Development and Testing of an Automated Building Commissioning Analysis Tool (ABCAT)

2010 ◽  
Author(s):  
John D. Bynum ◽  
David E. Claridge ◽  
Jonathan M. Curtin
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Weather Conditions ◽  
The United States ◽  
Commercial Buildings ◽  
Test Case ◽  
Analysis Tool ◽  
Component Level ◽  
Building Commissioning ◽  
Occupant Comfort

Experience has shown that buildings on average may consume 20% more energy than required for occupant comfort which by one estimate leads to $18 billion wasted annually on energy costs in commercial buildings in the United States. Experience and large scale studies of the benefits of commissioning have shown the effectiveness of these services in improving the energy efficiency of commercial buildings. While commissioning services do help reduce energy consumption and improve performance of buildings, the benefits of the commissioning tend to degrade over time. In order to prolong the benefits of commissioning, a prototype fault detection and diagnostic (FDD) tool intended to aid in reducing excess energy consumption known as an Automated Building Commissioning Analysis Tool (ABCAT) has been developed. ABCAT is a first principles based whole building level top down FDD tool which does not require the level of expertise and money often associated with more detailed component level methods. The model based ABCAT tool uses the ASHRAE Simplified Energy Analysis Procedure (SEAP) which requires a smaller number of inputs than more sophisticated simulation methods such as EnergyPlus or DOE-2. ABCAT utilizes a calibrated mathematical model, white box method, to predict energy consumption for given weather conditions. A detailed description of the methodology is presented along with test application results from more than 20 building years worth of retrospective applications and greater than five building years worth of live test case applications. In this testing, the ABCAT tool was used to successfully identify 24 significant energy consumption deviations in five retrospective applications and five significant energy consumption deviations in four live applications.

scholarly journals Building energy consumption prediction and energy control of large-scale shopping malls based on a noncentralized self-adaptive energy management control system

2020 ◽  
pp. 014459872092073
Author(s):  
Bao Peng ◽  
Hui-Min Zou ◽  
Peng-Fei Bai ◽  
Yu-Yang Feng
Keyword(s):  
Energy Consumption ◽  
Influencing Factors ◽  
Air Conditioning ◽  
Large Scale ◽  
Commercial Buildings ◽  
Shopping Malls ◽  
Consumption Values ◽  
Central Air Conditioning ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

Central air conditioning is the main energy-consuming equipment in modern large-scale commercial buildings. Its energy consumption generally accounts for more than 60% of the electricity load of an entire building, and there is a rising trend. Focusing on reducing central air conditioning energy consumption is a first priority to achieve energy savings in modern large-scale commercial buildings. To study the main influencing factors of central air conditioning energy consumption in large shopping malls, in-depth collection and analysis of energy consumption data of Shenzhen Tian-hong shopping mall were considered, and the impact of factors such as the basic composition of central air conditioning, time, and Shenzhen weather on the energy consumption of shopping malls was considered. The most representative Buji Rainbow store of the Rainbow Group is used as the research object. The influencing factors of central air conditioning on its energy consumption are divided into air conditioning pumps, host 1–1, host 1–2, host 2–1, and host 2–2. The power consumption of the freezer and the eight impact indicators of time and weather in Shenzhen were constructed using Pearson correlation coefficients and a long short-term memory neural network method to construct a regression model of the energy consumption prediction of the mall building. The average relative deviation between the predicted energy consumption values and the measured energy consumption values is less than 10%, which indicates that the main influencing factors selected in this paper can better explain the energy consumption of the mall, and the obtained energy consumption prediction model has high accuracy.

scholarly journals Daylight Harvesting Optimization for Commercial Buildings using AI Technique

2021 ◽  
pp. 8-14
Author(s):  
Gaurav Patil ◽  
Shravan Vishwakarma
Keyword(s):  
Energy Consumption ◽  
Weather Conditions ◽  
Commercial Buildings ◽  
Human Comfort ◽  
Global Energy ◽  
Daylight Harvesting ◽  
Global Energy Consumption

As Energy consumption in buildings increases considerably from year to year due to the increase in human comfort needs and services . In addition to weather conditions, several factors influence the energy consumption for cooling buildings, such as the structure of the walls, the window-to-wall ratio and the orientation of the building. The energy consumption of buildings has been reported to represent a relatively large proportion of global energy consumption.

Retrospective Testing of an Automated Building Commissioning Analysis Tool (ABCAT)

2009 ◽  
Author(s):  
Guanjing Lin ◽  
David E. Claridge
Keyword(s):  
Energy Consumption ◽  
Implementation Process ◽  
Building Energy ◽  
Energy Performance ◽  
Diagnostic Techniques ◽  
Analysis Tool ◽  
Building Energy Consumption ◽  
Building Commissioning ◽  
Consumption Data ◽  
Cost Efficient

Commissioning services have proven successful in reducing building energy consumption, but the optimal energy performance obtained by commissioning may subsequently degrade. Automated Building Commissioning Analysis Tool (ABCAT), which combines a calibrated simulation with diagnostic techniques, is a simple and cost efficient tool that can help maintain the optimal building energy performance after building commissioning. It can continuously monitor whole building energy consumption, warn operation personnel when an HVAC system problem has increased energy consumption, and assist them in identifying the possible cause(s) of the problem. This paper presents the results of a retrospective implementation of ABCAT on five buildings, each of which has at least three years of post-commissioning daily energy consumption data, on the Texas A&M University campus. The methodology of ABCAT is reviewed and the implementation process of ABCAT on one building is specifically illustrated. Eighteen faults were detected in 15 building-years of consumption data with a defined fault detection standard. The causes of some of the detected faults are verified with historical documentation. The remaining fault diagnoses remain unconfirmed due to data quality issues and incomplete information on maintenance performed in the buildings.

scholarly journals Modeling and Multiobjective Optimization of Indoor Airborne Disease Transmission Risk and Associated Energy Consumption for Building HVAC Systems

2021 ◽  
Author(s):  
Michael James Risbeck ◽  
Martin Z. Bazant ◽  
Zhanhong Jiang ◽  
Young M Lee ◽  
Kirk H Drees ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Disease Transmission ◽  
Weather Conditions ◽  
Infection Risk ◽  
Hvac Systems ◽  
Transmission Risk ◽  
Disease Spread ◽  
Risk Levels ◽  
Occupant Comfort ◽  
Airborne Disease

The COVID-19 pandemic has renewed interest in assessing how the operation of HVAC systems influences the risk of airborne disease transmission in buildings. Various processes, such as ventilation and filtration, have been shown to reduce the probability of disease spread by removing or deactivating exhaled aerosols that potentially contain infectious material. However, such qualitative recommendations fail to specify how much of these or other disinfection techniques are needed to achieve acceptable risk levels in a particular space. An additional complication is that application of these techniques inevitably increases energy costs, the magnitude of which can vary significantly based on local weather. Moreover, the operational flexibility available to the HVAC system may be inherently limited by equipment capacities and occupant comfort requirements. Given this knowledge gap, we propose a set of dynamical models that can be used to estimate airborne transmission risk and energy consumption for building HVAC systems, based on comfort preferences and weather conditions. By combining physics-based material balances with phenomenological models of the HVAC control system, it is possible to predict time-varying airflows and other HVAC variables, which are then used to calculate the key metrics. Through a variety of examples involving real and simulated commercial buildings, we show that our models can be used for monitoring purposes by applying them directly to transient building data as operated, or they may be embedded within a multi-objective optimization framework to evaluate the tradeoff between infection risk and energy consumption. By combining these applications, building managers can determine which spaces are in need of infection risk reduction and how to provide that reduction at the lowest energy cost. The key finding is that both the baseline infection risk and the most energy-efficient disinfection source can vary significantly from space to space and depend sensitively on the weather, thus underscoring the importance of the quantitative predictions provided by the models.

Adaptive Operation Decisions in Net Zero Building Clusters

2015 ◽  
Author(s):  
Philip Odonkor ◽  
Kemper Lewis
Keyword(s):  
Energy Consumption ◽  
Energy Savings ◽  
Weather Conditions ◽  
Energy Price ◽  
Operational Strategies ◽  
Pareto Analysis ◽  
Net Zero ◽  
Occupant Comfort ◽  
Energy Grid ◽  
Optimization Mechanism

In light of the growing strain on the energy grid and the increased awareness of the significant role buildings play within the energy ecosystem, the need for building operational strategies which minimize energy consumption has never been greater. One of the major hurdles impeding this realization primarily lies not in the lack of decision strategies, but in their inherent lack of adaptability. With most operational strategies partly dictated by a dynamic trio of social, economic and environmental factors which include occupant preference, energy price and weather conditions, it is important to realize and capitalize on this dynamism to open up new avenues for energy savings. This paper extends this idea by developing a dynamic optimization mechanism for Net-zero building clusters. A bi-level operation framework is presented to study the energy tradeoffs resulting from the adaptive measures adopted in response to hourly variations in energy price, energy consumption and indoor occupant comfort preferences. The experimental results verify the need for adaptive decision frameworks and demonstrate, through Pareto analysis, that the approach is capable of exploiting the energy saving opportunities made available through fluctuations in energy price and occupant comfort preferences.

scholarly journals A Comprehensive Analysis of Energy and Daylighting Impact of Window Shading Systems and Control Strategies on Commercial Buildings in the United States

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2401
Author(s):  
Niraj Kunwar ◽  
Mahabir Bhandari
Keyword(s):  
United States ◽  
Energy Demand ◽  
Energy Savings ◽  
Control Strategies ◽  
The United States ◽  
Commercial Buildings ◽  
Office Buildings ◽  
Increase Energy Efficiency ◽  
Occupant Comfort ◽  
The Impact

Commercial buildings consume approximately 1.9 EJ of energy in the United States, 50% of which is for heating, cooling, and lighting applications. It is estimated that windows contribute up to 34% of the energy used for heating and cooling. However, window retrofits are not often undertaken to increase energy efficiency because of the high cost and disruptive nature of window installation. Highly efficient window technologies would also need shading devices for glare prevention and visual comfort. An automated window shading system with an appropriate control strategy is a technology that can reduce energy demand, maintain occupant comfort, and enhance the aesthetics and privacy of the built environment. However, the benefits of the automated shades currently used by the shading industry are not well studied. The topic merits an analysis that will help building owners, designers and engineers, and utilities make informed decisions using knowledge of the impact of this technology on energy consumption, peak demand, daylighting, and occupant comfort. This study uses integrated daylight and whole-building energy simulation to evaluate the performance of various control strategies that the shading industry uses in commercial office buildings. The analysis was performed for three different vintages of medium office buildings at six different locations in United States. The results obtained show the control strategies enabled cooling energy savings of up to 40% using exterior shading, and lighting energy savings of up to 25%. The control strategies described can help building engineers and researchers explore different control methods used to control shading in actual buildings but rarely discussed in the literature. This information will give researchers the opportunity to investigate potential improvements in current technologies and their performance.

scholarly journals Simple Homogenization-Based Approach to Predict Raveling in Porous Asphalt

2020 ◽  
Vol 2674 (12) ◽  
pp. 263-277
Author(s):  
Hong Zhang ◽  
Kumar Anupam ◽  
Athanasios Skarpas ◽  
Cor Kasbergen ◽  
Sandra Erkens
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
Local Stress ◽  
Real Field ◽  
Stress And Strain ◽  
Analysis Tool ◽  
Component Level ◽  
Strain Fields ◽  
Porous Asphalt ◽  
Homogenization Technique

In the Netherlands, more than 80% of the highways are surfaced by porous asphalt (PA) mixes. The benefits of using PA mixes include, among others, the reduction of noise and the improvement of skid resistance. However, pavements with PA mixes are known to have a shorter lifetime and higher maintenance costs as compared with traditional dense asphalt mixes. Raveling is one of the most prominent distresses that occur on PA mix pavements. To analyze the raveling distress of a PA mix pavement, the stress and strain fields at the component level are required. Computational models based on finite element methods (FEM), discrete element methods (DEM), or both, can be used to compute local stress and strain fields. However, they require the development of large FEM meshes and large-scale computational facilities. As an alternative, the homogenization technique provides a way to calculate the stress and strain fields at the component level without the need for much computation power. This study aims to propose a new approach to analyze the raveling distress of a PA mix pavement by using the homogenization technique. To demonstrate the application of the proposed approach, a real field-like example was presented. In the real field-like example, the Mori–Tanaka model was used as a homogenization technique. The commonly available pavement analysis tool 3D-MOVE was used to compute the response of the analyzed pavement. In general, it was concluded that the homogenization technique could be a reliable and effective way to analyze the raveling distress of a PA mix pavement.

scholarly journals Using Machine Learning to Predict Retrofit Effects for a Commercial Building Portfolio

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4334
Author(s):  
Yujie Xu ◽  
Vivian Loftness ◽  
Edson Severnini
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Energy Savings ◽  
Detection System ◽  
Effective Means ◽  
The United States ◽  
Data Driven ◽  
Commercial Building ◽  
Carbon Footprints ◽  
Site Energy

Buildings account for 40% of the energy consumption and 31% of the CO2 emissions in the United States. Energy retrofits of existing buildings provide an effective means to reduce building consumption and carbon footprints. A key step in retrofit planning is to predict the effect of various potential retrofits on energy consumption. Decision-makers currently look to simulation-based tools for detailed assessments of a large range of retrofit options. However, simulations often require detailed building characteristic inputs, high expertise, and extensive computational power, presenting challenges for considering portfolios of buildings or evaluating large-scale policy proposals. Data-driven methods offer an alternative approach to retrofit analysis that could be more easily applied to portfolio-wide retrofit plans. However, current applications focus heavily on evaluating past retrofits, providing little decision support for future retrofits. This paper uses data from a portfolio of 550 federal buildings and demonstrates a data-driven approach to generalizing the heterogeneous treatment effect of past retrofits to predict future savings potential for assisting retrofit planning. The main findings include the following: (1) There is high variation in the predicted savings across retrofitted buildings, (2) GSALink, a dashboard tool and fault detection system, commissioning, and HVAC investments had the highest average savings among the six actions analyzed; and (3) by targeting high savers, there is a 110–300 billion Btu improvement potential for the portfolio in site energy savings (the equivalent of 12–32% of the portfolio-total site energy consumption).

Quality Control of Data in a Large-Scale Cancer Register Program

1966 ◽  
Vol 05 (02) ◽  
pp. 67-74 ◽  
Author(s):  
W. I. Lourie ◽  
W. Haenszeland
Keyword(s):  
United States ◽  
Quality Control ◽  
Cancer Patients ◽  
Large Scale ◽  
The United States ◽  
Newly Diagnosed ◽  
Related Data ◽  
Cancer Register ◽  
Independent Review ◽  
End Results

Quality control of data collected in the United States by the Cancer End Results Program utilizing punchcards prepared by participating registries in accordance with a Uniform Punchcard Code is discussed. Existing arrangements decentralize responsibility for editing and related data processing to the local registries with centralization of tabulating and statistical services in the End Results Section, National Cancer Institute. The most recent deck of punchcards represented over 600,000 cancer patients; approximately 50,000 newly diagnosed cases are added annually.Mechanical editing and inspection of punchcards and field audits are the principal tools for quality control. Mechanical editing of the punchcards includes testing for blank entries and detection of in-admissable or inconsistent codes. Highly improbable codes are subjected to special scrutiny. Field audits include the drawing of a 1-10 percent random sample of punchcards submitted by a registry; the charts are .then reabstracted and recoded by a NCI staff member and differences between the punchcard and the results of independent review are noted.

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