Energy Efficiency In Commercial Buildings Research Articles

Optimizing Energy Efficiency in Commercial Buildings through Advanced Building Strategies

The pressing issue of climate change and its correlation with greenhouse gas emissions has propelled a heightened emphasis on energy-efficient building practices, particularly within the commercial sector. This study delves into advanced design strategies aimed at maximizing energy efficiency while upholding quality and comfort standards. By scrutinizing a functioning commercial building in Gurugram, Haryana, data was gathered to construct an energy model using eQUEST. Analysis encompassed various building facets such as envelope components (e.g., walls, roofs, and windows), HVAC systems, lighting, and occupancy patterns. The outcomes furnish valuable insights into pivotal factors influencing energy efficiency and propose tailored strategies for integration into commercial building design. This methodical approach, informed by case evaluation, seeks to optimize energy performance and cost-effectiveness across diverse building types and climates.

Energy optimization for HVAC systems in multi-VAV open offices: A deep reinforcement learning approach

With global warming intensifying and resource conflicts escalating, the world is undergoing a transformative shift toward sustainable practices and energy-efficient solutions. With more than 32% of the global energy used by commercial and residential buildings, there is an urgent need to revisit traditional approaches to Building Energy Management (BEM). Within a BEMSplatform, regulating the operation of Heating, Ventilation, and Air Conditioning (HVAC) systems is more important, noting that HVAC systems account for about 40% of the total energy cost in the commercial sector.This paper offers a Deep Reinforcement Learning (DRL) algorithm as a data-driven approach to controlling HVAC operation to enhance the energy efficiency of commercial buildings with open offices while ensuring thermal comfort for occupants in different zones. Compared to alternative methods such as rule-based models and model-predictive control, data-driven models have shown promising results in optimizing building energy consumption without the need for building-specific thresholds, prior knowledge about the underlying physics of heat distribution, and digital mapping of the airflow. Despite the astonishing performance of modern DRL methods in controlling energy management, a particular energy-saving solution for open-plan offices with multiple Variable Air Volume (VAV) systems, where different zones cannot be treated independently, is still missing. Also, some of the existing methods suffer from key issues such as long training time and lack of generalizability for using over-complicated models, incorporating external factors that are hard to model and characterize, and including factors that are not typically accessible. To solve these issues, we propose a low-complexity DRL-based model with multi-input multi-output architecture for the HVAC energy optimization of open-plan offices, which uses only a handful of controllable and accessible factors. The efficacy of our solution is evaluated through extensive analysis of the overall energy consumption and thermal comfort levels compared to a baseline system based on the existing HVAC schedule from a real case. This comparison shows that our method achieves 37% savings in energy consumption with minimum temperature violation (<1%) of the desired temperature range during work hours. It takes only a total of 40 min for 5 epochs (about 7.75 min per epoch) to train a network with superior performance and covering diverse conditions for its low-complexity architecture; therefore, it easily adapts to changes in the building setups, weather conditions, occupancy rate, etc. Moreover, by enforcing smoothness on the control strategy, we suppress the frequent and unpleasant on/off transitions on HVAC units to avoid occupant discomfort and potential damage to the system. The generalizability of our model is verified by applying it to different building models and under various weather conditions.

ANÁLISE SISTEMÁTICA DA CONEXÃO DE EFICIÊNCIA ENERGÉTICA EM EDIFICAÇÕES COMERCIAIS COM OS OBJETIVOS DO DESENVOLVIMENTO SUSTENTÁVEL E AS MUDANÇAS CLIMÁTICAS

Buildings have a huge role in the increase of energy consumption and in the energy-related CO2 emissions. Buildings are also important to the society's transition to a low-carbon economy, with increased energy efficiency and lower environmental impacts. This research aimed to identify through a systematic review of the literature relevant existing studies on the theme of energy efficiency in commercial buildings and analyze their connection with Sustainable Development Goals (SDG) and Climate Mitigation. The databases Web of Science and Scopus were used with specific search criteria and resulted in 227 articles published in the last decade. This research illustrates a comprehensive approach related to the panorama of the relation between articles and SDGs and climate mitigation. In addition, the discussion of the literature review highlights examples with benefits and barriers forthe implementation of energy efficiency in commercial buildings, showing its relevant features.

Role of façade shades in energy efficiency in commercial buildings

Energy consumption and production all over the world is a key contributor to climate change due to resultant emissions of CO2. To meet the national goal of achieving net zero carbon emissions by 2070, the current trends in energy consumption need to be addressed, with the building sector accounting for 40% of the global energy consumption. In India, the heating, cooling and lighting load of commercial buildings has increased from 32% to 46% in the past two decades. Energy demands can sustainably be reduced by means of energy conservation measures, particularly during the building design stage. As demands for new commercial spaces increased in crowded cities, the multi-storeyed glass façade building emerged out and continues to be a popular building design. This paper examines the role of façade shading elements in pulling down the energy demands and harnessing required daylight for lighting comfort. Daylight, energy, solar insolation and glare patterns are analysed in existing building in Noida on the basis of which different shading configurations are proposed by means of computer simulation, using Ecotect, eQUEST software and Radiance tool. Based on the analysis, three cogent designs are discussed and the optimum shade design is identified.

Building Energy Performance Certificate—A Relevant Indicator of Actual Energy Consumption and Savings?

A building energy performance gap can be illustrated as the difference between the theoretical (methodologically defined) and the actual energy consumption. In EU countries, Energy Performance Certificates are issued when buildings are constructed, sold, or leased. This information is the first step in order to evaluate the energy performance of the building stock. In Serbia, when issuing an energy certificate, the adopted national methodology recognizes only energy consumption for heating. The main purpose of this paper is to evaluate the energy gap and estimate the relevance of an Energy Performance Certificate to meet the national energy efficiency or carbon target. An Energy Performance Certificate determines the theoretical residential and commercial building energy efficiency or its “design intent”. This research stresses the necessity of measuring and achieving reductions in actual energy consumption through system regulation and consumers’ self-awareness in buildings. The research compares the performance of the building stock (135) that is connected to the District Heating System (DHS), with its own integrated heat meter, to Individual Gas Boiler (IGB) systems (18), in the city of Novi Sad, Serbia, built after 2014. For the purpose of comparing energy consumption, 16 buildings were selected that are very similar in terms of design, operation, and location. The data used are derived from metered consumption data, official evidence of city service companies, and Energy Performance Certificates of the considered buildings. We have determined that IGB systems have a much wider specific annual performance gap (11.19–101 kWh/m2a) than the buildings in the DHS (3.16–18.58 kWh/m2a).

In-situ application of an ANN algorithm for optimized chilled and condenser water temperatures set-point during cooling operation

In this study, an artificial neural network (ANN) based real-time predictive control and optimization algorithm for a chiller based cooling system was developed and applied to an actual building to analyze its cooling energy saving effects through in-situ application and actual measurements. For this purpose, we set the cooling tower's condenser water outlet temperature and the chiller's chilled water outlet temperature as the system control variables. To evaluate the algorithm performance, we compared and analyzed the electric consumption and the COP when the chilled and condenser water temperatures were controlled conventionally and controlled based on the ANN. As a result, the ANN model's accuracy was high, with a Cv(RMSE) of 4.9%. In addition, the ANN based control algorithm's energy analysis showed that the average energy saving rate for the chiller was 24.7% and that the total average energy saving effect for the chiller and cooling towers was 7.4%. The results confirmed that the proposed MPC algorithm could contribute to improved HVAC energy efficiency in commercial buildings.

A screening method for lowering customer acquisition cost in small commercial building energy efficiency projects

The retrofit of buildings to improve energy efficiency represents a vast opportunity in the construction market with the potential to reduce energy demand, create jobs, and achieve environmental benefits. Multiple challenges need to be overcome to unleash this potential and realize the benefits of investments in building energy retrofit projects. Small-/medium-sized commercial buildings are typically underserved by the energy retrofit industry due to the lack of owners’ ability to manage and finance energy efficiency improvements. In the case of energy-intensive and low-margin types of commercial buildings such as restaurants, the potential for energy savings is high, but the favorable conditions that enable retrofits, such as a pending renovation, propensity of clients to conserve energy, and willingness to take time for a retrofit, are scarce. In this research, the development of small energy efficiency projects is examined by proposing a screening method to find prospective candidates for financed energy efficiency projects in a targeted region. This method enables the co-creators of energy retrofit projects to predict the decision-making behavior of business owners and their willingness to invest in energy retrofit based on their restaurants’ attributes in social media. As part of a current initiative, supported by the Pennsylvania Department of Environmental Protection, a series of design, development, and delivery of retrofit projects in the context of food services was closely studied. The initial focus was on the Philadelphia region as a model for broader statewide applications. Restaurant owners were surveyed, and information was collected about their buildings’ energy systems, utility bills, and their willingness and/or capacity to invest in energy efficiency upgrades. A “Philadelphia restaurants” dataset was then scraped from the Yelp website. The data includes information about restaurant ratings, number of reviewers, price range, working hours, and ambiance. A methodology was developed to analyze the data and to find patterns and criteria that could possibly influence the owner’s decision for investing in energy performance improvement. To identify patterns, two methods were employed: (1) several focus groups over program period with team members who played roles in project development phase and (2) statistical analysis to identify the factors contributing to successful projects. Using these screening questions from focus groups and the results from statistical analysis, the retrofit likelihood can be estimated for each individual project. This methodology can be applied to larger datasets and would enable the investigators to screen the whole market and prioritize the targets for business recruitment.

Improving energy efficiency of commercial buildings by Combined Heat Cooling and Power plants

Commercial buildings play a key-role in the energy consumption of the building sectors. Recent statistics have shown that as the number of commercial buildings is continuously increasing, their effects on energy consumption are expected to grow. These buildings are characterized by high energy demand mainly due to lighting and HVAC requirements. Rooms of energy saving exist by considering that: (i) electricity demands and HVAC requirements occur simultaneously during the day and (ii) both demands are currently satisfied by using separate energy systems. It is apparent that the adoption of polygeneration systems could represent a valid solution to achieve energy savings. To this aim, the paper investigated the profitability of a trigeneration system for commercial buildings, considering a big Do It Yourself shop located in the northern part of Italy, as case study. The analysis was based on (i) energy consumption data collected by energy-audits and (ii) a profit-oriented management strategy for the trigeneration systems proposed in literature. Results showed that trigeneration represents a profitable energy conversion system thanks to revenues achieved by selling surplus electricity and the support of financial mechanism for “High-Efficient” eligibility. In comparison with the currently adopted energy conversion systems, important reductions in energy consumption and CO2 emissions are observed.

Reflective Roofing Use on Commercial Buildings in the United States: An Energy Type and Cost Analysis

Reflective roof membranes have been shown to lower air conditioning costs by reducing the influx of thermal energy through a roof assembly and into the building. However, there have been studies suggesting that reflective roofs in cold climates give rise to increased winter heating costs, due to the lack of absorption of solar thermal energy. Such studies could be used to justify continued use of dark, absorptive roof membranes even in areas that are striving to reduce urban heat island effects and/or lower contributions to global warming. In a prior modeling study, by this author and others, based on gas heat, it was found that reflective roofing provides net annual energy savings so long as air conditioning was used. Studies by others have suggested that when electric heat is used, the winter heating cost savings associated with non-reflective roofing outweigh summer air conditioning cost savings with reflective roofing. However, these studies did not take into account electric demand charges. Therefore, this present study modeled the energy efficiency of commercial buildings in order to compare the effect of gas versus electric heat with varying levels of demand charge included, on the net energy efficiency. Four different levels of demand charges were compared, along with three levels of solar reflectance for thirteen cities located throughout the US. In every studied case, when gas heat was assumed, net annual energy savings were predicted for reflective membranes. For electric heat, net energy savings were achieved in most cases even when demand charges were zero. In three northernmost cities, this was the case provided that demand charges exceeded a relatively small minimum. This finding suggests that reflective roofing provides for net energy efficiency improvements in most US cities and all cities when demand charges exceed USD 6.25 Therefore, efforts by cities to encourage reflective roofing as part of urban heat island effect mitigation programs should not be reduced.

Building Relationships: Using Embedded Plug Load Sensors for Occupant Network Inference

Understanding the underlying structure of building occupant dynamics is crucial to improving the effectiveness and energy efficiency of commercial buildings, as occupants fundamentally drive building design and operation. In current practice, we largely account for occupant behavior in the design and management of buildings through rudimentary schedules of presence or absence. However, the increasing availability of embedded sensors—such as plug load sensors—offers an opportunity not only to monitor occupants’ activity patterns but also to use these patterns to gain insight into the network structure of occupants. In this letter, we present a statistical methodology for inferring this network, which comprises social, spatial, and organizational ties among occupants. We apply our method to a 7-person office environment in Northern California, and we compare the inferred networks to ground truth social, spatial, and organizational networks obtained through validated survey questions. We demonstrate that this approach offers insights into the complex nature of occupant dynamics, which can ultimately serve as inputs into building design strategies that minimize energy consumption and improve occupant well-being.

Data-driven building load profiling and energy management

Commercial buildings consume a lot of energy and contribute a significant part of greenhouse gas emission. Many energy-saving or green-building initiatives were compromised by equipment and human-related faults under the umbrella of poor facility management. Data-driven building energy management is a cost-effective approach to improve energy efficiency of commercial buildings, and gains more and more popularity worldwide with the deployment of smart metering systems. This paper developed a systematic process of using smart metering data to quantify building daily load profiles (i.e. energy consumption patterns) with a set of statistics, e.g. base load, peak load, rising time and so on. Then prediction models of these building load statistics are constructed from historical training data consisting of energy consumption, environment and holiday information. At last residuals of the prediction models are analyzed to form statistical control charts. As a result anomaly energy consumption could be detected by comparing the predicted statistics and observed ones, which will help building managers to locate problems just in time. The effectiveness of the proposed solution is verified through real-world data analysis and computational studies.

Energy Efficient Designs of Sustainable Buildings in Urban Environment.

The Developing communities in their path for rapid development is endeavoring to make all necessary and appropriate measures to enhance the efficiency of energy utilization and increase the beneficiation of the energy resources. The energy production, transmission, distribution and utilization efficiency becomes a vital factor and measure of national development. Governmental organizations were established earlier to be responsible for energy planning and efficient utilization, information dissemination and capacity building as well as devising the necessary codes and standards. Throughout the Nation Energy resources are widely used and consumption rates are in general exceeding the International accepted values. Energy rationalization and audit exercises were developed and monitored by Governmental authorities, Universities and Research centers through the past two decades with a definitive positive energy reduction and beneficiation. The development of the relevant codes for Residential and Commercial Energy Efficiency in Building is underway through the governmental bodies responsible for the research, training and development in the building Technology sector and is the umbrella under which the National and Unified Arab Codes are developed and issued.

Investigating HVAC Thermal Comfort and Energy Efficiency in Commercial Buildings

Investigating HVAC Thermal Comfort and Energy Efficiency in Commercial Buildings

Role of ICT in enhancing energy efficiency of commercial buildings: Case study of optimized energy control system in hospital “P”

The increase in oil prices and energy consumption has been a great burden for Korea, which significantly depends on imported, foreign energy sources. In this context, the reduction of building energy consumption, which comprises 40% of total energy consumption, is a very important issue. The use of ICT to optimize energy control of building has surfaced as an effective solution. Therefore, this research empirically analyzed a hospital “P” that implemented ICT-based energy consumption and cost reduction initiative. The hospital first replaced the existing inefficient absorption type heating/cooling system with a hot water storage and pump system. This was accompanied by a change in electricity plans to maximize cost reduction. Second, the hospital applied an ICT-based optimized control algorithm that considers surrounding factors (external temperature and changes in energy demands). The analysis of these mechanisms indicates that the ICT-based energy consumption and cost reduction initiative for hospitals can reduce energy consumption by 52.7% by replacing low-efficiency equipment and additionally by 25.7% with implementation of an optimized control algorithm. Therefore, the implementation of the explored mechanisms will provide effective energy consumption reduction opportunities for other hospitals and buildings with high energy consumption.

In-Depth Analysis of Energy Efficiency Related Factors in Commercial Buildings Using Data Cube and Association Rule Mining

Significant amounts of energy are consumed in the commercial building sector, resulting in various adverse environmental issues. To reduce energy consumption and improve energy efficiency in commercial buildings, it is necessary to develop effective methods for analyzing building energy use. In this study, we propose a data cube model combined with association rule mining for more flexible and detailed analysis of building energy consumption profiles using the Commercial Buildings Energy Consumption Survey (CBECS) dataset, which has accumulated over 6700 existing commercial buildings across the U.S.A. Based on the data cube model, a multidimensional commercial sector building energy analysis was performed based upon on-line analytical processing (OLAP) operations to assess the energy efficiency according to building factors with various levels of abstraction. Furthermore, the proposed analysis system provided useful information that represented a set of energy efficient combinations by applying the association rule mining method. We validated the feasibility and applicability of the proposed analysis model by structuring a building energy analysis system and applying it to different building types, weather conditions, composite materials, and heating/cooling systems of the multitude of commercial buildings classified in the CBECS dataset.

Improving building energy efficiency in India: State-level analysis of building energy efficiency policies

India is expected to add 40 billion m2 of new buildings till 2050. Buildings are responsible for one third of India's total energy consumption today and building energy use is expected to continue growing driven by rapid income and population growth. The implementation of the Energy Conservation Building Code (ECBC) is one of the measures to improve building energy efficiency. Using the Global Change Assessment Model, this study assesses growth in the buildings sector and impacts of building energy policies in Gujarat, which would help the state adopt ECBC and expand building energy efficiency programs. Without building energy policies, building energy use in Gujarat would grow by 15 times in commercial buildings and 4 times in urban residential buildings between 2010 and 2050. ECBC improves energy efficiency in commercial buildings and could reduce building electricity use in Gujarat by 20% in 2050, compared to the no policy scenario. Having energy codes for both commercial and residential buildings could result in additional 10% savings in electricity use. To achieve these intended savings, it is critical to build capacity and institution for robust code implementation.

The effectiveness of US energy efficiency building labels

Information programs are promising strategies to encourage investments in energy efficiency in commercial buildings. However, the realized effectiveness of these programs has not yet been estimated on a large scale. Here we take advantage of a large sample of monthly electricity consumption data for 178,777 commercial buildings in Los Angeles to analyse energy savings and emissions reductions from three major programs designed to encourage efficiency: the US Department of Energy’s Better Buildings Challenge, the US Environmental Protection Agency’s Energy Star program and the US Green Building Council’s Leadership in Energy and Environmental Design (LEED) program. Using matching techniques, we find energy savings that range from 18% to 30%, depending on the program. These savings represent a reduction of 210 million kilowatt-hours or 145 kilotons of CO2 equivalent emissions per year. However, we also find that these programs do not substantially reduce emissions in small and medium sized buildings, which represent about two-thirds of commercial sector building emissions. To tackle the high energy consumption of buildings, information programs to promote investment in energy efficiency measures have been introduced. This study compares the effectiveness of three US programs and finds that despite large energy savings, progress is lacking for small and medium sized buildings.

Potential Barriers to Improving Energy Efficiency in Commercial Buildings: The Case of Supermarket Refrigeration

According to engineering analyses, energy-intensive commercial buildings have ample opportunities to reduce energy use while saving money, but many seemingly profitable strategies go unadopted. This study explores potential barriers to energy- and refrigerant-reducing investments in supermarket refrigeration. While supermarkets are large energy users, energy and refrigerant account for a relatively small proportion of expenditures. However, due to the intensely competitive nature of the industry, reducing refrigeration costs could have a sizable impact on supermarkets’ bottom lines. Given the scarcity of empirical data in this context, we conducted focus groups and interviews with U.S. supermarket representatives to elicit information about potential barriers to investment and used content analysis to qualitatively evaluate results. Participant statements suggest that the rate at which they adopt specific energy-reducing refrigeration technologies and practices correlates fairly well with estimates of expected payback. Uncertainty and imperfect information about the performance of new technologies, high opportunity costs of capital, and tradeoffs with other valued system attributes such as reliability and customer appeal were the most pervasive potential barriers discussed by participants, although split incentives between firms and contractors or employees also played a role for some firms. Our assessment is that these factors have moderately limited or slowed investments in energy-saving refrigeration technologies for many firms; only in the cases of uncertainty/imperfect information and split incentives between firms and contractors or employees are these barriers potentially indicative of market failures. In addition, in several instances we identify ways in which analysts could improve engineering-based net present value calculations to make them more consistent with actual firm decisions.

Statistically informed static pressure control in multiple-zone VAV systems

The proliferation of Building Automation Systems (BAS) has enabled the development of and use of more complex algorithms for controlling HVAC systems and increase energy efficiency in commercial buildings. One such complex algorithm, static pressure reset, which is associated with minimization of the static pressure in the supply air duct at all times while still maintaining zonal comfort—is a proven low cost means to reduce fan power consumption in Variable Air Volume (VAV) systems.Here we explore the possibility of utilizing historical BAS data to estimate annual energy savings from static pressure reset in any facility. The approach employed first establishes a statistical relationship between the effective system loss coefficient as a function of the mean VAV damper position using the measured duct static pressure, VAV damper positions and airflows. Observed is that the system loss coefficient decreases with an increase in mean VAV damper position. Thus, the desired control algorithm for minimizing fan power should seek to maximize the individual VAV damper positions needed for comfort. However, for a given duct static pressure, control sensitivity is diminished as more dampers are opened fully. Application of Static Pressure Reset in a number of buildings reveals that there is a maximum number of zonal dampers that can be fully opened before control robustness diminishes. In this condition, any additional increase in damper position has little effect on the system and zonal flowrates. If more flow is required, the static pressure must be increased. In this context, given the system loss relationship with mean damper position and the estimated maximum number of dampers that can be opened, the potential increase in the mean damper position to a value achievable if the maximum number of dampers are opened can be estimated at all times in the study period. This permits estimation of the minimal static pressure required. Savings can be estimated for the historical conditions based upon comparison of the predicted fan power based upon the optimal static pressure with the actual fan power. This methodology was tested on three different buildings with varying system characteristics. Savings estimates correlated well to the savings actually realized from static pressure reset. This result has significant implications for energy service providers, who could use the predictions to guarantee savings from static pressure reset.

Small buildings, big impacts: The role of small commercial building energy efficiency case studies in 2030 Districts

Small commercial buildings, or those comprising less than 50,000 square feet of floor area, make up 90% of the total number of buildings in the United States. Though these buildings currently account for less than 50% of total energy consumption in the U.S., this statistic is expected to change as larger commercial buildings become more efficient and thus account for a smaller percentage of commercial building energy consumption. This paper describes the efforts of a multi-organization collaboration and their demonstration partners in developing a library of case studies that promote and facilitate energy efficiency in the small commercial buildings market as well as a case study template that standardized the library. Case studies address five identified barriers to energy efficiency in the small commercial market, specifically, lack of: (1) access to centralized, comprehensive, and consistent information about how to achieve energy targets, (2) reasonably achievable energy targets, (3) access to tools that measure buildings’ progress toward targets, (4) financial incentives that make the reduction effort attractive, and (5) effective models of how disparate stakeholders can collaborate in commercial centers to reach targets. The case study library can be organized by location, building type, project size, energy savings, end uses impacted, and retrofit measures. This paper discusses the process of developing the library and case study template. Finally, the paper presents next steps for the library and explores energy savings potential its widespread use.