- +1-402-403-0090
Clayton Miller
National University of Singapore, Department of Building, Faculty Member
- University of Nebraska Lincoln, Architectural Engineering, AlumnusSwiss Federal Institute of Technology (ETH), Architecture, Graduate Studentadd
- Sustainable Building Design, Energy Efficiency Buildings, Architectural Engineering, Building Energy Simulation, Model Predictive Control (MPC), Energy Efficiency in Buildings, and 26 morePattern Recognition, Building Physics, RETROFITTING OF BUILDINGS, Building Performance Evaluation, Architecture, Python, Building Technology, Facilities Management, Lean Management, Data-driven Decision Making, Energyplus, Philosophy, Engineering, Data Mining, Energy, Numerical Analysis, Heat Transfer, Energy and exergy based analysis in thermal systems, Tropical climates, Structures and Fire Engineering, Building Performance Simulation, HVAC / Building Services, Building Services (Architecture), Energy Efficient Retrofitting, Revit, and Visual Analyticsedit
- General Research Focus: - Building Operations Benchmarking, Diagnostics and Optimization - Building Energy Simulation... moreGeneral Research Focus:
- Building Operations Benchmarking, Diagnostics and Optimization
- Building Energy Simulation
Specific research topics which I'm interested in exploring:
- Utilization of structured data contained in existing building simulation and BIM workflow (from integrated design and/or LEED compliance) for use in operations optimization and diagnostics
- Defining optimized balance between data-driven (black box) and first principles-based (white box) approaches
- Data quality screening from common building and energy management systems
- Taking energy information systems for buildings to the next level through specific use 'app' development on new platforms which are being release by major vendors, ie: SkySpark by SkyFoundry, Panoptix by Johnson Controls, Indie Energy Network by Indie Energyedit - Arno Schlüteredit
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ABSTRACT Building performance research using various informatics techniques has progressed extensively in the last twenty years by advancing the fields of automated fault detection and diagnostics (AFDD), commissioning, data mining, and... more
ABSTRACT Building performance research using various informatics techniques has progressed extensively in the last twenty years by advancing the fields of automated fault detection and diagnostics (AFDD), commissioning, data mining, and visualization for commercial buildings. Despite this effort, it has been difficult to understand the effectiveness of different approaches as compared to each other as there is a lack of general, public benchmarking datasets for this industry. We propose a repository in which researchers can release their detailed raw datasets for the purpose of repeatability, benchmarking, and utilization by other researchers. We start this effort through the public release of a single, large building performance seed dataset. The dataset is from a primary and secondary school campus that has 76,000 square meters floor area of conditioned, indoor space in seven buildings that include classroom, office, sports facilities, auditorium, cafeteria, dormitory, and mixed-use spaces. The dataset contains almost 3 years of detailed temporal data from 3,690 measured data points, most of which are sampled at a frequency of 1-3 minutes. The campus is located in a tropical climate with a continuously high cooling and dehumidification load. Some of the dataset has been annotated with building event schedules and known anomalous behavior which can be used as ground truth for detection algorithms. The dataset is available for download online and will serve as the first example in a planned repository of raw datasets from various buildings from different climates and contexts.
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In this paper, we present what was learned in the research & design process of a decentralized ventilation system with chilled ceilings for a commercial office in Singapore. We make two key observations from the knowledge gathered. First,... more
In this paper, we present what was learned in the research & design process of a decentralized ventilation system with chilled ceilings for a commercial office in Singapore. We make two key observations from the knowledge gathered. First, we observe, in quantitative terms, that present-day radiant cooling panel products may not provide sufficient sensible cooling capacity for commercial offices in hot and humid climates. However, upon considering the use of passive chilled beams as an alternative chilled ceiling product, we do observe that a decentralized ventilation system comprising both recirculating and dedicated outdoor air fan coil units may reduce daily electricity requirements for air-conditioning in Singaporean office spaces by over 15%.
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Building information modelling (BIM) has carved a growing niche in the construction industry for the support of new building projects. The same cannot be said for existing buildings, where the prevalence of uncertain data, and unclear... more
Building information modelling (BIM) has carved a growing niche in the construction industry for the support of new building projects. The same cannot be said for existing buildings, where the prevalence of uncertain data, and unclear information, has been difficult to reconcile with the unambiguous nature of BIM parameterization. An opportunity to alleviate these challenges may have arrived from the recent boon of virtual reality platforms for navigating physical environments. This paper demonstrates how labeling of equirectangular images with data or text widgets is possible using publicly-available software libraries. A prototype is presented and tested on a building in Singapore.
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This paper describes the implementation of long wave radiation (LWR) exchange as part of a co-simulation process of an urban scale simulation program, CitySim, and a building scale program, EnergyPlus. This coupling process was achieved... more
This paper describes the implementation of long wave radiation (LWR) exchange as part of a co-simulation process of an urban scale simulation program, CitySim, and a building scale program, EnergyPlus. This coupling process was achieved through the use of functional mockup units (FMU) to exchange various weather, load, and environmental information between the two simulation engines. LWR is an important factor to exchange between the programs as CitySim has more advanced capabilities for radiation exchange calculations from a set of urban buildings and EnergyPlus has a more advanced building heating and cooling load calculation engine. The LWR exchange between surfaces is computed in CitySim by a linearization of the longwave energy balance at each surface around an average between the surface and its environmental temperatures. The environmental temperature for each surface is determined using the simplied radiation algorithm neglecting inter-reections and is aggregated into a sing...
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This paper describes an open data set of 3,053 energy meters from 1,636 non-residential buildings with a range of two full years (2016 and 2017) at an hourly frequency (17,544 measurements per meter resulting in approximately 53.6 million... more
This paper describes an open data set of 3,053 energy meters from 1,636 non-residential buildings with a range of two full years (2016 and 2017) at an hourly frequency (17,544 measurements per meter resulting in approximately 53.6 million measurements). These meters were collected from 19 sites across North America and Europe, with one or more meters per building measuring whole building electrical, heating and cooling water, steam, and solar energy as well as water and irrigation meters. Part of these data was used in the Great Energy Predictor III (GEPIII) competition hosted by the American Society of Heating, Refrigeration, and Air-Conditioning Engineers (ASHRAE) in October-December 2019. GEPIII was a machine learning competition for long-term prediction with an application to measurement and verification. This paper describes the process of data collection, cleaning, and convergence of time-series meter data, the meta-data about the buildings, and complementary weather data. Thi...
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Global climate change is a clear and present danger to our environment, but the impacts of climate change on human health are less known. People in Asian countries are more susceptible to the negative impacts of climate change and the... more
Global climate change is a clear and present danger to our environment, but the impacts of climate change on human health are less known. People in Asian countries are more susceptible to the negative impacts of climate change and the subsequent environmental exposures because of the high population density, rapid urbanization, and natural geography of the region. The objective of this multidisciplinary collaborative ecological study was to explore the impact of environmental exposures such as temperature (°C), noise (db), humidity (%rh), air conditioning exposure time (hours), and distance traveled to school (km) on the comfort and academic success of school children in Singapore. Analysis of a large dataset from the Singapore National Science Experiment revealed a positive correlation between the distance traveled to school and favorable environmental conditions (moderate temperatures, low noise, low humidity, and higher amount of air conditioning time) and student academic perfor...
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We present an approach for rapidly assessing the per-formance of early design stage building information models (BIM) from both the building and urban sys-tems scale. This effort builds upon two previously de-veloped tools, the Design... more
We present an approach for rapidly assessing the per-formance of early design stage building information models (BIM) from both the building and urban sys-tems scale. This effort builds upon two previously de-veloped tools, the Design Performance Viewer (DPV) and the CitySim urban simulation engine. It couples them to produce a more informed model. The DPV is a plugin for Autodesk Revit Architecture that creates a model for the EnergyPlus building performance en-gine based on information contained in the BIM. We combine the two simulation engines using the Func-tional Mock-up Interface (FMI) co-simulation frame-work to improve the accuracy of both simulations. This work extends the DPV to extract a CitySim model in addition to the EnergyPlus model. The CitySim model contains not only the building being investi-gated, but also a simplified representation of the sur-rounding buildings. We extend the CitySim solver to use the FMI standard for co-simulation to exchange simulation variab...
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ABSTRACT The amount of sensor data generated by modern building systems is growing rapidly. Automatically discovering the structure of diurnal patterns in this data supports implementation of building commissioning, fault detection and... more
ABSTRACT The amount of sensor data generated by modern building systems is growing rapidly. Automatically discovering the structure of diurnal patterns in this data supports implementation of building commissioning, fault detection and retrofit analysis techniques. Additionally, these data are crucial to informing design professionals about the efficacy of their assumptions and strategies used in performance prediction simulation models. In this paper, we introduce DayFilter, a day-typing process that uses Symbolic Aggregate approXimation (SAX), motif and discord extraction, and clustering to detect the underlying structure of building performance data. Discords, or infrequent daily patterns, are filtered and tagged for deeper, detailed analysis of potential energy savings opportunities. Motifs, or the most frequent patterns, are detected and further aggregated using k-means clustering. This procedure is designed for application on whole building and sub-system metrics from hierarchical building and energy management systems (BMS/EMS). The process transforms quantitative raw data into qualitative subgroups based on daily performance similarity and visualizes them using expressive techniques. We apply DayFilter on 474 days of example data from an international school campus in a tropical climate and 407 days of data from an office building from a temperate European climate. Discords are filtered resulting in 17 and 22 patterns found. Selected discords are investigated and many correlate with specific failures and energy savings detected by the on-site operations staff. Six and ten motif candidates are detected in the two case studies. These motifs are then further aggregated to five and six performance clusters that reflect the typical operational behavior of those projects. We discuss the influence of the parameter choices and provide initial parameter settings for the DayFilter process.
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Research Interests: Engineering, Environmental Science, Sustainable Building Design, Building Energy Simulation, Distributed System, and 9 moreEnergy efficiency, Zero emission buildings, Exergy, Energy and exergy based analysis in thermal systems, Physical sciences, Tropical climates, Energy Efficiency, ENERGIES, and Radiant Cooling System
ABSTRACT Building retrofit analysis of buildings in Switzerland traditionally relies on expert heuristics and best practices. These processes are not often supplemented by data or model-driven techniques that would enhance the accuracy... more
ABSTRACT Building retrofit analysis of buildings in Switzerland traditionally relies on expert heuristics and best practices. These processes are not often supplemented by data or model-driven techniques that would enhance the accuracy and ability to quantify the impact of innovative technologies. We present a process of calibrated building energy model (BEM) analysis of a case study using a building information model (BIM) and measured data from a custom wireless sensor network. The case study is a mixed-use office and residential historically listed building in Zürich, Switzerland. A BIM model was first developed in Autodesk Revit and then extracted to an EnergyPlus model through the Design Performance Viewer (DPV) toolkit that uses the RevitPythonShell (RPS) plug-in to convert the BIM data model to a geometric representation for EnergyPlus. This model was further developed using the OpenStudio modeling suite and the collected sensor data was used for calibration. The geometry translation process from BIM to BEM included many difficult challenges with respect to zone creation and model simplification. The calibration process was implemented on various façade and heating system retrofit options and an option was chosen for the project that has a predicted energy savings of 32%. Other results of this calibration and lessons learned regarding model development and translation to EnergyPlus are discussed.
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The co-simulation of both urban and building-level models leverages the advantages of both platforms. It better accounts for the localized effects of surrounding buildings, geography and climate conditions while maintaining high-fidelity... more
The co-simulation of both urban and building-level models leverages the advantages of both platforms. It better accounts for the localized effects of surrounding buildings, geography and climate conditions while maintaining high-fidelity building systems representation. This paper describes the co-simulation process of the building and urban-scale models of two university campuses in Switzerland using EnergyPlus and CitySim. In the first case study, on-site measured performance data is compared to the co-simulation results. The second case study examines the results of the two engines. The results show that coupling of EnergyPlus with CitySim resulted in a-15.5% and-7.5% impact on cooling consumption and a +6.5% and +4.8% impact on heating use as compared to solo simulations.The co-simulation process was able to better model realistic conditions for heating, but not cooling in one case study. It was able to substantially reduce the discrepancies in prediction between the engines in the other study.
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Building information modelling (BIM) has carved a growing niche in the construction industry for the support of new building projects. The same cannot be said for existing buildings, where the prevalence of uncertain data, and unclear... more
Building information modelling (BIM) has carved a growing niche in the construction industry for the support of new building projects. The same cannot be said for existing buildings, where the prevalence of uncertain data, and unclear information, has been difficult to reconcile with the unambiguous nature of BIM parameterization. An opportunity to alleviate these challenges may have arrived from the recent boon of virtual reality platforms for navigating physical environments. This paper demonstrates how labeling of equirectangular images with data or text widgets is possible using publicly-available software libraries. A prototype is presented and tested on a building in Singapore.
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District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the greenhouse gas emissions from the building sector. These systems require high investments which are returned... more
District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, prolonging the investment return period. The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were compared with results from a dynamic heat demand model, previously developed and validated by the authors. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations. Abstract This paper presents a method to automatically cluster typical days of energy consumption in one or several buildings. The method is based on an optimized version of the Symbolic Aggregate approXimation (SAX) method. SAX is a data mining technique for clustering time series with recent applications in building fault detection and building performance assessment. The number of clusters and accuracy of SAX highly depends on two highly sensitive input variables, i.e., the word size and the alphabet size. We propose the use of the genetic algorithm NSGA-II to optimize the number of words and alphabet size of SAX subjected to three fitness objectives, i.e., maximize data accuracy and compression and minimize complexity. In addition, we propose the use of MAVT as selection method of the optimal solution. The methodology is applied to measured energy consumption data of three representative buildings on a university campus in Singapore. Potential future uses of the approach include advanced studies in fault detection and calibration of urban building performance models.
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As of 2015, there are over 60 million smart meters installed in the United States; these meters are at the forefront of big data ana-lytics in the building industry. However, only a few public data sources of hourly non-residential meter... more
As of 2015, there are over 60 million smart meters installed in the United States; these meters are at the forefront of big data ana-lytics in the building industry. However, only a few public data sources of hourly non-residential meter data exist for the purpose of testing algorithms. This paper describes the collection, cleaning, and compilation of several such data sets found publicly on-line, in addition to several collected by the authors. There are 507 whole building electrical meters in this collection, and a majority are from buildings on university campuses. This group serves as a primary repository of open, non-residential data sources that can be built upon by other researchers. An overview of the data sources, subset selection criteria, and details of access to the repository are included. Future uses include the application of new, proposed prediction and classification models to compare performance to previously generated techniques.
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This paper discusses the creation of targeting and segmentation information about non-residential buildings that are equipped with advanced metering infrastructure (AMI) meters, or smart meters. Statistics, model, and pattern-based... more
This paper discusses the creation of targeting and segmentation information about non-residential buildings that are equipped with advanced metering infrastructure (AMI) meters, or smart meters. Statistics, model, and pattern-based temporal features are extracted from over 36,000 smart meters. They are then merged with a database of past energy efficiency interventions such as lighting, HVAC, and controls retrofits from 1,600 buildings. The buildings are divided into Good, Average, and Poor performing classes according to consumption from before and after the retrofits. Classification models are developed that improve the ability to predict retrofit success and standard industry class by 18.3% and 27.6% respectively over baselines. This study serves as an example of better leveraging smart meter data from non-residential buildings for utility targeted incentive programs. The methodology outlined is preliminary and further models and temporal features are to be tested.
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This study focuses on the inference of characteristic data from a data set of 507 non-residential buildings. A two-step framework is presented that extracts statistical, model-based, and pattern-based behavior. The goal of the framework... more
This study focuses on the inference of characteristic data from a data set of 507 non-residential buildings. A two-step framework is presented that extracts statistical, model-based, and pattern-based behavior. The goal of the framework is to reduce the expert intervention needed to utilize measured raw data in order to infer information such as building use type, performance class, and operational behavior. The first step is temporal feature extraction, which utilizes a library of data mining techniques to filter various phenomenon from the raw data. This step transforms quantitative raw data into qualitative categories that are presented in heat map visualizations for interpretation. In the second step, a random forest classification model is tested for accuracy in predicting primary space use, magnitude of energy consumption, and type of operational strategy using the generated features. The results show that predictions with these methods are 45.6% more accurate for primary building use type, 24.3% more accurate for performance class, and 63.6% more accurate for building operations type as compared to baselines.
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Measured and simulated data sources from the built environment are increasing rapidly. It is becoming normal to analyze data from hundreds, or even thousands of buildings at once. Mechanistic, manual analysis of such data sets is... more
Measured and simulated data sources from the built environment are increasing rapidly. It is becoming normal to analyze data from hundreds, or even thousands of buildings at once. Mechanistic, manual analysis of such data sets is time-consuming and not realistic using conventional techniques. Thus, a significant body of literature has been generated using unsupervised statistical learning techniques designed to uncover structure and information quickly with fewer input parameters or meta data about the buildings collected. Further, visual analytics techniques are developed as aids in this process for a human analyst to utilize and interpret the results. This paper reviews publications that include the use of un-supervised machine learning techniques as applied to non-residential building performance control and analysis. The categories of techniques covered include clustering, novelty detection , motif and discord detection, rule extraction, and visual analytics. The publications apply these technologies in the domains of smart meters, portfolio analysis, operations and controls optimization, and anomaly detection. A discussion is included of key challenges resulting from this review, such as the need for better collaboration between several, dis-parate research communities and the lack of open, benchmarking data sets. Opportunities for improvement are presented including methods of reproducible research and suggestions for cross-disciplinary cooperation.
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The satisfaction of occupants with an indoor environment supplied by a conventional central VAV airconditioning system was compared to a novel system represented by decentralized ventilation units and a network of passive chilled beams.... more
The satisfaction of occupants with an indoor environment supplied by a conventional central VAV airconditioning system was compared to a novel system represented by decentralized ventilation units and a network of passive chilled beams. In parallel with occupant survey measurement of indoor air quality (IAQ) parameters was conducted with the developed wireless IAQ sensing kits. The same occupant cohort was surveyed for both systems, as occupants of the conventionally-conditioning office, known as the 'Previous Office' later moved into the office conditioned by the novel system, known as the '3for2 Office'. We observed that the perceived thermal comfort and air quality satisfaction of the occupants were markedly higher in the 3for2 Office than in the Previous Office. While occupants of the 3for2 Office could raise or lower indoor air temperatures, they appeared to be consistently satisfied with higher indoor air temperatures than that set by the building management of the Previous Office. The primarily Singaporean occupants of the 3for2 Office also felt thermally comfortable even when indoor air velocities in the office were measured to be below 0.1 m/s recommended by Singaporean Standards. The 3for2 Office's ventilation system was designed to supply outdoor air beyond minimal required ventilation rates, which lead to relatively low
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In this paper, we present what was learned in the research & design process of a decentralized ventilation system with chilled ceilings for a commercial office in Singapore. We make two key observations from the knowledge gathered. First,... more
In this paper, we present what was learned in the research & design process of a decentralized ventilation system with chilled ceilings for a commercial office in Singapore. We make two key observations from the knowledge gathered. First, we observe, in quantitative terms, that present-day radiant cooling panel products may not provide sufficient sensible cooling capacity for commercial offices in hot and humid climates. However, upon considering the use of passive chilled beams as an alternative chilled ceiling product , we do observe that a decentralized ventilation system comprising both recirculating and dedicated outdoor air fan coil units may reduce daily electricity requirements for airconditioning in Singaporean office spaces by over 15%.
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This paper describes the implementation of long wave radiation (LWR) exchange as part of a co-simulation process of an urban scale simulation program, CitySim, and a building scale program, EnergyPlus. This coupling process was achieved... more
This paper describes the implementation of long wave radiation (LWR) exchange as part of a co-simulation process of an urban scale simulation program, CitySim, and a building scale program, EnergyPlus. This coupling process was achieved through the use of functional mockup units (FMU) to exchange various weather, load, and environmental information between the two simulation engines. LWR is an important factor to exchange between the programs as CitySim has more advanced capabilities for radiation exchange calculations from a set of urban buildings and EnergyPlus has a more advanced building heating and cooling load calculation engine. The LWR exchange between surfaces is computed in CitySim by a linearization of the longwave energy balance at each surface around an average between the surface and its environmental temperatures. The environmental temperature for each surface is determined using the simplified radiation algorithm neglecting inter-reflections and is aggregated into a single, global environmental radiant temperature (T env). The LWR exchange process is implemented in EnergyPlus by CitySim sharing the variables T env and h env that are then used to calculate radiation gain or loss through the envelope as well as influence on the conductances of the surfaces. This approach overrides the conventional EnergyPlus ground, sky and air radiation calculations. Solo and coupled simulations are performed on a set of four scenarios and result in up to a 36% discrepancy in heating and 11% in cooling load calculations amongst solo and coupled simulations.
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Simulation model calibration has been long identified as a key means of reconciling the consumption and efficiency characteristics of buildings. A key step in this process is the creation of the actual diversity factor profiles for... more
Simulation model calibration has been long identified as a key means of reconciling the consumption and efficiency characteristics of buildings. A key step in this process is the creation of the actual diversity factor profiles for occupancy and various energy end uses such as lighting, plug-loads, and HVAC. Creation of these model inputs is conventionally a tedious process of site surveys, interviews or temporary sensor installation. Sometimes measured energy data can be used to create these schedules, however there are many challenges, especially when the sensor network available is large or unorganized. This paper describes a process applying a series of knowledge discovery filters to screen data quality, weather sensitivity, and temporal breakouts from large nonresiden-tial building performance datasets collected by building management and energy information systems (BMS/EIS). These screening techniques are used to qualify the desirability for calibrated model diversity schedule creation from a forensic perspective. A diurnal pattern filtering technique is then applied that automatically extracts frequent daily performance profiles, which can then be normalized and used as model inputs according to conventional industry techniques. The process is applied on a raw dataset of 389 power meter data streams collected for eight years from the EIS of a campus of 32 higher education buildings. The results are discussed in the context of time and effort savings for creating urban and building scale simulation model inputs.
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The amount of sensor data generated by modern building systems is growing rapidly. Automatically discovering the structure of diurnal patterns in this data supports implementation of building commissioning, fault detection and retrofit... more
The amount of sensor data generated by modern building systems is growing rapidly. Automatically discovering the structure of diurnal patterns in this data supports implementation of building commissioning, fault detection and retrofit analysis techniques. Additionally, these data are crucial to informing design professionals about the efficacy of their assumptions and strategies used in performance prediction simulation models. In this paper, we introduce DayFilter, a day-typing process that uses Symbolic Aggregate approXimation (SAX), motif and discord extraction, and clustering to detect the underlying structure of building performance data. Discords, or infrequent daily patterns, are filtered and tagged for deeper, detailed analysis of potential energy savings opportunities. Motifs, or the most frequent patterns, are detected and further aggregated using k-means clustering. This procedure is designed for application on whole building and sub-system metrics from hierarchical building and energy management systems (BMS/EMS). The process transforms quantitative raw data into qualitative subgroups based on daily performance similarity and visualizes them using expressive techniques. We apply DayFilter on 474 days of example data from an international school campus in a tropical climate and 407 days of data from an office building from a temperate European climate. Discords are filtered resulting in 17 and 22 patterns found. Selected discords are investigated and many correlate with specific failures and energy savings detected by the on-site operations staff. Six and ten motif candidates are detected in the two case studies. These motifs are then further aggregated to five and six performance clusters that reflect the typical operational behavior of those projects. We discuss the influence of the parameter choices and provide initial parameter settings for the DayFilter process.
Research Interests:
Building performance research using various informatics techniques has progressed exten- sively in the last twenty years by advancing the fields of automated fault detection and di- agnostics (AFDD), commissioning, data mining, and... more
Building performance research using various informatics techniques has progressed exten- sively in the last twenty years by advancing the fields of automated fault detection and di- agnostics (AFDD), commissioning, data mining, and visualization for commercial buildings. Despite this effort, it has been difficult to understand the effectiveness of different approaches as compared to each other as there is a lack of general, public benchmarking datasets for this industry. We propose a repository in which researchers can release their detailed raw datasets for the purpose of repeatability, benchmarking, and utilization by other researchers. We start this effort through the public release of a single, large building performance seed dataset. The dataset is from a primary and secondary school campus that has 76,000 square meters floor area of conditioned, indoor space in seven buildings that include classroom, office, sports facilities, auditorium, cafeteria, dormitory, and mixed-use spaces. The dataset contains almost 3 years of detailed temporal data from 3,690 measured data points, most of which are sampled at a frequency of 1-3 minutes. The campus is located in a tropical climate with a continuously high cooling and dehumidification load. Some of the dataset has been annotated with building event schedules and known anomalous behavior which can be used as ground truth for detection algorithms. The dataset is available for download online and will serve as the first example in a planned repository of raw datasets from various buildings from different climates and contexts.
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We present the design, construction and operation of a novel building systems laboratory, the BubbleZERO—Zero Emission Research Operation. Our objective was to design a space to evaluate the performance of Swiss-developed low exergy... more
We present the design, construction and operation of a novel building systems laboratory, the BubbleZERO—Zero Emission Research Operation. Our objective was to design a space to evaluate the performance of Swiss-developed low exergy building systems in the tropical climate of Singapore using an integrated design approach. The method we employed for evaluation in the tropics was to design and build a test bed out of the shipping containers that transported the prototype low exergy systems from Switzerland to Singapore. This approach resulted in a novel laboratory environment containing radiant cooling panels and decentralized air supply, along with a self-shading, inflated “bubble” skin, experimental low emissivity (LowE) glazing, LED lighting, wireless sensors and distributed control. The laboratory evaluates and demonstrates for the first time in Singapore an integrated high-temperature cooling system with separate demand-controlled ventilation adapted for the tropics. It is a functional lab testing system in real tropical conditions. As such, the results showing the ability to mitigate the risk of condensation by maintaining a dew point below 18 °C by the separate decentralized ventilation are significant and necessary for potential future implementation in buildings. In addition, the control system provides new proof of concept for distributed wireless sensors and control for reliable automation of the systems. These key results are presented along with the integrated design process and real-life tropical operation of the laboratory.
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The Lean Production framework was pioneered by the Toyota Motor Corporation as a method for improving process and product quality and it has proven effective within multiple domains such as manufacturing, management, healthcare, and... more
The Lean Production framework was pioneered by the Toyota Motor Corporation as a method for improving process and product quality and it has proven effective within multiple domains such as manufacturing, management, healthcare, and government. Numerous studies have shown that quality control and performance evaluation in the building industry could be significantly improved as many facilities fail to deliver the anticipated performance that designers and owners expect. This paper qualitatively investigates a Lean-focused framework in the context of building performance analysis and verification. Ideally this approach could empower building designers and operators with the ability to practice “kaizen”, or quality control exercises, in all phases of the building life cycle. The foundational “4P” model of Lean Production is analyzed and its application to buildings is proposed through the use of strategically-designed system level performance metrics, developing a culture of improvement and empowerment, and focusing on basic systems engineering. A discussion of the major challenges that the building industry faces is included with suggestions on short-term applications in motivated, owner- occupied subsectors of the market. The framework is then discussed in the context of the United World College Campus, Singapore case study in which many Lean Principles were observed in the long-term pursuit of a high performance campus.
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We present an approach for rapidly assessing the per-formance of early design stage building information models (BIM) from both the building and urban sys-tems scale. This effort builds upon two previously de-veloped tools, the Design... more
We present an approach for rapidly assessing the per-formance of early design stage building information models (BIM) from both the building and urban sys-tems scale. This effort builds upon two previously de-veloped tools, the Design Performance Viewer (DPV) and the CitySim urban simulation engine. It couples them to produce a more informed model. The DPV is a plugin for Autodesk Revit Architecture that creates a model for the EnergyPlus building performance en-gine based on information contained in the BIM. We combine the two simulation engines using the Func-tional Mock-up Interface (FMI) co-simulation frame-work to improve the accuracy of both simulations. This work extends the DPV to extract a CitySim model in addition to the EnergyPlus model. The CitySim model contains not only the building being investi-gated, but also a simplified representation of the sur-rounding buildings. We extend the CitySim solver to use the FMI standard for co-simulation to exchange simulation variables with the EnergyPlus model at each time step. This process includes an automated workflow that enables simulation driven design em-ploying these techniques. We compare the results of the coupled and uncoupled simulations and explain discrepancies. We then discuss further refinements to the models such as more accurate representation of the long wave radiation exchange.
Research Interests:
Retrofit measures are an effective means to improve both the heating energy and carbon footprint of a building. On one hand, reducing the losses through the envelope reduces the energy consumption. On the other hand, updating the heating... more
Retrofit measures are an effective means to improve both the heating energy and carbon footprint of a building. On one hand, reducing the losses through the envelope reduces the energy consumption. On the other hand, updating the heating from a fossil-fuel based system to an emission-free one bears the potential for CO2-emission free operation. The latter can be achieved if the supply temperature of the heating system can be sufficiently reduced, such that the operation of a heat pump with a high coefficient of performance becomes feasible. For this, typically the heating area is increased to facilitate the heat transfer. Qualitatively, it is understood that increasing the heating area and improving the insulation of the envelope allows one to lower the supply temperature. However, it is unclear how these improvements relate to each other, or what their individual or combined effect is. In this research, we present a steady-state model to illustrate the impact of retrofit measures on the supply temperature. The model requires the determination of two dimensionless parameters, as well as an estimate for the thermal transmittance (U-value) of the envelope. For this, we developed a flexible, low-cost sensor network. We apply our model to a real retrofit scenario of a historically listed building in Zurich, Switzerland, and show that the current state of the building is already suitable for a low temperature heating system. The findings of our model are confirmed by a calibrated dynamic building simulation. The proposed model provides a means to relate energy savings to reduction of green house gases, and, thus to reduce the CO2 footprint of the building stock.""
Research Interests:
Building retrofit analysis of buildings in Switzerland traditionally relies on expert heuristics and best practices. These processes are not often supplemented by data or model-driven techniques that would enhance the accuracy and ability... more
Building retrofit analysis of buildings in Switzerland traditionally relies on expert heuristics and best practices. These processes are not often supplemented by data or model-driven techniques that would enhance the accuracy and ability to quantify the impact of innovative technologies. We present a process of calibrated building energy model (BEM) analysis of a case study using a building information model (BIM) and measured data from a custom wireless sensor network. The case study is a mixed-use office and residential historically listed building in Zürich, Switzerland. A BIM model was first developed in Autodesk Revit and then extracted to an EnergyPlus model through the Design Performance Viewer (DPV) toolkit that uses the RevitPythonShell (RPS) plug-in to convert the BIM data model to a geometric representation for EnergyPlus. This model was further developed using the OpenStudio modeling suite and the collected sensor data was used for calibration. The geometry translation process from BIM to BEM included many difficult challenges with respect to zone creation and model simplification. The calibration process was implemented on various façade and heating system retrofit options and an option was chosen for the project that has a predicted energy savings of 32%. Other results of this calibration and lessons learned regarding model development and translation to EnergyPlus are discussed.
Research Interests:
We present the design, construction and operation of a novel building systems laboratory, the BubbleZERO—Zero Emission Research Operation. Our objective was to design a space to evaluate the performance of Swiss-developed low exergy... more
We present the design, construction and operation of a novel building systems laboratory, the BubbleZERO—Zero Emission Research Operation. Our objective was to design a space to evaluate the performance of Swiss-developed low exergy building systems in the tropical climate of Singapore using an integrated design approach. The method we employed for evaluation in the tropics was to design and build a test bed out of the shipping containers that transported the prototype low exergy systems from Switzerland to Singapore. This approach resulted in a novel laboratory environment containing radiant cooling panels and decentralized air supply, along with a self-shading, inflated “bubble” skin, experimental low emissivity (LowE) glazing, LED lighting, wireless sensors and distributed control. The laboratory evaluates and demonstrates for the first time in Singapore an integrated high-temperature cooling system with separate demand-controlled ventilation adapted for the tropics. It is a functional lab testing system in real tropical conditions. As such, the results showing the ability to mitigate the risk of condensation by maintaining a dew point below 18 °C by the separate decentralized ventilation are significant and necessary for potential future implementation in buildings. In addition, the control system provides new proof of concept for distributed wireless sensors and control for reliable automation of the systems. These key results are presented along with the integrated design process and real-life tropical operation of the laboratory.
Research Interests:
This paper outlines the development AirTerminal:DualDuct:VAV:OutdoorAir, which is designed to accommodate for dual duct air conditioning systems with decoupled outdoor air and recirculated air streams. The terminal unit is designed to set... more
This paper outlines the development AirTerminal:DualDuct:VAV:OutdoorAir, which is designed to accommodate for dual duct air conditioning systems with decoupled outdoor air and recirculated air streams. The terminal unit is designed to set the airflow of the outdoor air (OA) stream at the zone level based on the zonal ventilation requirements which are defined and calculated within the module. The recirculated return air (RA) stream is then modulated to meet the zone setpoint and reheat would be available as necessary in certain climates. Conceptual development of the module and potential system types and applications for this new terminal unit is discussed.
Research Interests:
This paper outlines the conceptual development of a building energy simulation model for the Single Coil, Twin Fan Air Conditioning (SCTF) system in the whole building simulation program EnergyPlus. The SCTF system is a unitary,... more
This paper outlines the conceptual development of a building energy simulation model for the Single Coil, Twin Fan Air Conditioning (SCTF) system in the whole building simulation program EnergyPlus. The SCTF system is a unitary, multi-zone system which conditions the outdoor ventilation and recirculated return air in two separate streams through a single compartmented cooling coil and supplies these unmixed streams via two variable speed fans and a dual duct arrangement to zonal mixing boxes. The main challenges addressed in EnergyPlus include the existing inability to concurrently simulate unmixed air streams within a centralized VAV system, supply and control outdoor air and recirculated air independently to a space via dual duct network, and prediction of performance of a compartmented cooling coil – all of which have potential applications in other innovative systems.