Visual Preferences and Human Interactions with Shading and Electric Lighting Systems

Buildings in the United States are responsible for 40% of the primary energy use and 30% of carbon dioxide emissions. As awareness is being raised for the energy consumption and environmental impacts of buildings, it is not surprising that improving building performance has gained significant attention over the past years. Increasing the energy efficiency and reducing the emissions associated with buildings is possible through the use of high-performance building design and implementation of advanced building controls. Moreover, as part of the modern life style, people in developed countries spend most of their time inside the buildings. This fact necessitates consideration of two important requirements. First that energy saving achieved by efficiency methods in practice should not compromise occupants’ comfort. Second, energy impacts of building users and their indoor environment preferences should be taken into account at both design and operation phases. Therefore, understanding and modeling human-building interactions and their links to energy consumption and occupant satisfaction with the indoor environment is the main goal of this research. To this end and with a focus on the visual environment, systematic data collection from a large number of participants is undertaken and novel probabilistic modeling approaches are explored to provide new insights towards human-centered sustainable buildings. The specific research objectives of this thesis are: 1. Study human interactions with motorized roller shades and dimmable electric lights as well as human perception and satisfaction with the luminous environment in private offices with variable daylight and electric light conditions. 2. Develop a novel Bayesian approach to model the interrelated human interactions with window shades and electric lights. 3. Develop a Bayesian classification and inference modeling framework for occupants’ visual preferences in daylit perimeter offices. To this end, four identical private offices in a high performance building located in West Lafayette, IN were equipped with sensing network and online survey questionnaires to study almost 300 occupants during the two sets of field studies conducted for this thesis. The first field study extends the knowledge of human-building interactions to advanced building systems such as motorized roller shades and dimmable electric lights and reveals behavioral patterns enabled through side-by-side comparisons of different environmental controls and user interfaces ranging from fully automated to fully manual and from low to high levels of accessibility (wall switch, remote controller and graphical web interface). Results of the field study reveal: (a) occupational dynamics and human variables as two key features, in addition to environmental variables, in describing human-shading and -electric lighting interactions; (b) higher daylight utilization in offices with easy-to-access controls; (c) strong preference for customized indoor climate, along with a relationship between occupant perception of control and acceptability of a wider range of visual conditions. With the insights gained from the first field study, the research extends to exploit the resulted dataset as a basis for the development of a hierarchical Bayesian approach which is used, for the first time, to model human interactions with motorized roller shades and dimmable electric lights. Bayesian multivariate binary-choice logit models have been constructed to predict shade raising/lowering and electric light increasing actions while Bayesian regression models with built-in physical constraints to estimate the magnitude of shading and electric lighting actions. The proposed models, in their structure, account for (a) intermediate operating states of the systems; (b) interrelated operation of shades and lights; (c) personal characteristics and human attributes. Moreover, the developed human-building interaction modeling framework benefits from the advantages of the Bayesian formalism as it (a) provides a systematic approach to identify significant features in describing the human-building interactions; (b) incorporates prior beliefs about the systems; (c) captures the epistemic uncertainty, which is important when dealing with small-sized datasets, a ubiquitous issue in human data collection in actual buildings. The second field study was designed and conducted to collect data for occupants’ satisfaction with the visual environment when exposed to different combinations of daylight and electric light conditions, along with data from room sensors, shading and light dimming states. The resulted dataset is then used as a basis to model occupants’ visual preferences such as prefer darker, prefer brighter, or satisfied with current conditions. Bayesian multinomial logistic regression is augmented with Dirichlet process prior to encode within the model structure that occupants’ visual preferences are influenced by a combination of environmental and control state variables as well as individual visual characteristics. The latter is treated as a hidden random variable which is used to cluster occupants with similar visual preference characteristics and to determine the optimal number of clusters among the observed population. Modeling results based on observations from 75 occupants in glare-free conditions suggest work plane illuminance, window unshaded area, and electric light ratio as significant features of the general visual preference model and reveal the existence of three distinct clusters with physical interpretation; preference for bright, moderate, and dark conditions. In the final step, a method for learning the visual preferences of new occupants is deployed which uses a mixture of the general probabilistic sub-models to infer new occupants’ cluster values and personalized preference profiles. The proposed approach proves to be efficient as it is shown to predict personalized profiles with 81% prediction accuracy with very few observations (less than 16) from each new occupant. In summary, the systematic data collection methods and prototype interfaces used in this dissertation establish a consistent and reliable approach for studying human interactions with building systems and satisfaction with the indoor environment. Unique datasets for human attributes towards the visual environment in perimeter building zones have been generated especially for the occupants’ direct preference votes with different visual conditions which is currently lacked in the literature. The probabilistic models for human interactions with shading and lighting systems and occupants’ visual preferences incorporate individual characteristics and account for uncertainties associated with limited data, thus, are to increase prediction accuracy when implemented in Building Performance Simulation tools. The research presented herein facilitates an effective pathway towards implementation of adaptive personalized environments and is a necessary precursor for future investigation and expansion to human-centered building controls

Integrated Daylighting and Artificial Lighting Control based on High Dynamic Range Vision Sensors

One fifth of the electricity consumption of Swiss buildings is due to electric lighting. Integrated control of sun shading and artificial lighting can mitigate this demand while maintaining user comfort. However, the drawback of existing building control approaches is that they do not consider one of the main aspects of human-centric lighting: visual comfort. The goal of this doctoral thesis is to develop an integrated energy efficient sun shading and electric lighting control system that incorporates widely accepted visual comfort criteria and privileges daylighting over electric lighting. The first part is dedicated to High Dynamic Range (HDR) vision sensor calibration, programing, validation and preliminary testing. A sensor originally developed by the Centre Suisse dâElectronique et de Microtechnique (CSEM) was photometrically, spectrally and geometrically calibrated and validated with respect to reliable illuminance and multi-point luminance meters. This HDR vision sensor was then equipped with an embedded image processing routine in order to assess âon the flyâ discomfort glare indices. It has been demonstrated that the developed device, is able to serve as an enhanced visual comfort feedback sensor in building automation systems. On the other hand, it can be employed to characterize highly glazed facades and workspaces regarding visual comfort and glare risks, as demonstrated in a project in Singapore. Two monitoring campaigns are reported in the second part of this thesis. Firstly, 30 human subjects occupied two identical office rooms of the LESO solar experimental building for 15 afternoons to compare the performance of a fuzzy logic control system incorporating two HDR vision sensors with respect to a âbest-practiceâ controller. Subjective self-reported visual comfort surveys, paper- and computer-based visual tests and monitoring of the electric lighting consumption were carried out simultaneously in both offices. It was shown that the electricity demand of the office with the advanced controller is 32% lower than that of the reference room, while the subjectsâ visual performance remained comparable. Secondly, an eight-month data monitoring campaign was carried out in the same building in order to study the ability of a novel control approach to maintain optimal visual and thermal comfort conditions while reducing the energy performance gap of a room as well as its electric lighting demand. The experimental results showed that the advanced controller mitigated the performance gap during the heating season by 72% with regard to standard occupant behavior and by 19% with respect to a best-practice automated system. This system reduced backup heating demand leading to lower CO2 gas emissions. At the same time, visual comfort constraints regarding Daylight Glare Probability (DGP) and workplane horizontal illuminance were respected during work hours. Finally, a self-commissioning integrated controller for Venetian blinds enhanced with a learning module was developed and validated for 22 days in a daylighting testbed at the Fraunhofer Institute for Solar Energy (ISE) in Freiburg, Germany. It has been shown that the visual comfort constraints are respected for 96% of the work hours and that the controller can successfully limit the number of shading movements. The market potential for HDR vision sensors and integrated control platforms has been studied and possible commercialization tracks have been identified

Machine learning for smart building applications: Review and taxonomy

© 2019 Association for Computing Machinery. The use of machine learning (ML) in smart building applications is reviewed in this article. We split existing solutions into two main classes: occupant-centric versus energy/devices-centric. The first class groups solutions that use ML for aspects related to the occupants, including (1) occupancy estimation and identification, (2) activity recognition, and (3) estimating preferences and behavior. The second class groups solutions that use ML to estimate aspects related either to energy or devices. They are divided into three categories: (1) energy profiling and demand estimation, (2) appliances profiling and fault detection, and (3) inference on sensors. Solutions in each category are presented, discussed, and compared; open perspectives and research trends are discussed as well. Compared to related state-of-the-art survey papers, the contribution herein is to provide a comprehensive and holistic review from the ML perspectives rather than architectural and technical aspects of existing building management systems. This is by considering all types of ML tools, buildings, and several categories of applications, and by structuring the taxonomy accordingly. The article ends with a summary discussion of the presented works, with focus on lessons learned, challenges, open and future directions of research in this field

Data-driven methods to improve resource utilization, fraud detection, and cyber-resilience in smart grids

This dissertation demonstrates that empirical models of generation and consumption, constructed using machine learning and statistical methods, improve resource utilization, fraud detection, and cyber-resilience in smart grids. The modern power grid, known as the smart grid, uses computer communication networks to improve efficiency by transporting control and monitoring messages between devices. At a high level, those messages aid in ensuring that power generation meets the constantly changing power demand in a manner that minimizes costs to the stakeholders. In buildings, or nanogrids, communications between loads and centralized controls allow for more efficient electricity use. Ultimately, all efficiency improvements are enabled by data, and it is vital to protect the integrity of the data because compromised data could undermine those improvements. Furthermore, such compromise could have both economic consequences, such as power theft, and safety-critical consequences, such as blackouts. This dissertation addresses three concerns related to the smart grid: resource utilization, fraud detection, and cyber-resilience. We describe energy resource utilization benefits that can be achieved by using machine learning for renewable energy integration and also for energy management of building loads. In the context of fraud detection, we present a framework for identifying attacks that aim to make fraudulent monetary gains by compromising consumption and generation readings taken by meters. We then present machine learning, signal processing, and information-theoretic approaches for mitigating those attacks. Finally, we explore attacks that seek to undermine the resilience of the grid to faults by compromising generators' ability to compensate for lost generation elsewhere in the grid. Redundant sources of measurements are used to detect such attacks by identifying mismatches between expected and measured behavior

Intelligent Decision Support System for Energy Management in Demand Response Programs and Residential and Industrial Sectors of the Smart Grid

This PhD thesis addresses the complexity of the energy efficiency control problem in residential and industrial customers of Smart electrical Grid, and examines the main factors that affect energy demand, and proposes an intelligent decision support system for applications of demand response. A multi criteria decision making algorithm is combined with a combinatorial optimization technique to assist energy managers to decide whether to participate in demand response programs or obtain energy from distributed energy resources

An Exploration of the Applications of Increased Information Availability in Smart Buildings

Modern buildings have the capability to capture vast quantities of information about the building itself, its purpose, the people that use it and its wider environment. With the development of fields such as the Internet of Things and Big Data, the future of buildings will involve more data around all aspects of their operation and the people using them. Within the context of these changes, this research hypothesises that the availability of increasing information within buildings can enable new ways of operation to step change their performance. Initially the thesis combines an extensive literature review of modern building developments and the current landscape that buildings operate within to develop clarity around the term “Smart Building”. Two case studies are then presented to demonstrate the potential of Smart Building concepts: The first case study involves a pilot study within an existing university library building using occupancy, energy, occupant satisfaction and building functionality data to investigate the potential of the buildings ability to vary physical space with occupancy. The second study uses computational fluid dynamics to model the thermal comfort variations throughout a large underfloor heated naturally ventilated atrium. The results are then used to investigate potential energy savings and comfort improvements through correlating individual comfort preferences with environmental variations. The work forms a clear definition of a Smart Building to create a framework for researchers and designers to focus future Smart Building developments. The first case study then demonstrates that by varying physically occupied space with occupancy, energy consumption of the building can be reduced by approximately 33%. The second study demonstrates that a step change in both comfort and energy efficiency can be achieved in flexible working spaces by aligning individual preferences with environmental conditions. These findings are discussed in detail, addressing limitations and future expansions of the novel approaches developed