Energy Performance Of Commercial Buildings Research Articles

Laboratory testing methods to evaluate the reliability of occupancy sensors for commercial building applications

The energy performance of commercial buildings is greatly influenced by occupants which are highly variable and among the most unpredictable components of a building's operation. While most building control systems use fixed, predetermined occupancy schedules, these fixed occupancy levels can be quite different from actual occupancy. This can cause unnecessary energy consumption, particularly from heating, ventilation, and air conditioning (HVAC) and lighting systems which are responsible for approximately 60% of commercial buildings' energy use. The use of occupancy counting sensor systems integrated with building management system controls is one method that can be used to improve the energy-consuming performance of buildings. However, there is no standardized universal methodology and metrics to evaluate their reliability. The aim of this research is to develop a uniform evaluation methodology to assess the reliability of occupancy counting sensor systems in a controlled laboratory environment. The developed testing methodology includes both “typical” scenarios representing the occupancy scenarios of a typical commercial building, and “failure” testing scenarios which represent a range of potential scenarios that may impact a sensor system's reliability. These methods were then implemented in a case study to evaluate the performance of two novel occupancy counting sensor systems (i.e., door-centric, and camera-based). Results suggest that typical testing results can be used to compare the overall performance of the occupancy counting sensor systems; however, failure testing is also important to understand the weaknesses of the sensor system in order to select the suitable one for the intended use of the commercial building. In addition, the proposed methodology includes a modified confusion matrix which enables the ability to identify if failures are caused by over or under counting occupants and to what extent this occurs over the testing period.

Assessment of Dynamic Photovoltaic Shading Systems on Energy Performance of Commercial Buildings

Abstract In this paper, the energy benefits of dynamic photovoltaic (PV)-integrated overhang systems are evaluated for office buildings. The benefits of dynamic PV-integrated overhangs combined energy efficiency by decreasing both heating and cooling energy demand and on-site renewable power generation by deploying tracking PV panels. The energy performance of dynamic PV-integrated overhangs is considered for various design and operation conditions. Specifically, the impacts of various design features are considered such as overhang depth, window size, glazing type, and heating, ventilation, and air conditioning (HVAC) system type. Moreover, the effects of operating conditions are assessed including the location of the building and the control strategy of the dynamic PV-integrated systems such as load-tracking, demand-tracking, and PV-tracking. The results of the various analyses indicate that the heating and cooling energy demands of the dynamic overhangs are mostly affected by the window glazing type and window overhang depths. The analysis shows that the use of demand-tracking maximizes the energy performance of the dynamic PV-integrated overhangs when deployed to US office buildings.

Energy use and environmental degradation in Europe: evidence from panel nonlinear ARDL

This study examines the nonlinear effect of energy use including coal, gas, oil, and electricity on CO2 emissions in European Union (EU) countries. Annual panel data of twenty-one European Union countries i.e. Austria, Azerbaijan, Belarus, Bulgaria, Belgium, Denmark, Finland, France, Greece, Germany, Hungary, Ireland, Italy, Kazakhstan, Lithuania, Netherland, Norway, Spain, Slovakia, Switzerland, and Ukraine from 1990 to 2018 is used, while panel nonlinear ARDL methodology is applied for examining the long and short run association. Findings showed that an increase in the coal, electricity, and oil usage increase carbon dioxide emissions, while a reduction in coal, electricity, gas, and oil usage reduce carbon dioxide emissions in the long run. Results of panel nonlinear ARDL (NARDL) model prove the existence of asymmetric impact of energy usage including coal, electricity, and oil on CO2 emissions in European countries. Bidirectional causality exits among positive partial sum of gas usage and CO2 emissions, as well as among partial positive component of oil usage and CO2 emissions in the short run. Pollution needs to be controlled by using new and efficient technologies by making research and development centers more efficient and productive, while the usage of renewable energy resources should be promoted by replacing the traditional energy resources to reduce emissions. ENERGY STAR strategy can be used to improve the energy performance of commercial buildings and industrial plants as well as government has to formulate policies of production which are environment friendly.

Uncertainties in Non-Domestic Energy Performance Certificate Generating in the UK

In light of the recent launch of the Minimum Energy Efficiency Standard targeting the energy performance of commercial buildings, this study compares the energy performance certificates of three UK hotels generated by two different software, EDSL TAS and SBEM, both accredited by the UK government for the purpose. Upon finding the results discrepant, the study finds that the two software’s different assumptions for the air permeability rate contribute to the discrepancy. While modifying this value makes the results from the two software more aligned, further issues regarding the validation process arise. The study continues to find that the underlying issue can be found within the National Calculation Methodology’s assumption about domestic hot water consumption in hotels. These assumptions are compulsory to follow when generating a non-domestic energy performance certificate in the UK, therefore, any uncertainties within them can affect all the buildings seeking an energy performance certificate within that sector. Finally, the study discusses that, for meeting the carbon dioxide mitigation goals, it is necessary to make changes to the current procedure of energy performance certificate generating in the UK to increase its reliability.

Accuracy of different machine learning algorithms and added-value of predicting aggregated-level energy performance of commercial buildings

As with many other sectors, to improve the energy performance and energy neutrality requirements of individual buildings and groups of buildings, built environment is also making use of machine learning for improved energy demand predictions. The goal of achieving energy neutrality through maximized use of on-site produced renewable energy and attaining optimal level of energy performance at building-cluster level requires reliable short term (resolution shorter than one day) energy demand predictions. However, the prediction and analysis of the energy performance of buildings is still focused on the individual building level and not on small neighborhood scale or building clusters.In a smart grid context, to better understand electricity consumption at different spatial levels, prediction should be at both individual as well as at building-cluster levels, especially for neighborhoods with definite boundaries (such as universities, hospitals).Therefore, in this paper, using data from 47 commercial buildings, a number of machine learning algorithms were evaluated to predict the electricity demand at individual building level and aggregated level in hourly intervals. Predicting at hourly granularity is important to understand short-term dynamics, yet most of the neighborhood scale studies are limited to yearly, monthly, weekly, or daily data resolutions. Two years of data were used in training the model and the prediction was performed using another year of untrained data. Learning algorithms such as; boosted-tree, random forest, SVM-linear, quadratic, cubic, fine-Gaussian as well as ANN were all analysed and tested for predicting the electricity demand of individual and groups of buildings. The results showed that boosted-tree, random forest, and ANN provided the best outcomes for prediction at hourly granularity when metrics such as computational time and error accuracy are compared.

A model-based decision support tool for building portfolios under uncertainty

Abstract Despite abundant energy use data, few facilities managers have a good benchmark for tracking energy performance in commercial buildings. Building energy self-benchmarking is an effective means of comparing performance to expectations. This paper presents an improved theory for a decision support tool that can self-benchmark building energy performance, identify energy faults, and quantify their severity. Detailed building energy simulation modeling of a big-box retail store with open source software is accessible and economical to industry for generating performance benchmarks. Methods of parametric sampling and uncertainty analysis are enhanced with detailed parameter uncertainty characterization. Uncertainty and sensitivity analysis are used to adjust risk tolerance thresholds for each unique monitored end-use. A dynamic cost function allows utility theory to compute expected costs covering multiple criteria. Improved theory for decision support tool is tested on ten faulted model scenarios placed in three climate zones. Finally, we demonstrate fault response prioritization.

Benchmarking energy performance in commercial buildings

The main purpose of energy benchmarking in commercial buildings is to put actual energy consumption into perspective so that building owners and occupiers can take steps to reduce it. Many rating tools, such as the display energy certificate in the UK, are primarily based on floor area. This means that nearly empty buildings can receive a good rating while intensively utilised buildings are penalised, even though their energy use per person may be much lower. To provide more meaningful benchmarking requires consideration of the intensity of use. This paper proposes a simple approach to include occupancy density and hours of use in the benchmarking methodology for commercial buildings. The paper also discusses how landlord and tenant energy can be benchmarked separately to reflect who is responsible for the energy consumption. Finally, the paper suggests modifying the standard A to G rating scale used in the UK to better reflect the diversity of energy performance in commercial buildings.

Using multiple regression analysis to develop energy consumption indicators for commercial buildings in the U.S.

Multiple regression analysis plays an important role in evaluating the energy consumption of buildings. These models are commonly used to assess the energy performance of commercial buildings and to predict any potential for energy consumption reduction. In this study, the building simulation software DOE-2 was used to predict energy consumption. A total of 17 key building design variables were identified related to building envelope, building orientation, and occupant schedule. Since, building energy consumption depends on many operational and design parameters; large numbers of simulations are needed to generate data for the multiple regression models. To tackle this problem, a randomized approach was adopted to reduce the required number of simulations examining the whole design space. Monte Carlo simulation technique was used to generate thirty thousand combinations of design parameters, covering the full range for each climate region. In order to implement the Monte Carlo simulation, an in-house computer program was developed to interface with DOE-2 energy simulation software. Stepwise regression was used to reduce the number of parameters and only include the most effective parameters. R statistical analysis program was also used to develop the set of linear regression equations. Parametric study and sensitivity analysis between levels of most effective parameters were performed. The developing models can be used to estimate the energy consumption of office buildings in early stages of design.

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

Buildings currently account for 30–40 percent of total global energy consumption. In particular, commercial buildings are responsible for about 12 percent of global energy use and 21 percent of the United States’ energy use, and the energy demand of this sector continues to grow faster than other sectors. This increasing rate therefore raises a critical concern about improving the energy performance of commercial buildings. Recently, researchers have investigated ways in which understanding and improving occupants’ energy-consuming behaviors could function as a cost-effective approach to decreasing commercial buildings’ energy demands. The objective of this paper is to present a detailed, up-to-date review of various algorithms, models, and techniques employed in the pursuit of understanding and improving occupants’ energy-use behaviors in commercial buildings. Previous related studies are introduced and three main approaches are identified: (1) monitoring occupant-specific energy consumption; (2) Simulating occupant energy consumption behavior; and (3) improving occupant energy consumption behavior. The first approach employs intrusive and non-intrusive load-monitoring techniques to estimate the energy use of individual occupants. The second approach models diverse characteristics related to occupants’ energy-consuming behaviors in order to assess and predict such characteristics’ impacts on the energy performance of commercial buildings; this approach mostly utilizes agent-based modeling techniques to simulate actions and interactions between occupants and their built environment. The third approach employs occupancy-focused interventions to change occupants’ energy-use characteristics. Based on the detailed review of each approach, critical issues and current gaps in knowledge in the existing literature are discussed, and directions for future research opportunities in this field are provided.

Evaluating the impact of extreme energy use behavior on occupancy interventions in commercial buildings

Occupancy interventions, which are typically used to diffuse energy conservation practices among commercial building occupants, are showing promising but un-sustained energy savings. One possible explanation to the observed results is the presence of extreme energy users in the buildings who can have an important influence on their moderate peers and revoke the benefits of occupancy interventions. While this ‘extremism’ in human behavior phenomenon has been extensively studied in various social science fields, it has yet to be applied on the study of energy conservation from occupancy interventions in commercial buildings. Using agent-based modeling and data from actual commercial buildings, this paper evaluates the influence of extreme energy users (1) on their peers and on the energy performance of commercial buildings and (2) on the effectiveness of commonly implemented occupancy interventions. Parametric variations and statistical analyses indicate that extremism can highly affect the effectiveness of traditional occupancy interventions. In parallel, a combination of methods has shown to empower moderate energy users, helping diffuse and maintain energy conservation practices even in the presence of extreme energy users. The demonstrated capability to test and optimize occupancy interventions is expected to support and boost their adoption in large-scale energy conservation initiatives.

Development of an energy prediction tool for commercial buildings using case-based reasoning

Building energy prediction is a key factor to assess the energy performance of commercial buildings, identify operation issues and propose better operating strategies based on the forecast information. Different models have been used to forecast energy demand in buildings, including whole building energy simulation, regression analysis, and black-box models (e.g., artificial neural networks). This paper presents a different approach to predict the energy demand of commercial buildings using case-based reasoning (CBR). The proposed approach is evaluated using monitored data in a real office building located in Varennes, Québec. The energy demand is predicted at every hour for the following 3h using weather forecasts. The results show that during occupancy, 7:00–18:00, the coefficient of variance of the root-mean-square-error (CV-RMSE) is below 13.2%, the normalized mean bias error (NMBE) is below 5.8% and the root-mean-square-error (RMSE) is below 14kW. When the statistical criteria are calculated for all hours of the day, the CV-RMSE is 12.1%, the NMBE is 1.0% and the RMSE is 11kW. The case study demonstrates that CBR can be used for energy demand prediction and could be implemented in building operation systems.

Law briefing

Purpose – This legal update examines the implications for commercial landlords of regulations required to be made under Energy Act 2011, and to come into force by 1 April 2018. Under those regulations, a landlord “may not let” commercial premises falling below a specified energy performance rating (likely to be E). The sanction of market deprivation arguably represents a significant shift in the balance between incentive and compulsion as the key policy tool adopted by the UK Government in seeking to improve the energy performance of commercial buildings. The paper aims to discuss these issues. Design/methodology/approach – The paper sets out and reflects a practitioner's concerns relating to the proposed new sanction of market deprivation. It identifies and highlights practical difficulties likely to be encountered when considering the interaction of the proposed regulations with existing statute (e.g. Landlord and Tenant Act 1954) and contractual provisions such as tenant break clauses. Findings – The prospect of being unable to let commercial premises that fall below a specified energy efficiency rating must focus landlord attention on rights to enter to carry out improvement works. The paper identifies a potentially significant trap for landlords in the model green lease clauses issued by the Better Buildings Partnership where tenant consent is required. Research limitations/implications – The paper does not reflect extensive or exhaustive academic research. Consistently with its purpose a legal update, it identifies key issues likely to be encountered by practitioners. Practical implications – The principal practical implication is the need for landlords and their professional advisors to consider as part of any current lease negotiations the need to secure rights of entry for landlords to carry out improvement works where premises are at risk of falling below the energy performance rating likely to be specified in regulations to be made under Energy Act 2011, s 49. Originality/value – The paper reflects a practitioner's views, developed through client matters and also through designing and delivering professional training sessions, on the likely implications of the requirement for regulations under Energy Act 2011.

Cognitive biases in energy decisions during the planning, design, and construction of commercial buildings in the United States: an analytical framework and research needs

Despite a national goal for every building to achieve net-zero energy by 2050 and despite exemplary projects proving the technical and economic feasibility of much better energy performance, commercial buildings in the USA today use more energy per square foot than they ever have. Decisions made during planning, design, and construction (delivery) of commercial buildings appear systematically irrational, not maximizing utility for designers, occupants, or society. In other fields, notably economics, improved understanding of cognitive biases like “loss aversion” and “anchoring” has helped to explain seemingly irrational decision making. Related work has examined these cognitive biases for energy decisions made in an occupied building. Less clear is the role these cognitive biases play in the high-impact, long-term energy decisions made during commercial building delivery. As an initial step towards addressing this gap in understanding, this paper outlines key energy decisions in commercial building delivery and shows how cognitive biases may impact these decisions. A suggested approach to study these biases, and to design policies that address them, is provided. By highlighting these potential cognitive biases, based on an understanding of the building delivery process, this paper aims to engage those with relevant expertise in the behavioral and social sciences to help address the decision making that is preventing progress towards improved energy performance in commercial buildings.

An ETTV-based approach to improving the energy performance of commercial buildings

This paper aims to study the various parameters that affect the energy performance of commercial buildings in Singapore. The parameters are diverse, ranging from characteristics of construction of the walls and windows, to the various system settings and types within the building. Building energy performance is measured via two key indexes, namely, the Envelope Thermal Transfer Value (ETTV) and the annual cooling energy requirement ( E c). Parameters related to these two indexes are identified. An additional parameter, the solar absorptance of the wall, is further incorporated to calibrate the ETTV equation. A relative ranking on the functional parameters of ETTV has been performed to evaluate their effectiveness in lowering the ETTV of buildings. In addition, the impact of using cladding on ETTV is also studied. A correlation for E c, expressed in the form of a simple linear equation, has been developed. This correlation accounts for the internal building loads, envelope loads, operating schedules and efficiency of the cooling equipment. Finally, ETTV and E c have been employed to study the effects of chiller over-sizing and ventilation rates on building cooling energy. In the pursuit for better energy-efficient buildings, the approach presented in this paper contributes to the construct of sustainable energy-efficient built-environment.

Opaque envelope parameters versus energy consumption in commercial buildings in Brazil

This article presents an analysis of the energy performance of commercial buildings as a function of the influence of the overall heat transfer coefficient of the external walls and roof. Cases with different internal load densities, exterior absorptance, patterns of use, window-to-wall ratios and other parameters were simulated to analyse their influence on the annual energy consumption. The analysis was carried out through computer simulation using the EnergyPlus program and weather files for Florianopolis, Curitiba and Sao Luis. Two building typologies were adopted: a five-storey office building and a one-storey commercial store. These results show that the limits adopted by ASHRAE Standard 90.1 can be exceeded, depending on the case and climate analysed, as using high values of overall heat transfer coefficient in the walls can help to dissipate the internal heat gain to the exterior, minimizing the air conditioning energy consumption.

Energy saving by realistic design data for commercial buildings in Hong Kong

Oversized equipment is one of the key factors for poor energy performance of commercial buildings in Hong Kong. Similar situations exist also in many buildings in different parts of the world. The use of realistic design criteria has been identified as an effective method to reduce the equipment oversizing problem. A set of realistic design criteria for lighting power density, occupation density, and appliances' load-intensity for various types of premises have been established based on data obtained in walk-through surveys of energy end-uses in 31 Hong Kong office buildings. Potential electricity savings through the use of realistic design criteria were estimated to be 6–22% of the electricity consumption of Hong Kong, which corresponds to an annual cost of HK$12.2–44.7 billion.

Incremental costs within the design process for energy efficient buildings

Two demonstration programmes by the Canadian government, C-2000 and Commercial Buildings Incentive Programme (CBIP), are described with their effects of on the design and delivery process to improve the energy performance of commercial buildings. Both programmes operated on the assumption that design for high performance requires extra design time and also incur extra costs for contracted specialists and energy simulations. This was achieved through the provision of funding additional design time and providing support to the client and design team. A survey of completed projects within these programmes was undertaken to validate this assumption. Significant cost savings for clients were demonstrated at a modest and acceptable increment in design costs. The provision of modest governmental funding for the design stage has been a successful policy, with positive feedback from clients and operating cost savings offset by modest increases in capital cost or design time. Le présent document présente deux programmes de démonstration mis au point par le gouvernement Canadien: ‘C-2000’ et ‘Commercial Buildings Incentive Programme (CBIP)’. Il décrit leurs effets sur les processus de conception et de livraison afin d'améliorer les performances énergétiques des bâtiments commerciaux. Ces deux programmes sont basés sur l'hypothèse qu'une conception très performante nécessite un délai de conception supplémentaire et engendre donc des surcoûts pour les spécialistes engagés et les simulations énergétiques requises. Ces conditions ont été remplies grâce au financement d'un délai de conception supplémentaire et à la fourniture d'une assistance au client et à l'équipe de conception. Une étude de projets complets menée dans le cadre de ces programmes a été entreprise afin de valider cette hypothèse. La réduction significative des coûts pour les clients a été démontrée, ainsi qu'une augmentation modeste et acceptable des coûts de conception. La politique gouvernementale consistant à apporter un financement modeste lors de la phase de conception a fait ses preuves et a eu un impact positif sur les clients. De plus, des économies ont été réalisées en termes de coûts d'exploitation grâce à la faible augmentation du coût de financement ou du délai de conception.