Commercial Office Buildings Research Articles

Digitized fuel load survey in commercial and university office buildings for fire safety assessment

Fuel load significantly affects fire development in compartments, and its design value derives from the statistical results of numerous surveys. This work enhances the fuel load database by employing a digitized survey method to assess 27 university and commercial offices in Hong Kong and Mainland, China. The results revealed significant differences in fuel load densities: university offices averaged 382 MJ/m2 (SD: 297 MJ/m2) and commercial offices averaged 1804 MJ/m2 (SD: 1319 MJ/m2). Moreover, it proposed an online questionnaire method to overcome onsite access limitations. Furthermore, it identified higher fuel load densities in commercial offices due to higher paper-made content and greater occupancy density than university offices. The fuel load of university offices was lower than that in previous surveys and design codes, while commercial fuel load was higher. Notably, there is an increasing tendency of fuel load density and plastic combustible composition over the years. Additionally, it considers that Gompertz distribution better fits cumulative probabilities of fuel load density data with a small sample size. Overall, it provides a valuable database for future fire scenario design, fire codes edition, and fire safety assessment and discusses future collaboration with AI applications.

Evaluating Annual Sun Exposure in LEED v4 for Commercial Office Buildings: Inclusion of Annual Glare to Enhance the Occupant Visual Performance and Comfort

ABSTRACT LEED is one of the most widely adopted building benchmarks globally, and it encompasses multiple rating criteria for performance. However, such criteria typically focus on reducing energy consumption and may disregard providing a high level of occupant satisfaction. LEED focuses on a desk height evaluation of Spatial Daylight Autonomy and Annual Sunlight Exposure to predict the daylight availability and probability of glare, respectively, which may disregard an occupant’s visual comfort at eye level. This paper proposes a more comprehensive approach to daylighting performance evaluation in a commercial office space that incorporates Annual Glare – vertical-eye level glare evaluation, in addition to Spatial Daylight Autonomy and Annual Sunlight Exposure. A comparison between Annual Sunlight Exposure and Annual Glare is established to understand the criteria missing in LEED that evaluates discomfort glare and improves the visual performance and comfort of the occupants significantly.

Advanced Decision- Making Framework for Sustainable Energy Retrofit of Existing Commercial Office Buildings

In the background of rising global initiatives to combat climate change and promote better energy management, retrofitting of current structures has become a major strategy. This abstract and literature review aims at presenting a full review of a decision-making approach that can be used in choosing sustainable options for energy retrofitting. This particular framework is designed to help guide retrofit decision-making due to the many issues that surround the process by providing an economic as well as an environmental and social context. Energy retrofitting means improving the existing structure or complex to decrease its energy intensity and have a negative impact on the environment. As stated earlier, it is a critical process for attaining sustainability goals and advancing the performance of buildings. But the decision on which retrofit measures should be implemented can be rather difficult because of the availability of a vast number of technologies and because more factors must be considered, such as cost and energy savings. The decision-making framework provided in this article aims at categorizing retrofit selection into this list of features and views the current article as a way of making the process more efficient. It encompasses the life-cycle cost analysis (LCCA), the economic valuation, and the multi-criteria decision analysis tools and criteria. These tools facilitate the evaluation of financial viability, the advantages of climatically retrofitting, and the environmental effects of distinct retrofitting choices. The above-mentioned framework is exemplified with a case study of an office building retrofit project. The following case includes an example of the application of the mentioned framework and illustrates the explanation of how it helps to make decisions. Due to the consideration of costs, energy, and environmental performance, the framework enables the stakeholders to choose appropriate retrofit measures. Other than the case study, the framework also focuses on the need to establish a sustainable energy retrofit DSS. Thus, the DSS supplements collected data on building performance, retrofit technologies, and economic aspects, which can be accessed by the stakeholders in real-time and with built-in facilities for scenario analysis. This system improves decision-making since retrofit results can be monitored and evaluated frequently. Both tools and strategies for the decision-making of sustainable energy retrofits bring a logical and systematic approach aimed at neutralizing the decision-making challenges related to retrofit selection. By applying economic, environmental, and social factors in the decision-making process, the framework assists the stakeholders in reaching energy efficiency and sustainability goals. The development of another strong decision-support system also improves the working of the framework and checks that retrofit projects are done efficiently and are strategic to the general goal of sustainable development.

Green Strategies During Lower of Occupancy: The Best Practices of Facilities Management for Energy Optimization in Commercial Office Buildings

Green Strategies During Lower of Occupancy: The Best Practices of Facilities Management for Energy Optimization in Commercial Office Buildings

Exploring factors influencing the agglomeration of implicit consumption space: A comparison with explicit consumption space

The implicit development of consumption space in buildings relying on the O2O business mode is a new form of urban renewal under experience economy and e-commerce. The concept of implicit consumption space emerged as a result. There is a lack of research on implicit consumption space and this study aims to fill this gap. Using POI data and spatial analysis methods, the paper studies the clustering characteristics and influencing factors of the explicit and implicit consumption space in the Old City of Nanjing to provide practical implications for urban renewal, and finds that the implicit consumption space grows fast and has strong clustering, and its layout basically follows the traditional commercial location theory. Commercial districts, office buildings and traffic accessibility are the common influencing factors for the distribution of explicit and implicit consumption spaces. Explicit and implicit consumption space influence each other in layout, while implicit consumption space has a stronger dependence on explicit consumption space. The influencing factors for the agglomeration of explicit and implicit consumption space have spatial variations. The implicit consumption space is an important path for the bottom-up renewal of the old city, and also provides an opportunity for urban function optimization, innovation, and entrepreneurship.

Climate risk assessment of buildings: An analysis of operating emissions of commercial offices in Australia

Building climate risk assessment involves benchmarking a building‘s energy use intensity against decarbonisation pathways to mitigate the impacts on climate change. Various climate risk assessment tools and frameworks are used for commercial buildings in different jurisdictions. This study reviewed 20 climate risk assessment tools based on their availability, application and underlying framework. Due to being the only available tool that sets global targets with local benchmarks in Australia to assess the risks associated with energy use in commercial offices, Carbon Risk Real Estate Monitor (CRREM) was chosen. Using the CRREM tool, 431 commercial office buildings across Australia are benchmarked and the results were compared based on climate zones, jurisdictions, locations and sustainability ratings. Results revealed that 58.2% (n = 251) of buildings met the energy use targets. Most energy-efficient offices are found in the VIC state, within the Central Business District of Melbourne. Conversely, improvement is needed in Darwin, Northern Territory. The buildings at climate risk (41.8%, n = 180) need to reduce their on-site energy intensity by an aggregated net performance gap of 5484 kWh/m2/year in total. This significant shortfall highlights the critical imperative for all buildings to swiftly align with decarbonisation pathways, ensuring they meet 2050 climate change targets.

Large-scale simulation-based parametric analysis of an optimal precooling strategy for demand flexibility in a commercial office building

Grid-interactive efficient buildings (GEBs) optimize their operations by using flexible load techniques, such as precooling, to reduce peak electricity demand. However, precooling effectiveness depends on factors like building properties, weather, and utility rates. Therefore, investigating the impacts of these condition-specific factors is crucial, especially when considering precooling strategies that utilize thermal mass in commercial buildings. In this paper, we first devised a novel heuristic control approach that incorporates parameterized optimal precooling thermostat schedules to enhance demand flexibility in a commercial office building. Subsequently, we conducted a thorough performance evaluation of this control strategy. Utilizing the DOE medium-sized office building as the virtual testbed, we showed that the parameterized schedule effectively approximates model predictive control and requires drastically reduced computational overhead. On top of that, we investigated the impact of building thermal mass, outdoor air conditions, and energy price profiles on the optimal precooling strategy. Our results show substantial cost savings and peak load reduction potential in buildings with heavy thermal mass, with some energy penalty. Although the potential for cost savings is lower in buildings with low and medium thermal mass, the energy penalty remains consistent in all three thermal mass scenarios. Furthermore, the results highlight that while outdoor air conditions play a role in cost and energy performance, the cooling load exerts a more immediate and substantial influence on cost savings in precooling strategies. In addition, the duration of peak pricing and the ratio between peak and off-peak times exhibit clear correlations with cost savings, peak load reduction, and energy consumption, aligning with intuitive expectations. These findings offer valuable insights for optimizing precooling strategies in office buildings.

The Impact of Big Data-Driven Industrial Digital Unification System on Commercial Management Operational Efficiency

Abstract Integrated management digitization is an important way to boost the competitiveness of commercial management enterprises. In this paper, we first design the functional architecture and system deployment of the commercial management enterprise digital system, including digital operation management, commercial operation data mining analysis, and other functional modules to meet the commercial operation management needs of commercial complexes, office buildings, and urban integrated operation business. The K-means clustering algorithm is then improved by using a particle swarm algorithm that is based on it. Specifically, the distribution estimation algorithm and stagnation perturbation strategy are used to update the population information and control the particle position boundary. Then, the greedy approach is used to select the advantageous particles. Then, the digital unified construction system of the commercial management enterprise is finally constructed to realize the data mining function of the operation and management and to assist the enterprise in carrying out customer management, property rights management, scientific decision-making, risk assessment, and so on. Finally, after testing the data analysis performance of the system, the business performance of the W commercial management group company that uses the system of this paper for digital transformation is analyzed. It is found that the accuracy of this paper's algorithm is the same as the PSO-Kmeans method. Still, the number of iterations of this paper's algorithm is the least; the lowest is only 14 times, and the optimization of efficiency is significant. The return on net assets and the net sales margin of W Commercial Management improved from 15.8% and 28.12% in 2016 to 20.20% and 28.12% in 2023. The debt repayment and operation ability are also optimized substantially, and the system designed in this paper The effectiveness of the developed system is proved. This study provides a proven solution for the digital transformation of commercial management enterprises and improves the operational efficiency of commercial management.

Low-carbon optimization in commercial buildings through data mining

The government of China commits to achieve peak carbon dioxide emissions by 2030. According to the United Nations Environment Programme, nearly 40% of energy-related carbon dioxide emissions are attributable to the building sector while building operations account for over 70% of carbon emissions. How to quantitatively analyze the factors related to operational carbon emissions under the circumstance of rapid growth of data volume is the key problem to be solved. This article explored the main factor affecting carbon emissions of commercial office buildings based on data mining and analyzed all carbon sources using the variation and deviation method in the city of Beijing, China. It is found that electricity, trains, airplanes, and hotels have a great impact on the building’s operation stage carbon emission, and targeted carbon emission reduction policies and measures can be made to reduce the sample’s carbon emission.

Parametric analysis of chiller plant energy consumption in a tropical climate

The approach that could reduce the energy consumption of water-cooled chillers in office buildings was investigated through experimental and simulation methods. The chiller was modelled in TRNSYS and validated using physical measurements from an operational water-cooled water chiller and fan coil system. The validated model was used to analyse the energy consumption of the water chiller-fan coil system under a high chilled water temperature setpoint (CWTS) in a representative commercial office building. Subsequently, indoor thermal comfort was evaluated using the PMV-PPD model. Finally, the design of experiment (DOE) was employed using a statistical two-level non-randomized factorial design in Minitab to study the effects of high CWTS, number of rows, and tube diameter on the heat and mass transfer performance of the fan coil unit. The results showed that the CWTS can be increased by various degrees from 10 up to 18 °C for energy efficiency for a commercial office building in the tropics. This increase in CWTS would result in a daily energy saving potential of about 5% of the chiller as compared to the existing operational settings without any extra cost. Conversely, the daily energy consumption by the fan coil would increase by about 5.5% by this increment in the CWTS. It was determined that the chiller system can provide comfort even when the CWTS is increased to 14 °C. The DOE analysis showed that under the condition of high CWTS (14 °C), energy consumption that is less than the current energy consumption may be expected from the fan coil system when the number of rows increased from 3 to 6 and the tube diameter increased from 7 to 9 mm.

Sequential attention deep learning architecture with unsupervised pre-training for interpretable and accurate building energy prediction with limited data

ABSTRACT Predicting building energy consumption is important for energy efficiency and reducing carbon emissions. However, deep learning (DL) models for energy consumption forecasting often have limited predictive generalization and lack explainability. To address these challenges, this research adopts a sequential attention deep learning architecture (SADLA) that uses attention mechanisms to learn the importance of different features for predicting cooling energy consumption, which helps building occupants and managers to make informed decisions about energy optimization. The model was trained and tested on comprehensive and scarce datasets in terms of recording periods and predictor numbers. The data were derived from a three-month field experiment in a commercial office building in Seoul, Korea. Furthermore, models were constructed with other prevalent algorithms like Long Short-Term Memory, deep neural networks, and Extreme Gradient Boosting for comparative assessment. The results from the one- and two-week datasets and reduced features suggested that the SADLA-based models surpassed others in model generalization (R2 = 0.961, 0.967, and 0.976 respectively). However, all algorithms demonstrated comparable performance with the three-month dataset achieving an average R2 of 0.970. These findings underscore the potential of the adopted model in addressing data scarcity in existing and new buildings for accurate cooling energy prediction.

Empirical validation of building energy simulation model input parameter for multizone commercial building during the cooling season

This paper presents a critical advancement in Building Energy Modeling (BEM) through an empirical validation approach using a high-quality dataset from a multizone commercial office building in Oak Ridge, TN, USA. BEM is widely utilized in diverse construction applications, but its effectiveness relies on the accuracy of its predictions. The study focuses on empirical validation of input parameters in BEM, including building envelope data, infiltration modeling, and rooftop unit system performance curves. The validation of simulation input parameters leads to substantial improvements in the accuracy of simulation results. Notable both NMBE and cv (RMSE) values are reduced by 0.5 % for indoor air temperature and 17 % for indoor air relative humidity compared to the previous model. At the system level, both NMBE and cv (RMSE) values are reduced by 2 % for fan energy consumption and 4 % for cooling energy consumption, compared to the previous model. A literature review highlights a significant gap in empirical validation studies, which predominantly concentrate on either component-level or whole building validation. Furthermore, many studies employ simplified setups that may not faithfully represent the complexities of multizone commercial buildings. This paper distinguishes itself by emphasizing the critical importance of component-level input parameter validation. It underlines the need to validate data related to building envelope components and HVAC system performance curves, resulting in more accurate simulation outcomes. The utilization of actual multizone commercial building data enhances the study's practical relevance. In summary, this research underscores the pivotal role of input parameter validation in enhancing the accuracy and reliability of BEM.

Performance assessment of a real water source heat pump within a hardware-in-the-loop (HIL) testing environment

Over the last decade, the global fight against climate change through electrification has led to an increase in research on building heating, ventilation, and air conditioning (HVAC) systems that utilize intelligent control algorithms to provide demand-side grid service while also maintaining the thermal comfort of building occupants. As the pivotal point between building electricity consumption and indoor thermal comfort, high-efficiency electrical vapor-compression heat pumps are at the center of these emerging studies, and various grid-interactive and occupant-comfort control algorithms have been developed for them. The impact of these algorithms on heat pump operation and performance when subjected to different weather conditions, building loads, and grid requests calls for investigation and verification via experimental testing with actual heat pumps integrated with real-time building and grid responses. This study introduces a Water-Source Heat Pump (WSHP) Hardware-in-The-Loop (HIL) Test Facility that is the first of its kind. This testbed utilizes a 2-ton variable speed water-to-air heat pump that is capable of interacting with a virtual environment currently comprised of an EnergyPlus (E+) building simulation, an agent-based occupant behavioral model, and a single U-tube ground-loop heat exchanger (GLHE) model. Detailed descriptions of the testbed’s physical design and operation, virtual environement, as well as their mutual communication is provided. An uncertainty analysis is also performed under manufacturer specified heating and cooling design conditions. This analysis shows that the total load across the WSHP’s demand side heat exchanger, i.e., the sum of its latent and sensible components, can be measured with a relative uncertainty of ± 10.4% and ± 3.6% in cooling and heating mode respectively. The WSHP’s coefficient of performance (COP) can be measured with relative uncertainties of ± 10.4% in cooling mode, and ± 3.7% in heating mode. A preliminary 24-h experimental demonstration is then performed utilizing the DOE prototype small commercial office building model in E+. The simulation takes place in Atlanta, GA on the date of 08/26/15 from 12:00 AM to 11:59 PM using TMY3 weather data. . The results from this demonstration show that over the course of this experiment the simulated outputs of zone dry-bulb temperature, zone humidity ratio, and WSHP inlet water temperature can be tracked by testbed emulators up to a root mean squared error (RMSE) of ± 0.27 °C, ± 0.376 g/kg, and ± 0.85 °C respectively. The WSHP’s dynamic behavioral characteristics and performance are also captured, and correspond well with the authors’ previous understanding of heat pump efficiency as a function of evaporator and condenser fluid inlet conditions respectively.

Development of Janitors’ Workload Calculator

This paper presents the development of a Workload Calculator specific to the commercial office building janitorial service industry. This is developed due to the increased need for addressing the high workload issues and increased work-related injury claims in the industry. Actual field data were collected to quantify various workload measures and the risks are evaluated with widely used ergonomics job evaluation methods. These workload measures include work pace, overall workload (steps walked and energy expenditure demands, hand/wrist, shoulder and low back biomechanical exposures). This developed workload calculator can provide industrial practitioners with a tool to estimate the workload of planned or existing jobs. It can also help EHS, safety and ergonomics practitioners to identify issues and develop focused interventions. It can also provide cleaning equipment manufacturers and cleaning method developers means to identify issues in the current system and develop new equipment and methods for improvement.

Comparing simulated demand flexibility against actual performance in commercial office buildings

Commercial building energy benchmarking has been used as a mechanism to evaluate energy use of a single building over time, relative to other similar buildings, or to simulations of a reference building conforming to various energy standards. Lack of empirical demand flexibility data and consistent flexibility metrics has limited the ability to compare demand flexibility performance with estimated demand flexibility in buildings. In this study, we collected demand response performance data for a total of 831 demand response events from 192 sites as a first step to build such a building demand flexibility dataset, and propose a standard core data schema to consolidate field data from different sources. We also performed parametric simulations of a control strategy called “global temperature adjustment” using commercial office prototype building models. We then compared the simulated demand flexibility performance against the actual data for offices with global temperature adjustment strategy implemented. During demand response events with an average outside air temperature of 34 °C (range 23 °C–42 °C), the measured demand decrease intensity of the demand flexibility metrics were 6.1 watts per square meter (W/m2), 10.0 W/m2, 11.1 W/m2, 7.1 W/m2, and 4.7 W/m2 for small, small–medium, medium, medium–large, and large office buildings, respectively. Compared to the measured data in medium- and large-size buildings, the simulated demand decrease intensity was 0.7 W/m2 (17%) lower on average. The discrepancy between simulated and measured peak demand intensities fell within one standard deviation of the mean measured data. The comparison results validate the credibility of simulations in capturing real building data for assessing the technical potential of building demand flexibility.

POTENTIAL OPPORTUNITIES OF BLOCKCHAIN TECHNOLOGY IN STREAMLINING THE MALAYSIAN COMMERCIAL OFFICE BUILDING OPERATION MANAGEMENT PROCESS

Managing commercial real estate is extremely complex and cumbersome and involves many stakeholders who provide access to and modify a variety of information. This led to some common issues with traditional commercial real estate management such as lack of transparency, third-party interference, the complexity of the agreements, record keeping and security, and absence of real-time data. Accordingly, blockchain technology is anticipated to address the issues of data openness, trustworthiness, and correctness in the global real estate market. This research aims to discover the potential opportunities that blockchain could offer in streamlining the commercial office building operation management process. A conceptual framework of blockchain-based technology adoption in the current Malaysian commercial office building operation management process was developed based on the literature review and focus group discussion (FGD). The framework was underpinned by four core operation management that emerge from the research findings including financial, administrative, technical, and promotional management.

Research on Online Temperature Prediction Method for Office Building Interiors Based on Data Mining

Indoor environmental parameters are closely related to the energy consumption and indoor thermal comfort of office buildings. Predicting these parameters, especially indoor temperature, can contribute to the management of energy consumption and thermal comfort levels in office buildings. An accurate indoor temperature prediction model is the basis for implementing this process. To this end, this paper first discusses the input and output parameters of the model, and then it compares the prediction effects of mainstream prediction model algorithms based on data mining under the same data conditions. The superiority of the XGBoost integrated learning algorithm is verified, and a further XGBoost-based indoor temperature online prediction method is designed. The effectiveness of the method is validated using actual data from a commercial office building in Haidian District, Beijing. Finally, optimization methods for the prediction method are discussed with regard to the scheduler mechanism proposed in this paper. Overall, this work can assist building operators in optimizing HVAC equipment running strategies, thus improving the indoor thermal comfort and energy efficiency of the building.

HVAC Systems Evaluation and Selection for Sustainable Office Buildings: An Integrated MCDM Approach

Heating, Ventilation, and Air-Conditioning (HVAC) systems are critical components of maintaining an indoor air quality that ensures the thermal comfort of occupants in diverse building types. However, HVAC systems are also responsible for a substantial portion of the total energy consumption of commercial and industrial office buildings. This paper presents an integrated approach of two powerful MCDM techniques: the Best-Worst-Method (BWM) and The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) in order to evaluate and rank HVAC systems to ensure the best selection toward designing a sustainable office building. A set of conflicting criteria reported by international sustainable assessment centers were employed along with various HVAC systems to develop what is called herein a BWM-based TOPSIS model in order to conduct such a novel research attempt. Within the context of the investigated office building herein, the mini-package ductless system appears to be the best choice. This study could be further enhanced by including additional criteria and a larger sample size in future studies.

Assessing building's post-earthquake functional recovery accounting for utility system disruption

Experience from past earthquakes has demonstrated the need to account for design goals beyond safety, known as functional recovery objectives, in the interest of community resilience. Frameworks have been proposed in the literature to assess the post-earthquake functional recovery of a building, but without accounting for utility systems’ disruption, which may be a key contributor to determining when a building is functional. This paper integrates a previously proposed probabilistic method for estimating the post-earthquake restoration of critical utility services with an individual building's functional recovery assessment framework. The integration was performed by incorporating utilities into the building system fault trees embedded into a functional recovery framework for various building occupancies (residential and commercial office buildings). Once incorporated, the results are used to interrogate the functional recovery of a reinforced concrete building, and the recovery time results were presented for seven cases investigating contributing factors in the functional recovery results including the number of crews available for lifeline restoration, the effect of low-quality service on meeting tenant requirements for elevators, heating ventilation and air conditioning (HVAC), plumbing and electrical systems, consideration of fire watch, the effect of building seismic retrofit, as well as different cases of fragility functions for the lifeline systems. Results showed that utility systems’ disruption does not have a significant impact on the recoccupancy of a building because only one utility-dependent building system (fire suppression) is needed for the building's safety. Unlike reoccupancy, utility systems are significant for functional recovery, mainly at moderate hazard levels because, at these levels, lifeline networks could be damaged without significant building damage, such that the lifeline systems restoration governs. Buildings with more restrictive tenant requirements are more sensitive to tenant disruptions.

Development of design guidelines for thermal, visual, and noise insulation performance of building envelope of low-rise commercial office buildings in the Indian tropical climate

Development of design guidelines for thermal, visual, and noise insulation performance of building envelope of low-rise commercial office buildings in the Indian tropical climate