Energy Use In Office Buildings Research Articles

Influence of Transparent Envelope Parameters on Office Building Energy Use Intensity in the Different Thermal Climate Regions of China

The role of the transparent envelope in energy savings is crucial as it accounts for a significant proportion of the total energy loss (between 30 and 40%). This paper focuses on the identification of reasonable parameters for the transparent envelope in different climatic regions. To achieve this goal, typical urban office buildings from four different climatic regions are used as research objects. A total of 1600 scenarios were simulated to investigate the variation of energy use intensity, including transparent envelope parameters, meteorological parameters and different types of glazing. The results show that for south-facing transparent envelope facades, type D glazing is the most energy efficient in severe cold regions, type C in colder regions and type B in the other two climatic regions. No solar shading is required in the very cold region. Horizontal shading can be an effective method for saving energy in colder climates, while comprehensive shading can be beneficial in other regions. Deep shading is particularly energy efficient if it meets lighting requirements. For example, in Guangzhou, energy savings of 13.46%, 15.47%, 7.01% and 7.02% can be achieved in the east, west, south and north directions, respectively, using B-type glazing and a comprehensive shading depth of 900 mm.

The Impact of HVAC Operational Strategies on Energy Use in an Office Building in a Hot Climate

Office buildings, characterized by high internal heat gains, represent a major share of energy use, mainly due to HVAC systems, especially in commercial facilities. Such energy use becomes more significant in locations with harsh climatic conditions. The careful design, selection, and operation of HVAC and energy systems, however, can contribute to major reductions in the energy use in buildings. In this paper, we present the outcomes of a study conducted to assess the impact of HVAC operational strategies on energy use in office buildings in the hot and humid climate of Saudi Arabia. The study utilized detailed building-energy simulation coupled with utility-use data. The results revealed annual electrical energy savings ranging from an average of 1.9% for each 1°C increase in the thermostat setpoint temperature up to 42.4%, when limiting HVAC system operation to occupied hours only. On an average, there was about a 1.4% penalty in the annual energy for each hour of increase in the HVAC system start-up time. However, this penalty should not be avoided at the expense of compromising thermal comfort. In summary, a combination of appropriate HVAC operational strategies can have a significant impact on a building’s annual energy savings, as demonstrated in the presented case study.

Investigating the Energy-Efficient Structures Using Building Energy Performance Simulations: A Case Study

The use of energy efficient structures in the local construction industry assists in promoting green building concepts, leading to economical and eco-friendly solutions for self-sustained structures. The main aim of this study was to examine and compare the energy performance of various local buildings. Detailed 3D building models (house, office, and warehouse buildings) were constructed and investigated for their cost and energy savings using building energy simulation tools (green building studio and insight). Moreover, the effects of various building materials for walls, window panels, and roof construction were explored, and a life-cycle cost analysis was performed. It was observed that the effect of the window-to-wall ratio was less severe in term of energy use in office buildings compared to normal houses due to the larger amount of space available for air circulation. Furthermore, the most efficient location for windows was found to be at the middle of the wall in comparison with the top and bottom positions. The effect of the orientation mainly depended on the symmetry of the building. More symmetric buildings, i.e., tested warehouse buildings (rectangular structure), showed an energy use difference of around 7 MJ/m2/year for a 360° orientation change. Tested house buildings exhibited an energy use difference of up to 25 MJ/m2/year. Three-pane glass windows also showed major improvements, and the total energy consumption for houses was reduced to 14%. Furthermore, wood walls showed comparable energy performance with brick walls without the use of insulation. According to US-LEED guidelines, the tested house, office, and warehouse buildings achieved 79, 89, and 88 points, respectively. The cost recovery period for house, office, and warehouse buildings was estimated to 54, 13, and 14 years, respectively, including running and maintenance costs. It can be argued that the Insight and Green Building Studio packages can assist construction stakeholders to determine the energy efficiency of the modeled building as well as to help in the selection of materials for optimized and improved design.

Potential measures towards the reduction of cooling loads of office buildings in Ghana

PurposeGiven the climatic context and economic challenge of Ghana in its developmental strides, energy use of office buildings continues to be a task on the economy. Therefore, the study was about finding measures that could reduce cooling loads in 10 office buildings. The paper presents the outcome of a long-term study of the thermal conditions in a selected number of office buildings in Accra and Kumasi, Ghana.Design/methodology/approachThrough long-term monitoring of environmental data, the buildings were consequently modelled in a simulation application. Thereafter, a validation of the simulation models (using regression coefficients, r2 of 0.53–0.90) was undertaken towards finding measures to reduce cooling loads.FindingsThe results showed various potentials of efficient lighting, thermal mass, night ventilation, insulation to attic floors, efficient glazing, blind deployments, etc. in reducing cooling loads in the range of 2–17.5%. By combining the potential measures to study their synergistic effects on the loads, 35, 39 and 38% improvements were achieved for the low-rise, multi-storey and fully glazed office buildings.Originality/valueThese potential measures ought to be incorporated in the design, specification, construction and operation of Ghanaian office buildings to reduce the burden on the economy and the environment. Now more than ever, there is the need for climatic regions to come up with empirical data that could help relieve the world's economies from the post-pandemic stress.

On the impact of stochastic modeling of occupant behavior on the energy use of office buildings

The reliability of building performance simulation is hindered by several uncertainties, with aleatory uncertainty due to occupant behavior being one of the most critical. The present study aims to assess the propagation of uncertainty due to the adoption of stochastic models for modeling Occupant Presence and Actions (OPAs) available in the literature on the annual electric energy use of a reference office building. To this purpose, a global sensitivity analysis was designed and carried out by analyzing model inputs and energy outputs of 144 permutations of 15 different stochastic models for OPAs for a total of 7200 simulations. Building energy use computed considering stochastic OPAs modeling resulted in being sensibly higher than the reference value estimated assuming scheduled occupancy and rule-based occupant’s actions as suggested by reference standards. The median value of the electric energy use was 58.6% higher than the base case electric energy use. Furthermore, the stochastic models used to model window operation have the highest effect on energy output, followed by light switch-off, and occupancy models. Light switch-on models showed a lower influence on the overall building energy performance. Furthermore, the Generalized Estimating Equations method was adopted to assess the interdependence among stochastic models for OPA and showed that changing the stochastic model in window operation, occupancy estimation, and light switch-off behavior generates a considerable difference in building’s energy performance. Contrariwise, the available stochastic models for light switch-on and blind operation perform quite similarly among each other and have a limited impact on a building’s energy performance.

A Fundamental Study on the Development of New Energy Performance Index in Office Buildings

Recently, there have been significant concerns regarding excessive energy use in office buildings with a large window-to-wall ratio (WWR) because of the curtain wall structure. However, prior research has confirmed that the impact of the window area on energy consumption varies depending on building size. A newly proposed window-to-floor ratio (WFR) correlates better with energy consumption in the building. In this paper, we derived the correlation by analyzing a simulation using EnergyPlus, and the results are as follows. In the case of small buildings, the results of this study showed that the WWR and energy requirement increase proportionally, and the smaller the size is, the higher the energy sensitivity will be. However, results also confirmed that this correlation was not established for buildings approximately 3600 m2 or larger. Nevertheless, from analyzing the correlation between the WFR and the energy requirements, it could be deduced that energy required increased proportionally when the WFR was 0.1 or higher. On the other hand, the correlation between WWR, U-value, solar heat gain coefficient (SHGC), and material property values of windows had little effect on energy when the WWR was 20%, and the highest effect was seen at a WWR of 100%. Further, with an SHGC below 0.3, the energy requirement decreased with an increasing WWR, regardless of U-value. In addition, we confirmed the need for in-depth research on the impact of the windows’ U-value, SHGC, and WWR, and this will be verified through future studies. In future studies on window performance, U-value, SHGC, visible light transmittance (VLT), wall U-value as sensitivity variables, and correlation between WFR and building size will be examined.

Study on Behavioural Adaptation for the Thermal Comfort and Energy Saving in Japanese Offices

Office workers use a variety of adaptive opportunities to regulate their indoor thermal environment. The behavioural adaptations such as window opening, clothing adjustments, heating/cooling use are one of the important factors for the adaptive thermal comfort. It is well-known that they are the most important contributors in the adaptive thermal comfort model. Thus, if we understand the behavioural adaptation properly, we can explain the mechanism of the adaptive model. In addition, the indoor thermal environment is often adjusted using the air conditioning in Japanese office building to improve the thermal comfort and productivity. Thus, it is necessary to conduct research on the behavioural adaptation in the offices because the occupant behavior is different to the dwellings.
 In order to record the seasonal differences in behavioural adaptation and to develop an adaptive algorithm for Japanese offices, we measured temperatures in 11 office buildings and conducted the thermal comfort and occupant behaviour survey for over a year in Japanese offices. We collected 4,660 samples from about 1350 people.
 The proportion of ‘open window’ in the free running mode (neither heating or cooling being used) is significantly higher than that of the air-conditioned mode. The behavioural adaptation is related to the outdoor air temperature. The behavioural adaptation predicted by the regression analysis is in good agreement with the measured data. These findings can be applied to the building thermal simulation to predict the behavioural adaptation and energy use in office buildings.

Effect of high-performance facades on heating/cooling loads in London, UK office buildings

Office buildings require a significant amount of energy for heating and cooling purposes. A possible strategy for reducing this energy in order to reduce carbon dioxide emissions and operational costs is to specify high-performance facades. However, their benefits remain unclear for UK conditions with mild winters and cool summers. This paper reports on an investigation on the energy demand of offices in London, UK, incorporating facades with U-values between 1·2 and 2·6 W/(m2 K) and g-values between 0·3 and 0·5 using the dynamic simulation tool Tas. Other variables considered included climate change (using the Chartered Institution of Building Services Engineers’ future weather data files), low internal gains, long working hours and office orientation. It was found that, apart from the case when internal gains are low, cooling is overwhelmingly necessary and energy usage increases with decreasing U-value and decreases with decreasing g-value. Low-U-value facades act to reduce conduction heat losses, thereby increasing energy use. Conversely, low-g-value facades act to reduce solar heat gains, thereby reducing the energy required for cooling. The results are used to highlight deficiencies in The Building Regulations 2010 and where more advice would be of benefit. The paper also discusses the merits of a number of strategies for reducing energy use in office buildings.

Data-driven short-term load forecasting for heating and cooling demand in office buildings

Short-term forecasts of energy demand in buildings serve as key information for various operational schemes such as predictive control and demand response programs. Despite this, developing forecast models for heating and cooling loads has received little attention in the literature compared to models for electricity load. In this paper, we present data-driven approaches to forecast hourly heating and cooling energy use in office buildings based on temporal, autoregressive, and exogenous variables. The proposed models calculate hourly loads for a horizon between one hour and 12 hours ahead. Individual models based on artificial neural networks (ANN) and change-point models (CPM) as well as a hybrid of the two methods are developed. A case study is conducted based on hourly thermal load data collected from several office buildings located on the same campus in Ottawa, Canada. The models are trained with more than two years of hourly energy-use data and tested on a separate part of the dataset to enable unbiased validation. The results show that the ANN model can achieve higher forecasting accuracy for the longest forecast horizon and outperforms the results obtained by a Naïve approach and the CPM. However, the performance of the hybrid CPM-ANN method is superior compared to individual models for all studied buildings.

Behavioural Adaptation for the Thermal Comfort and Energy Saving in Japanese Offices

Office workers use a variety of adaptive opportunities to regulate their indoor thermal environment. The behavioural adaptations such as window opening, clothing adjustments, and use of heating/cooling are important factors for adaptive thermal comfort. It is well-known that they are the most important contributors in the adaptive thermal comfort model. Thus, if we understand the behavioural adaptation properly, we can explain the mechanism of the adaptive model. The indoor thermal environment is often adjusted using the air conditioning in Japanese office buildings to improve thermal comfort and productivity. Thus, it is necessary to conduct research on the behavioural adaptation in the offices because the occupant behavior is different from behaviour in dwellings. In order to record the seasonal differences in behavioural adaptation and to develop an adaptive algorithm for Japanese offices, we measured temperatures in 11 office buildings and conducted the thermal comfort and occupant behaviour survey for over a year. We collected 4,660 samples from about 1,350 people. The proportion of ‘open window’ in the free running mode (neither heating nor cooling being used) is significantly higher than that of the air conditioned mode. The behavioural adaptation is related to the outdoor air temperature. The behavioural adaptations such as window-opening, heating and cooling use predicted by regression analysis are in good agreement with the measured data. These findings can be applied to building thermal simulation to predict the behavioural adaptation and energy use in office buildings.

Observational study of the indoor environment and energy use in office buildings in tropical Asia

In recent years, the design for office buildings in tropical Asia have been affected by the American principals. In terms of energy conservation in the building sector, it is necessary to reveal the actual conditions in each region and the requirements of the standards, which are based on American building standards. This study thus investigated the indoor and outdoor thermal environments, the thermal sensation of the occupants, and energy consumption in office buildings in tropical Asia. This study simulated two models for a comparative analysis of real conditions and standards of American Society of Heating, Refrigeration and Air-Conditioning Engineers standard 90.1-2010. The simulation analysis showed that the electric power consumption of the actual models in Bangkok and Singapore were large because of the selection of a low temperature setting. Furthermore, in several cases investigated, the internal thermal loads, such as lighting and electrical equipment, were lower than the reference value. From the analysis, this paper presents the differences between reference values used in the Western and American standards, and the actual situation in tropical areas.

Comparison of different window behavior modeling approaches during transition season in Beijing, China

Window operation is an important occupant behavior, and has significant impacts on building energy consumption. Recently, various stochastic and non-stochastic models have been proposed, aiming to describe occupant window behavior based on several influencing factors. However, most of the employed methods are logit regression and Markov chain techniques, and the application of machine learning to model occupants' window behavior is rarely investigated. In addition, most published studies referring to occupants' window behavior have been carried out within European countries, where the influence of outdoor air quality is rarely considered. This study compares different models of occupants’ window behavior, including models based on logistic regression, Markov processes, and an artificial neural network. An artificial neural network model is proposed to explore the application and optimization of an artificial neural network algorithm under a condition of having less samples. Moreover, the outdoor fine inhalable particles (PM2.5) concentration is considered as an influencing factor for building a window opening model for office buildings during the transition season in China. From this work, it is generally concluded that the PM2.5 concentration and outdoor humidity should be considered in the modeling of occupant window behavior in Beijing, China. In addition, more true estimations can be obtained from artificial neural network models than from logistic regression models and Markov models. This result demonstrates that the proposed artificial neural network yields a prediction model of office window states with higher accuracy and better interpretability of highly correlated factors as compared to logistic regression models and Markov models. The proposed approaches provide a new and detailed way for engineers and building operators to better understand occupant window behaviors and their impacts on energy use in office buildings.

Optimization of Energy Usage of the Building Envelope Material at the Rental Office Buildings

This study aims to determine the effect of materials used in building envelopes on energy use in office buildings. To obtain energy use analysis data, ASHRAE 90.1 - 2013 based parameters are used in the Sefaira Building Simulation application. The building analyzed will be simulated, using variables such as sunlight conduction with roofs, walls, building window ratios, gap ratios in buildings, and use of electrical equipment per square meter. Based on the analysis carried out, the use of building energy by using standard material specifications from the application reaches 180 kWh / m2 / yr from what is supposed to be 79 kWh / m2 / yr. After being analyzed, a specification change is given to the liquid application so that the results of the analysis are 89 kWh / m2 / yr. So that the energy that can be reduced reaches 91 kWh / m2 / yr. This proves that the selection of building material specifications can affect energy use in rental office buildings.

Understanding energy-saving behaviors in the American workplace: A unified theory of motivation, opportunity, and ability

Occupant behavior has a significant impact on building energy consumption. To reduce energy use in office buildings, various intervention strategies have been investigated to promote energy-saving behaviors among occupants. However, the influential factors of these behaviors have not been fully understood in existing studies. To fill this gap, this study proposes an integrated Motivation-Opportunity-Ability (MOA) framework which incorporates social-psychological constructs from the Norm Activation Model and the Theory of Planned Behavior to investigate the determinants of energy-saving behaviors in the office environment. An online survey is distributed to collect data from multiple office buildings across the U.S. Results of the structural equation model (n = 612) show that opportunity has the strongest effect on energy-saving behaviors, followed by motivation and ability. In addition, motivation mediates the effect of opportunity and ability. This proposed framework offers an approach for decision-makers to design effective energy interventions based on the constraining factors in the targeted buildings.

Life cycle energy of high-rise office buildings in Hong Kong

Buildings are responsible for about 92% of the total energy end-use in Hong Kong, of which above 70% is from the commercial sector. Most of the buildings in Hong Kong are high-rise, and approximately 15% of the total building stock is commercial buildings. Despite the considerable improvement in the understanding of the life cycle energy use of office buildings, there is still a lack of research into the life cycle energy demand of high-rise office buildings. Such knowledge is however essential for facilitating building energy conservation in the high-rise urban environment. The aim of this paper is to investigate the life cycle energy use of high-rise office buildings. The research was carried out through a critical literature review and case studies of ten real-life high-rise office buildings in Hong Kong. Results show that the life cycle energy consumption is 51.78–73.64 GJ/m2 over the 50-year study period. Building operation consumes 78–89% and the rest (i.e. 11–22%) is taken by the embodied energy. Despite the distinction in terms of the amount of embodied energy between high-rise buildings and low-rise counterparts, no clear correlation is found between building height and embodied energy intensity. Moreover, energy use for material manufacturing and transport should be prioritised in reducing the embodied energy, of which the transport energy needs to be taken into extra consideration for the case of Hong Kong. The findings should inform construction industry participants of the significant potential in reducing the embodied energy of high-rise office buildings. The paper profiles the life cycle energy use of high-rise office buildings and should be important for the policy formulation towards the holistic energy conservation in high-rise buildings over their lifespans.

업무용 건물의 용도 및 운전 기간별 에너지 소비 특성 연구

The purpose of this study is to calculate the energy consumption rate based on data regarding energy use in office buildings, and to confirm the general characteristics of energy consumption.BR The energy consumption rate of the building is calculated by dividing the energy consumption by the floor area. The energy consumption rate of small-sized office buildings was calculated as 101.48~201.55 kWh/㎡·year and in the case of medium-sized buildings, the range was 92.77~177.89 kWh/㎡·year. In the case of small buildings, it was found that the energy consumption was 73.24 kWh/㎡·year in electronic device, 34.31 kWh/㎡·year in hot water supply, and 18.37 kWh/㎡·year in heating. In the case of medium-sized buildings, electronic devices was 73.08 kWh/㎡․year, lighting was 18.35 kWh/㎡·year and heating, 15.37 kWh/㎡·year. In all of the study buildings, the peak heating energy use was observed from 8:00 a.m. to 10:00 a.m during the winter, and the peak power management was required. Energy use at and around the midnight hour is confirmed to be 40~60% of weekly working hours, so it is necessary to manage power use at night time as well as during the day.BR In order to improve the accuracy of future studies, it is necessary to make efforts to secure the data with standardized energy measuring units for the various type of buildings.

Control strategies for dynamic insulation materials applied to commercial buildings

This paper evaluates the potential heating and cooling energy use and cost savings associated with improved controls of dynamic insulation materials (DIMs) when used in multilayered wall assemblies for commercial office buildings. Using a genetic algorithm (GA) based optimization technique, optimum R-value settings are identified for the DIMs throughout the year. These optimal R-value settings are selected to maximize energy cost savings when compared to traditional static insulated wall systems. It is found that optimal control settings for DIMs depend on several factors including building design and operation strategies as well as climatic conditions. Moreover, the optimal settings were found to depend on the orientation of exterior walls and well as the climatic conditions. Specifically, the analysis results indicate that north-facing walls are more active and frequently have to change their R-value in order to minimize the office building energy use and cost. When optimally controlled, DIMs could save up to 17% of annual heating and cooling energy costs for US office buildings.

An energy-efficient predictive control for HVAC systems applied to tertiary buildings based on regression techniques

Heating ventilation and air conditioning (HVAC) systems represent an important amount of the total energy use in office buildings, accounting for near 30%. Moreover, in countries affected by extreme climates HVAC systems’ contribution to energy demand increases up to 50%. Therefore, the automation of energy efficient strategies that act on the Building Energy Management System (BEMS) in order to improve building energy use becomes increasingly relevant. This paper delves into the devising of a novel HVAC optimization framework, coined as Next24h-Energy, which consists on a two-way communication system, an enhanced database management system and a set of machine learning algorithms based on random forest (RF) regression techniques mainly focused on providing an energy-efficient predictive control of the HVAC system. Therefore, the proposed framework achieves optimal HVAC ON/OFF and mechanical ventilation (MV) schedule operation that minimizes the energy consumption while keeps the building between a predefined indoor temperature margins. Simulation results assess the performance of the proposed Next 24h-Energy framework at a real office building named Mikeletegi 1 (M1) in Donostia-San Sebastian (Spain) yielding to excellent results and significant energy savings by virtue of its capability of adapting the parameters that control the HVAC schedule in a daily basis without affecting user comfort conditions. Specifically, the energy reduction for the test period is estimated in 48% for the heating and 39% for the cooling consumption.

A methodology for estimating office building energy use baselines by means of land use legislation and reference buildings

Understanding the energy demand of cities is a challenge especially in countries such as Brazil where the lack of building energy consumption benchmarks makes it hard to decide upon interventions in the building stock, as neither the existing nor the attainable energy performance are known. Aiming to overcome these difficulties this study develops a framework to estimate the Energy Use Intensity (EUI) baseline of an existing building stock category based on the investigation of land use regulations, a land tax database and field surveys. These offer a starting point to estimate current energy consumption and promising interventions without having to study specific buildings in depth. A case study of the high-rise commercial building stock took place in the city of Belo Horizonte, Brazil. Three representative typologies were identified. For each of these, archetype energy models were created in order to assess EUI. The analysis of the EUI baselines highlights differences between archetypes, explaining the impact of technical choices on the overall electricity consumption and indicating a growing tendency of energy consumption for forthcoming buildings. The archetype analysis also provides knowledge to identify the most promising energy savings potential and thus to support further energy policies in the city.

Impacts of climate change on U.S. building energy use by using downscaled hourly future weather data

With the growing concern of global climate change in the future, how building energy use pattern would change in response is of great interest and importance in drafting future building performance regulations and codes. In this paper, the outputs from global climate model (GCM) are integrated to typical meteorological year weather file to downscale and predict local hourly weather data in the context of U.S. climate regions using a “morphing” methodology. The “morphed” future hourly weather data is then used by EnergyPlus to predict future energy use pattern for residential building in the United States.Case studies in four representative cities in the U.S. show that climate change is to have great impacts on residential and office building energy use during the year of 2040–2069. The change of annual energy use is predicted to range from −1.64% to 14.07% for residential building and from −3.27% to −0.12% for office building under A2 scenario (a carbon emission scenarios defined by IPCC) in different regions. The research results suggest that the climate change will narrow the gap of energy use for residential buildings located in cold and hot climate regions in the U.S. and generally reduce office building energy use in the future. It is also found that the energy use of lightings and fans will slightly decrease in the future. Moreover, the growing peak electricity load during cooling seasons is going to exert greater pressure for the future grid.