Energy Use In Commercial Buildings Research Articles

How will United States commercial building energy use be impacted by IPCC climate scenarios?

Climate change and anthropogenically-forced shift of weather in the future will impact energy use and resilience of both the built environment and the electric grid. The aim of this analysis is to understand how future climate scenarios will impact electricity and natural gas use of commercial buildings in the United States. This study analyzes this impact for 2030, 2045, and 2100 using Representative Concentration Pathways (RCP) scenarios defined in Intergovernmental Panel on Climate Change (IPCC) Assessment Report 5. The large, gridded simulation of meteorological variables for RCPs 2.6, 4.5, 6.0, and 8.5 are selected and downscaled to make available hourly Future Meteorological Year (FMY) weather files for use and improvement in subsequent studies. High performance computing resources use these FMYs to simulate commercial prototype buildings in every American Society of Heating, Refrigeration, and Air Conditioning Engineers (ASHRAE) climate zone of the United States (US), and results are scaled to nation-wide energy use using conditioned floor area multipliers. The analysis is conducted without speculating the physical and performance traits of future buildings or the grid characteristics.This analysis quantifies the impact of climate change on source electrical and natural gas usage for commercial buildings in the United States over the next 80 years. If US commercial floorspace remained constant, total energy use by 2100 is predicted between an 1.75% decrease under the greatest emission scenario (8.5) and a 1.76% increase under the lowest emission scenario (2.6). When adjusted for anticipated urban growth by 2100, the predicted range is 65% increase (8.5) and 71% increase (2.6). Under a global temperature rise climate scenario, the warmest US climate zones will see a large increases in electricity use derived from space cooling while the coldest US climate zones will see significant decreases in natural gas use caused by the decrease in heating necessary. While climate change may ultimately require adaptations of the built environment to withstand its effects and because the United States is a country that requires more heating than cooling, from a building energy perspective, climate change (average temperature rise) is a net energy saver for the United States.

Exploring Factors Influencing Renewable Energy Diffusion in Commercial Buildings in Nigeria: A Grounded Theory Approach

The adoption and integration of renewable energy technologies (RETs) into buildings is key to making the necessary transition to low-carbon and resilient built environments. However, such technologies have struggled to gain a firm foothold in countries within the sub-Saharan African (SSA) region. This is particularly the case in Nigeria, which suffers from severe energy poverty, despite its significant RE and conventional energy potential. In Nigeria, a significant proportion of the energy demand for offices is provided by self-powered off-grid fossil-fuel generators. The country is also one of the primary settings for increased construction activity. This, combined with its susceptibility to the effects of climate change, presents significant concerns relating to the resilience of its built environment. However, there has not yet been a comprehensive empirical study addressing this, as previous studies have been limited in their insight and perspectives. This study adopted a grounded theory method (GTM) aligned with Charmaz’s approach, to gain in-depth participant-driven insights into factors influencing sustainable energy use in commercial buildings, focusing on solar photovoltaics (PVs). This led to the development of a theory of the sustainability transition process of construction professionals (CPs). It provides relevant, reliable, and relatable points of reference that would be beneficial to policymakers in developing plans for actionable interventions for PV and broader sustainable measures toward green energy transition. Furthermore, it highlights the value of employing GTMs in construction management research beyond the developing context. This paper contributes theoretically, empirically, and methodologically to facilitate a better understanding of the situations (context) grounded in empirical data.

Integrating building energy simulation with a machine learning algorithm for evaluating indoor living walls’ impacts on cooling energy use in commercial buildings

The environmental challenges of climate change increase the energy usage and peak demands of buildings. Most extant studies of greenery systems focus on exterior applications such as green façades and roofs, which indirectly affect the indoor environment. Few studies have focused on quantifying the influence of indoor greenery systems on building energy consumption. The cooling effects of indoor greenery systems such as living walls are largely accounted for by the evapotranspiration (ET) process, in which water is transferred to the ambient environment through the evaporation from and transpiration of plants. Current building energy simulation software such as EnergyPlus does not have a module for modeling indoor greenery systems.In this study, an ET model was created using a machine learning algorithm—Gaussian Mixture Regression (GMR) based on experimental data. An indoor living wall model quantifying sensible and latent loads from the ET process was integrated with the energy simulation software—EnergyPlus through the Python plugin feature. The U.S. Department of Energy (DOE) medium-sized office building reference model was modified and used in this study to evaluate indoor living walls’ impacts on cooling energy use. A parametric study on leaf to floor area ratios (LFAR), orientations, distances from windows, and climates was conducted to evaluate the influence of each factor on indoor living walls’ performance. Cooling effects of indoor living walls were evaluated in three ASHRAE climates with high cooling demands. Observable cooling energy savings were obtained for the south, east, and west perimeter zones while savings for the north perimeter zone was negligible in all three climates. With the consideration of extra electricity use from direct expansion (DX) dehumidification devices for humidity control, the maximum cooling electricity savings in Los Angeles, CA are 25.1 % when LFAR = 1.5 for the south perimeter zone, 14.4 % when LFAR = 0.5 for the east perimeter zone, 0.3 % when LFAR = 0.3 for the north perimeter zone, and 14.5 % when LFAR = 0.5 for the west perimeter zone on the design day.

Does Energy Star Certification Reduce Energy Use in Commercial Buildings?

Does Energy Star Certification Reduce Energy Use in Commercial Buildings?

Organizational roles in a sustainability alliance network

AbstractSustainability alliance networks offer a collaborative governance strategy to address both environmental and societal challenges too large for any single organization. While alliance networks, and particularly sustainability alliances, have been studied in a number of contexts, few alliances have been explored under the context of commercial building energy use—a sector with a multitude of policy, organizational, and technical barriers to the deployment of innovative energy‐ and cost‐saving strategies. In this research, we articulate a framework wherein organizations can assume varying roles across an alliance network based on the organizations' objectives, organizational resources, and the relationships or ties formed with other alliance members. Further, we show that particular roles within one alliance network have implications for the types of knowledge gained and the knowledge shared across the network. Our mixed‐method study draws from several data sources, employs analytic induction, and is supplemented by social network analysis in order to examine an energy efficiency‐focused sustainability alliance comprised of a broad coalition of stakeholders, including US government agencies, federally funded research laboratories, private firms, and trade associations. We develop a typology for six types of organizational roles identified in an energy efficiency alliance network—each role possessing varying implications for the organization's access to and sharing of knowledge.

Near-real-time plug load identification using low-frequency power data in office spaces: Experiments and applications

Plug loads account for up to one-third of the overall energy use in commercial buildings. There is thus a growing research interest in utilising load monitoring systems to track plug load usage by installing smart plugs to capture high-resolution consumption data. The availability of such data has also enabled the development of automatic plug load identification models that enhance the capabilities of existing load monitoring systems. Through our literature review, we highlighted several limitations that impede real-world implementation, such as the limited number of publicly available datasets for commercial buildings, models trained on data with high sampling frequencies while using an extended time window, and data leakage issues during model training. In this study, we proposed a near-real-time plug load identification approach that uses low-frequency power data (1/60 Hz) to identify plug loads in office spaces. The dataset used in this study is processed by first identifying the active periods of the plug loads before applying a novel dynamic time window strategy during feature extraction. These extracted features are subsequently passed through several classification algorithms and evaluated using different accuracy metrics. The proposed approach is also assessed through multiple experiments, including (1) identifying the best online and offline models, (2) comparing between different time window strategies, and (3) evaluating model performances under different sampling frequencies. As a result, the best online model achieved accuracies up to 93% using the Bagging algorithm with a minimum dynamic time window of 5 minutes. Finally, we highlighted two application areas of automatic plug load identification in energy dashboards and personalised control systems as part of future works.

Optimization Study of Parametric Thermal Bimetal Material Module for Green Building in Tropical Humid Climate

The largest energy consumption that we use is the consumption of electrical energy in terms of meeting the lighting needs and building air conditioning requirements (World Energy Consumption, 2016). According to the Ministry of Energy and Mineral Resources (2017), Indonesia’s largest energy use in commercial buildings is for flight systems (63%), lighting systems (20%), vertical transportation (7%) electronic devices (10%). The use of energy in the fulfillment of excessive needs result in worsening conditions on earth. Data can be a reflection of how the condition of the earth that we live at this time. Energy savings should be made to reduce the damage already occurring on this earth such as electricity usage savings, optimization of use of materials, the use of motor vehicles that cause air pollution, and others. The way that can be used to reduce artificial energy use is to utilize the existing passive building design such as the use of solar energy that can be maximized during the day, so that the use of electrical energy for lamps and artificial air conditioning. reduced. In addition, it can also be considered the optimal use of wind direction and speed that can suppress the use of Air Conditioner (AC) in excess. Building envelope with bimetal thermal material module application is part of kinetic architecture via biomimicry approach. Kinetic architecture is a concept where buildings are designed to allow parts of buildings to move without compromising the unity of the structural system. Approaches that can be applied in green building design is by optimizing bimetal material module that utilizes the thermal coefficient of a material. With this approach, building envelopes are improvised so that they can adapt to the existing environment. This research take location of case study in Kampung Juminahan, Yogyakarta, Indonesia which has characters of comunal housing.

Predictive modeling for US commercial building energy use: A comparison of existing statistical and machine learning algorithms using CBECS microdata

With the growing trove of publicly available building energy data, there are now ample opportunities to apply machine learning methods for prediction of building energy performance. In this study, we test different predictive modeling approaches for estimating Energy Use Intensity (EUI) for US commercial office buildings and the individual energy end-uses of HVAC, plug loads, and lighting, based on the latest Commercial Building Energy Consumption Survey (CBECS) 2012 microdata. After preliminary statistical analysis, six regression or machine learning techniques are applied and compared for prediction performance. Among all candidates, Support Vector Machine and Random Forest demonstrate both accuracy and stability. However, machine learning algorithms are better than the linear regression only to a limited extent, with on average 10–15% lower prediction errors for Total EUI prediction. Conversely, linear regression models slightly outperform machine learning methods in estimating Plug Loads EUI. These mixed results suggest careful consideration in applying advanced predictive algorithms to the CBECS dataset. Individual variable importance was tested using Random Forest, with the top 10 predictors differing for the total and sub-system EUIs. The analysis demonstrates that, for the techniques applied, the variables reported in CBECS have inadequate predictive power to map actual energy consumption. Filling information gaps in areas such as occupant behavior, power management, building thermal performance, and their interactions may help to improve predictive modeling.

Effects of Global Climate Change on Building Energy Consumption and Its Implications in Florida

ABSTRACTMany studies have investigated the impact of global warming on energy consumption. In this study, the morphing method and EnergyPlus (E+) software were used to investigate the impact of climate change on commercial building energy use under the A2 medium-CO2. emission scenario. The study simulated electricity and gas consumption of nine types of commercial buildings in eight cities, representing three climate zones in Florida. The nine types of commercial buildings included apartments, hotels, offices, and schools. The energy simulation results showed the future trends of growth and reduction in electricity and gas consumption for cooling and heating in TMY2 (TMY3), 2020, 2050, and 2080 in the eight selected cities. In general, gas and electricity demands for heating are projected to decrease, and electricity demand for cooling increases, at different rates in various areas of Florida. The study provides guidance for policy‐makers and utility companies as they craft their response to climate change in various regions of Florida.

Energy performance analysis of variable reflectivity envelope systems for commercial buildings

This paper evaluates potential energy savings associated with variable reflectivity envelope systems (VRCs) including cool roofs associated the application of coatings that can change their reflective properties over time depending on outdoor conditions and desired controlled strategies. It is well known that static cool roofs can save energy use during cooling periods but may increase energy consumption during swing or heating seasons due to lower beneficial heat gains. In the analysis, a two-step control strategy is considered to modify the properties of the cool roof coatings based on the temperature difference between internal and external roof surfaces. Using a simulation environment capable of modeling variable properties of outer surface of building envelope, a comparative analysis is carried out to evaluate the impact of VRCs on the heating and cooling energy end-uses for commercial buildings in four US climates. It is observed that VRCs can save heating energy use compared to static cool roofs for all four US climates for both commercial buildings. In particular, it is found that VRCs have the potential to reduce annual energy use in commercial buildings up to 11% compared to the static cool roofs.

Analysis of the Effect of Artificial Lighting on Heating and Cooling Energy in Commercial Buildings

In relation to recent attention focused on the use of energy by lighting, there is a large potential for commercial buildings to save energy in this respect. The use of energy-saving bulbs or dimming controls triggered by daylight, not only enables lighting energy saving, but delivers cooling energy through a reduction in indoor heat production. In this study, we examine the energy use in commercial buildings by evaluating lighting energy saving methods and the effect of using dimmer controls with a heat control method, which can both maximize the cooling load reduction during summer and save energy. Results show that the total energy from the reference commercial building was reduced by 20.9% by replacing LFL T12 bulbs with LEDs, together with an additional saving of 19.9% in cooling energy by using the lighting heat & dimming control method.

Commercial building energy use in six cities in Southern China

With China’s continuing economic growth, the percentage of government offices and large commercial buildings has increased tremendously; thus, the impact of their energy usage has grown drastically. In this survey, a database with more than 400 buildings was created and analyzed. We researched energy consumption by region, building type, building size and vintage, and we determined the total energy use and performed end use breakdowns of typical buildings in six cities in southern China. The statistical analysis shows that, on average, the annual building electricity use ranged from 50 to 100kWh/m2 for office buildings, 120 to 250kWh/m2 for shopping malls and hotels, and below 40kWh/m2 for education facilities. Building size has no direct correlation with building energy intensity. Although modern commercial buildings built in the 1990s and 2000s did not use more energy on average than buildings built previously, the highest electricity intensive modern buildings used much more energy than those built prior to 1990. Commercial buildings in China used less energy than buildings in equivalent weather locations in the US and about the same amount of energy as buildings in India. However, commercial buildings in China provide comparatively less thermal comfort than buildings in comparable US climates.

Agent-Based Modeling of Occupants and Their Impact on Energy Use in Commercial Buildings

AbstractEnergy modeling is globally used during the design phase to estimate future building energy performance. Predictions obtained from common energy estimation software typically deviate from actual energy consumption levels. This discrepancy can mainly be attributed to the misrepresentation of the role that building occupants play in the energy estimation equation. Although occupants might have different and varying energy use characteristics over time, current energy estimation tools assume they are constant. This paper proposes a new agent-based approach to commercial building energy modeling by accounting for the diverse and dynamic energy consumption patterns among occupants, in addition to the potential changes in their energy use behavior attributable to their interactions with the building environment and with each other. The impact of an active modeling of occupancy is then illustrated in a case study of an office in a university building, where more than 25% variation in the predicted energy...

Life cycle optimization of building energy systems

A life cycle optimization model intended to potentially reduce the environmental impacts of energy use in commercial buildings is presented. A combination of energy simulation, life cycle assessment, and operations research techniques are used to develop the model. In addition to conventional energy systems, such as the electric grid and a gas boiler, cogeneration systems which concurrently generate power and heat are investigated as an alternative source of energy. Cogeneration systems appeared to be an attractive alternative to conventional systems when considering life cycle environmental criteria. Internal combustion engine and microturbine (MT) cogeneration systems resulted in a reduction of up to 38% in global warming potential compared with conventional systems, while solid oxide fuel cell and MT cogeneration systems resulted in a reduction of up to 94% in tropospheric ozone precursor potential (TOPP). Results include a Pareto-optimal frontier between reducing costs and reducing the selected environmental indicators.

Telework Adoption and Energy Use in Building and Transport Sectors in the United States and Japan

Telework ~or telecommuting! has been proposed as a way of reducing transportation externalities such as congestion and passenger vehicle energy use. While there is contention among sources about the secondary induced transportation effects, many studies have estimated net benefits as a result of transportation reductions. Analyses including effects from shifts in commercial and residential building energy use, however, are not as visible. It is expected that commercial building energy use would decrease, and residential energy would increase as a result of telework adoption. We estimate macrolevel energy effects across these three energy intensive sectors ~transportation, commercial, and residential buildings ! to help characterize the potential of telework as a policy initiative to improve national energy efficiency. For current estimated teleworking populations and practices in the United States and Japan, we estimate national level energy savings of only 0.01-0.4% in the United States and 0.03-0.36% in Japan. In a future scenario with pervasive adoption of teleworking, where 50% of information workers telecommute 4 days per week, United States and Japan national energy savings are estimated at only about 1% in both cases. These energy savings are quite modest compared with currently available policy options to mitigate energy demand. By comparison, an improvement in average vehicle fuel efficiency of 20% would save 5.4% of total United States energy demand, suggesting that the direct benefits of adopting telework are relatively small given the large degree of behavioral and structural change required. Still, presuming that future work practices tend to favor greater adoption, the potential energy savings may merit direction towards environmentally beneficial implementation. If trends in the workplace favor more telework in the future, we suggest that maximizing environmental benefits merits greater attention to how telework is adopted. Obviously the increased number of avoided commutes is a factor, but more importantly, elimination of office space due to virtual offices yields energy savings that rival those from reduced commuting. Future analyses and implementation of telework should thus give greater attention to energy use in buildings and residences.

Energy use in commercial buildings in Hong Kong

The energy performances of 20 commercial buildings, covering the common types found in Hong Kong, have been studied. The monthly electricity billing data as well as the results from computer modeling were analyzed. Energy signatures for the commercial buildings were found by a method similar to PRISM. Correlation equations of energy use with building envelope parameters, such as OTTV, building size, envelope heat gain, and the annual chiller-load were derived. The results are useful for building envelope design, energy audit, and legislative control of energy use in commercial buildings.

Thermal Energy Storage for Space Cooling: An Underutilized Opportunity in Federal Buildings

ABSTRACT Cool storage technology can be used to significantly reduce energy costs by allowing energy-intensive, electrically driven cooling equipment to be predominantly operated during off-peak hours when electricity rates are lower. In addition, some system configurations may result in lower first costs and/or lower operating costs. Cool storage systems of one type or another could potentially be cost-effectively applied in most buildings with a space cooling system. A survey of approximately 25 manufacturers providing cool storage systems or components identified several thousand current installations, but less than 1 percent of these were at federal facilities. With the federal sector representing nearly 4 percent of commercial building floor space and 5 percent of commercial building energy use, federal utilization would appear to be lagging. Although current applications are relatively few, the estimated potential annual savings from using cool storage in the federal sector is $50 million. The first...

Thermal Energy Storage for Space Cooling: An Underutilized Opportunity in Federal Buildings

ABSTRACT Cool storage technology can be used to significantly reduce energy costs by allowing energy-intensive, electrically driven cooling equipment to be predominantly operated during off-peak hours when electricity rates are lower. In addition, some system configurations may result in lower first costs and/or lower operating costs. Cool storage systems of one type or another could potentially be cost-effectively applied in most buildings with a space cooling system. A survey of approximately 25 manufacturers providing cool storage systems or components identified several thousand current installations, but less than 1 percent of these were at federal facilities. With the federal sector representing nearly 4 percent of commercial building floor space and 5 percent of commercial building energy use, federal utilization would appear to be lagging. Although current applications are relatively few, the estimated potential annual savings from using cool storage in the federal sector is $50 million. The first...

Energy use in commercial buildings in Hong Kong

The energy performances of 20 commercial buildings, covering the common types found in Hong Kong, have been studied. The monthly electricity billing data as well as the results from computer modeling were analyzed. Energy signatures for the commercial buildings were found by a method similar to PRISM. Correlation equations of energy use with building envelope parameters, such as OTTV, building size, envelope heat gain, and the annual chiller-load were derived. The results are useful for building envelope design, energy audit, and legislative control of energy use in commercial buildings.

Generalization of the Fourier Series Approach to Model Hourly Energy Use in Commercial Buildings

Development of the accurate models for hourly energy use in commercial buildings has important ramifications for (I) retrofit savings analysis, (ii) diagnostics, (iii) on-line control and (iv) acquiring physical insights into the operating patterns of the buildings. Electric and thermal energy uses in commercial buildings, being strongly periodic, are eminently suitable for Fourier series analysis. Earlier studies assumed trigonometric polynomials with the hour of the day as the primary variable and one week as the period. This model, though suitable on the whole, was poor during certain weekday periods and during weekends. This paper presents a generalized Fourier series approach which, while ensuring a wider range of applicability, also yields superior regression fits partly because of the care taken to separate days of the year during which commercial buildings are operated differently and partly because of the rational functional form of regression model proposed. The validity of the approach is verified with year-long data of twenty-two monitored buildings.