE of TGRF-SVR are 37% and 38% lower than those of SVR, demonstrating that the improved model has accuracy improvement. In addition, compared with models such as LSTM and GRNN, the TGRF-ISSA-SVR model has higher prediction accuracy and shorter prediction time, so TGRF-ISSA-SVR can provide effective support for load prediction of large public buildings.

Soft omega joint for selective weakening of core walls in large precast commercial buildings

Commercial malls and similar large low-rise multi-storey building typologies are often constructed employing precast frame structures. Wall cores surrounding stairs and elevators often occupy a small area if compared to the frame area of large floors. Generally, these cores need to be decoupled from the frame structure, which is designated as the lateral load resisting system, given that, if rigidly connected to the frame, their high stiffness would draw seismic actions not compatible with their capacity or with economical/feasible foundation systems. The necessary core decoupling from the frame is hence generally made by horizontal structural joints of considerable width to avoid pounding, given the peculiar lateral flexibility of precast frame structures. However, these large joints are in reality highly complex, since they need to be compatible with internal finishes, pedestrian distribution and often fire compartmentation requirements. The present paper proposes an alternative to the use of decoupling frame-core joints consisting in deformable vertical joints made by omega-bent aluminum sheets and insulation strips to be inserted during production in the wall elements. The proposed omega-profiles, while avoiding the use of horizontal joints and keeping the fire compartmentation capacity of the core walls, selectively weaken the wall elements, lowering their stiffness and allowing for lateral deformation capacities compatible with that of frames. The mechanical behaviour of the proposed joint is assessed by lateral cyclic tests on full-scale wall panels with inner vertical voids surrounded by a solid frame connected with mechanical devices to the foundation beams. The comparison with the test results of a benchmark precast wall not provided with the proposed joints allows to quantify the enhanced flexibility and displacement capacity of the wall with omega joints and to interpret the role of the joints in modifying the resistance mechanism of the wall subjected to lateral loads. Moreover, the results of a non-linear numerical model based on an ideal joint behaviour allow to indirectly highlight the soft joint contribution to the wall behaviour under lateral load.

Statistical mechanics of thermostatically controlled multizone buildings.

We study the collective phenomena and constraints associated with the aggregation of individual cooling units from a statistical mechanics perspective. These units are modeled as thermostatically controlled loads (TCLs) and represent zones in a large commercial or residential building. Their energy input is centralized and controlled by a collective unit-the air handling unit (AHU)-delivering cool air to all TCLs, thereby coupling them together. Aiming to identify representative qualitative features of the AHU-to-TCL coupling, we build a simple but realistic model and analyze it in two distinct regimes: the constant supply temperature (CST) and the constant power input (CPI) regimes. In both cases, we center our analysis on the relaxation dynamics of individual TCL temperatures to a statistical steady state. We observe that while the dynamics are relatively fast in the CST regime, resulting in all TCLs evolving around the control set point, the CPI regime reveals the emergence of a bimodal probability distribution and two, possibly strongly separated, timescales. We observe that the two modes in the CPI regime are associated with all TCLs being in the same low or high airflow states, with an occasional collective transition between the modes akin to Kramer's phenomenon in statistical physics. To the best of our knowledge, this phenomenon has been overlooked in building energy systems despite its direct operational implications. It highlights a trade-off between occupational comfort-related to zonal temperature variations-and energy consumption.