Refining building energy modeling through aggregate analysis and probabilistic methods associated with occupant presence

textThe building sector represents the largest energy consumer among the United States' end use sectors. As a result, the public and private sector will continue to place great emphasis on designing energy efficient buildings that minimize operating costs while maintaining a healthy environment for its occupants. Creating design-phase building energy models can facilitate the process of selecting life-cycle appropriate design strategies aimed at maximizing building energy efficiency. The primary objective of this research study is to gain greater insight into likely causes of variation between energy predictions derived from building energy models and building energy performance during post-occupancy. Identifying sources of error can be used to improve future modeling efforts that can potentially lead to greater accuracy and better decisions made during the building's design phase. My research approach is to develop a method for conducting retrospective analysis of building energy models in the areas that affect the building's predicted and actual energy consumption. This entails collecting pre-construction and post-occupancy related data from various entities that exhibit influence on the building's energy performance. The method is then applied to recently-constructed military dormitory buildings that utilized building energy modeling and now have actual, metered building energy consumption data. The study also examines how building occupancy impacts energy performance. The value of this work will provide additional insight to future building energy modeling efforts.Civil, Architectural, and Environmental Engineerin

Human Mobility Monitoring using WiFi: Analysis, Modeling, and Applications

Understanding and modeling humans and device mobility has fundamental importance in mobile computing, with implications ranging from network design and location-aware technologies to urban infrastructure planning. Today\u27s users carry a plethora of devices such as smartphones, laptops, tablets, and smartwatches, with each device offering a different set of services resulting in different usage and mobility leading to the research question of understanding and modeling multiple user device trajectories. Additionally, prior research on mobility focuses on outdoor mobility when it is known that users spend 80% of their time indoors resulting in wide gaps in knowledge in the area of indoor mobility of users and devices. Here, I try to fill the gaps in mobility modeling in the areas of understanding and modeling indoor-outdoor human mobility as well as multi-device mobility. In this thesis, I propose the characterization and modeling of human and device mobility. Further, I design and deploy mobility-aware applications for contact tracing of infectious diseases and energy-aware Heating, Ventilation, and Air Conditioning (HVAC) scheduling. I try and answer a sequence of four primary inter-related questions : (1) how is indoor and outdoor user mobility different, (2) are multiple device trajectories belonging to a single user correlated, (3) how to model indoor mobility of users and (4) how to design effective mobility aware applications that are easily deployable and align with long term goals of sustainability as well relay positive societal impact. The insights gained from each question serves as a base to build up on the next question in the series. I present answers to these questions across three main parts of my thesis. The first part comprises of characterization and analysis of human and device mobility. In this part I design and develop tool to extract device trajectories from WiFi system logs syslog and map devices to users. These extracted trajectories and device to user mapping are used to characterize and empirically analyze the mobility of users at varying spatial granularity (indoor, outdoor) and extract device mobility correlations between multiple devices of users and forms the first part of my thesis. In the second part, based on the insights gained from the multi-granular and multi-device mobility characterization stated above, I argue that mobility is inherently hierarchical in nature and propose novel indoor human mobility modeling approach. Third, I leverage the passively observed mobility to design mobility-aware applications that either look back or look ahead in time. WiFiTrace is a look back or backtracking application that is a network-centric contact tracing tool to aid healthcare workers in manual contact tracing of infectious diseases and iSchedule is a look ahead machine learning based mobility-aware energy-saving application that predicts Heating, Ventilation, and Air Conditioning (HVAC) schedule for higher energy savings while increasing user comfort

A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment

The occupants' presence, activities, and behaviour can significantly impact the building's performance and energy efficiency. Currently, heating, ventilation, and air-conditioning (HVAC) systems are often run based on assumed occupancy levels and fixed schedules, or manually set by occupants based on their comfort needs. However, the unpredictability and variability of occupancy patterns can lead to over/under the conditioning of space when using such approaches, affecting indoor air quality and comfort. As a result, machine learning-based models and methodologies are progressively being used to forecast occupancy behaviour and routines in buildings, which may subsequently be used to aid in the design and operation of building systems. The present work reviews recent studies employing machine learning methods to predict occupancy behaviour and patterns, with a special focus on its related applications and benefits to building systems, improving energy efficiency, indoor air quality and thermal comfort. The review provides insight into the workflow of a machine learning-based occupancy prediction model, including data collection, prediction, and validation. An organised evaluation of the applicability or suitability of the different data collection methods, machine learning algorithms, and validation methods was carried out

Control of Residential Space Heating for Demand Response Using Grey-box Models

Certain advanced control schemes are capable of making a part of the thermostatic loads of space heating in buildings flexible, thereby enabling buildings to engage in so-called demand response. It has been suggested that this flexible consumption may be a valuable asset in future energy systems where conventional fossil fuel-based energy production have been partially replaced by intermittent energy production from renewable energy sources. Model predictive control (MPC) is a control scheme that relies on a model of the building to predict the future impact on the temperature conditions in the building of both control decisions (space heating) and phenomena outside the influence of the control scheme (e.g. weather conditions). MPC has become one of the most frequently used control schemes in studies investigating the potential for engaging buildings in demand response. While research has indicated MPC to have many useful applications in buildings, several challenges still inhibit its adoption in practice. A significant challenge related to MPC implementation lies in obtaining the required model of the building, which is often derived from measurements of the temperature and heating consumption. Furthermore, studies have indicated that, although demand response in buildings could contribute to the task of balancing supply and demand, suitable tariff structures that incentivize consumers to engage in DR are lacking. The main goal of this work is to contribute with research that addresses these issues. This thesis is divided into two parts.The first part of the thesis explores ways of simplifying the task of obtaining the building model that is required for implementation of MPC. Studies that explore practical ways of obtaining the measurement data needed for model identification are presented together with a study evaluating the suitedness of different low-order model structures that are suited for control-purposes.The second part of the thesis presents research on the potential of utilizing buildings for demand response. First, two studies explore and evaluate suitable incentive mechanisms for demand response by implementing an MPC scheme in a multi-apartment building block. These studies evaluate two proposed incentive mechanisms as well as the impact of building characteristics and MPC scheme implementation. Finally, a methodology for bottom-up modelling of entire urban areas is presented, and proved capable of predicting the aggregated energy demand of urban areas. The models resulting from the methodology are then applied in an analysis on demand response

Environmental Land Use Planning

Environmental Land Use Planning brings together leading scholars in the field of environmental problem solving to examine environmental problems and effects on land uses; analytical methods and tools in the field; and the role of governments, community grants and tradable permits in environmental planning. The chapters are based on empirical research from countries around the globe including Canada, USA, China, Nigeria, Germany, Serbia, Venezuela, and Brazil. The book discusses such issues as predicting changes in land use pattern, ecological footprint analysis, socioeconomic and behavioral modeling, and flood control approaches. It is insightful and serves as an important resource and reference material on environmental management

Modeling social norms in real-world agent-based simulations

Studying and simulating social systems including human groups and societies can be a complex problem. In order to build a model that simulates humans\u27 actions, it is necessary to consider the major factors that affect human behavior. Norms are one of these factors: social norms are the customary rules that govern behavior in groups and societies. Norms are everywhere around us, from the way people handshake or bow to the clothes they wear. They play a large role in determining our behaviors. Studies on norms are much older than the age of computer science, since normative studies have been a classic topic in sociology, psychology, philosophy and law. Various theories have been put forth about the functioning of social norms. Although an extensive amount of research on norms has been performed during the recent years, there remains a significant gap between current models and models that can explain real-world normative behaviors. Most of the existing work on norms focuses on abstract applications, and very few realistic normative simulations of human societies can be found. The contributions of this dissertation include the following: 1) a new hybrid technique based on agent-based modeling and Markov Chain Monte Carlo is introduced. This method is used to prepare a smoking case study for applying normative models. 2) This hybrid technique is described using category theory, which is a mathematical theory focusing on relations rather than objects. 3) The relationship between norm emergence in social networks and the theory of tipping points is studied. 4) A new lightweight normative architecture for studying smoking cessation trends is introduced. This architecture is then extended to a more general normative framework that can be used to model real-world normative behaviors. The final normative architecture considers cognitive and social aspects of norm formation in human societies. Normative architectures based on only one of these two aspects exist in the literature, but a normative architecture that effectively includes both of these two is missing

Urban building energy modelling for retrofit analysis under uncertainty

Urban building energy modelling (UBEM) is a growing research field that seeks to expand conventional building energy modelling to the realm of neighbourhoods, cities or even entire building stocks. The aim is to establish frameworks for analysing combined urban e˙ects rather than those of individual buildings, which city governments, utilities and other energy policy stakeholders can use to assess the current environmental impact of our buildings, and, maybe more importantly, the future e˙ects that energy renovation programmes and energy supply infrastructure changes might have. However, the task of creating reliable models of new or existing urban areas is diÿcult, as it requires an enormous amount of detailed input data – data which is rarely available. A solution to this problem is the introduction of archetype modelling, which is used to break down the building stock into a manageable subset of semantic building archetypes, for which, it is possible to characterize their parameters. It is the focus of this thesis to explore and develop new methods for stochastic archetype characterization that can enable archetype-based UBEM to be used for accurate urban-scale time series analysis.The thesis is divided into three parts. The first part acts as an introduction to case study data of the residential building stock of detached single-family houses (SFHs) in Aarhus, Denmark, which is used throughout the thesis for demonstration purposes.The second part concerns the development of methods for archetype modelling. Bayesian methods for archetype parameter calibration are presented that incorporates the variability of the underlying cluster of buildings, and correlation between parameters, to enable informed predictions of unseen buildings from the archetype under uncertainty. The capabilities of archetype-based UBEM are further widened through the introduction of dynamic building energy modelling that allows for time series analysis.The third part of the thesis is devoted to demonstrating the usefulness of the proposed archetype formulation as a building block for urban-scale applications. An exhaustive test scheme is employed to validate the predictive performance of the framework before establishing a city-scale UBEM of approx. 23,000 SFHs in Aarhus. It is used to forecast citywide heating energy use from 2017 up until 2050 under uncertainty of energy renovations and climate change.Overall, the proposed archetype-based UBEM framework promises very useful for fast, flexible and reliable urban-scale time series analysis, including forecasting the effects of energy renovation or city densification, to establish an informed basis for energy policy decision-making

Influence of occupant's behaviour on indoor environmental quality and energy consumptions

Buildings are dynamic, and the interactions of operators, occupants, and designers all influence the way in which buildings will perform. At the core of this research is the belief that technical solutions alone are not sufficient to face great challenges of saving energy while still maintaining or even improving current comfort levels. Buildings are engineered using tested components and generally reliable systems whereas people can be unreliable, variable, and perhaps even irrational. The studies in literature also reveal the gap between how designers expect occupants to use a building, and how they will actually operate it. Actually, there is often a significant discrepancy between the designed and the real total energy use in buildings. The reasons of this gap are generally poorly understood and largely have more to do with the role of human behaviour than the building design. Knowledge of user’s interactions within building is crucial to better understanding and a more valid predictions of building performance (energy use, indoor climate) and effective operation of building systems. The present work undertakes a theoretical and empirical study of the uncertainty of energy consumption assessment related to occupants’ behaviour in residential buildings. The main purpose of this research is to propose a methodology to model the user behaviour in the context of real energy use and applied it to a case study. The methodology, based on a medium/long-term monitoring, is aimed at shifting towards a probabilistic modelling the occupant behaviour related to the control of indoor environment with respect to the energy-related issues. The goal is to determine users’ behavioural patterns describing user’s interaction with the building controls. The procedure is applied first at modelling occupants’ interactions with windows (opening and closing behaviour) and then at modelling the heating set-point preferences. This research is based on the assumption that only switching from a deterministic approach in building energy simulation to a probabilistic one it will be feasible to obtain energy consumption prediction closer to reality. This probability is related to variability and unpredictability during the whole building operation. In this way, it become crucial to take into account the occupants’ presence and interactions with the building and systems. Actually, building energy simulation tools often reproduce building dynamics using numerical approximations of equations modelling only deterministic (fully predictable and repeatable) behaviours. In such a way, “occupant behaviour simulation” could refer to a computer simulation generating “fixed occupant schedules”, representing a fictional behaviour of a building occupant over the course of a single day. This is an important limitation of energy simulation tools for modelling occupant’s interactions with buildings, and highlights that the results are essentially unrealistic. The whole dissertation consists of four parts. In the first part the development of a theoretical model of the occupant behaviour is described based on a comprehensive literature review. With respect to the complexity of this issue, a specific literature survey is addressed to derive the most dominating driving forces useful for a more accurate description of occupant behaviour related to the habits of opening and closing the windows. Existing studies on the topic of window opening behaviour are highlighted and a theoretical framework to deal with occupants’ interactions with building controls, aimed at improving or maintaining the preferred indoor environmental conditions, is elaborated. The analysis of the literature highlights how a shared approach on identifying the driving forces for occupants’ window opening and closing behaviour has not yet been reached. In the second part of this dissertation, a method for defining occupant behaviour in simulation programs based on measurements is proposed. The proposed approach is based on measurements of both indoor and outdoor environmental parameters and the behavioural actions of the building occupants (window opening, TRV’s set point adjustments, occupancy sensors, etc..). From the collected data, different suitable user behavioural patterns (models) were defined by means of statistical analysis (logistic regression, Markov chain, etc..) and implemented in a building energy simulation tool. Moreover, a probabilistic distribution instead that a single value is preferred as a representation of energy consumptions. The proposed procedure was applied for modelling the human behaviour related to the window opening and closing and the change in thermostatic radiator valves (TRVs), and its implementation in the simulation tool IDA ICE so that the results obtained are probabilistic in nature. The third part of the dissertation deals with the validation of the obtained models to ensure the effectiveness of the models. In this section, the validation procedure is carried out using other data coming from an analogue dataset of dwellings where the same indoor and outdoor parameters are measured. These data will be used to validate the models of window opening behaviour. The validation is performed by comparing the probabilities of window opening and closing with the actual measured state of the windows in the dwellings. In literature, a variety of logistic models expressing the probability with which actions will be performed on windows, as a function of indoor temperature, outdoor temperature or both. Previously published models are then also compared using this validation procedure. The fourth part of the thesis represents a sightseeing of the future application of this field of research, focusing on the understanding of how technology and building design can improve energy efficiency exploiting the goal of making users more aware and hence careful on energy consumption. Overall, this dissertation highlights the importance of researching the individual’s behaviour in order to understand the differences in real building energy usage. Besides being limited to the cases of window opening and closing for most of the analyses, the methodology presented can also be applied to other types of behaviours