Policy Design for Controlling Set-Point Temperature of ACs in Shared Spaces of Buildings

Air conditioning systems are responsible for the major percentage of energy consumption in buildings. Shared spaces constitute considerable office space area, in which most office employees perform their meetings and daily tasks, and therefore the ACs in these areas have significant impact on the energy usage of the entire office building. The cost of this energy consumption, however, is not paid by the shared space users, and the AC's temperature set-point is not determined based on the users' preferences. This latter factor is compounded by the fact that different people may have different choices of temperature set-points and sensitivities to change of temperature. Therefore, it is a challenging task to design an office policy to decide on a particular set-point based on such a diverse preference set. As a result, users are not aware of the energy consumption in shared spaces, which may potentially increase the energy wastage and related cost of office buildings. In this context, this paper proposes an energy policy for an office shared space by exploiting an established temperature control mechanism. In particular, we choose meeting rooms in an office building as the test case and design a policy according to which each user of the room can give a preference on the temperature set-point and is paid for felt discomfort if the set-point is not fixed according to the given preference. On the other hand, users who enjoy the thermal comfort compensate the other users of the room. Thus, the policy enables the users to be cognizant and responsible for the payment on the energy consumption of the office space they are sharing, and at the same time ensures that the users are satisfied either via thermal comfort or through incentives. The policy is also shown to be beneficial for building management. Through experiment based case studies, we show the effectiveness of the proposed policy.Comment: Journal paper accepted in Energy & Buildings (Elsevier

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

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

Building information modeling for facility managers

A Decision Support System (DSS) can help facility managers to improve building performance, occupants’ comfort, and energy efficiency during the Operation and Maintenance (O&M) phase. These DSSs are normally data-intensive and have specific data requirements. Building Information Modeling (BIM) has the potential to advance and transform facilities O&M by providing facility managers with a digitalized virtual environment that allows them to retrieve, analyze, and process such data. However, the implementation of BIM in O&M phases is still limited. The majority of issues in the BIM-O&M context lie in the interoperability between different software that requires different data structures and formats. In a BIM environment, there are issues associated with extracting, storing, managing, integrating, and disseminating data so that interoperability is assured. Considering the aforementioned aspects, the aim of this thesis is to enable interoperability between BIM models and the DSSs for building performance aspects such as building condition, maintenance, and occupants’ comfort. This integration automatizes the data transfer process which can assist Facility Management (FM) team in properly establishing the necessary measurements to moderate the negative consequences on buildings and thereby improve their performance and occupants’ comfort. The approach can also provide FM teams with an effective platform for data visualization in a user-friendly manner that can assist in integrating digital insights into FM decision-making processes and converting them into positive strategic actions. The proposed approach is validated in existing software as a case study. It is possible to demonstrate the applicability of this approach by ensuring that its interactions and outcomes are feasible using case studies. Case studies also identify how much the task efficiencies are in comparison with the manual method, helping facility managers to optimize operation strategies of buildings in order to enhance their performance. Verification tests are also performed on the information exported from a software program. The results demonstrate an efficiency increase in high-quality FM data collection for different kinds of DSS, reducing the time and effort that the FM team spends on searching for information and entering data. A Dynamo script is designed to allow administrators to include as much information as they wish in BIM models. Moreover, a novel approach is proposed to create a new category in BIM to assist public and business administrations with managing assets efficiently. In addition, building performance aspects can also be analyzed using the proposed method of integrating occupants' feedback into BIM models. By implementing the proposed approach, FM teams are able to correctly establish measurements which can be applied to mitigate the negative effects on buildings, thus improving their performance and enhancing their occupants’ comfort. Besides, the proposed approach enables BIM to be a more useful tool for visualization by using the most appropriate charts and formatting.Un Sistema de Soporte de decisiones (SSD) puede ayudar a los gestores de edificios a mejorar su rendimiento, su eficiencia energética y el confort de sus ocupantes. Para el buen funcionamiento de los SSD se requieren muchos datos. El Building Information Modeling (BIM) permite mejorar la gestión de las operaciones y el mantenimiento de los edificios al proporcionar un entorno virtual digitalizado que permite recuperar, analizar y procesar los datos requeridos por los SSD. Sin embargo, la implementación de BIM en las fases de Operación y Mantenimimento (O&M) aún es escasa. La mayoría de los problemas en el contexto de BIM-O&M radican en la interoperabilidad entre diferentes programas que requieren diferentes estructuras y formatos de datos. En un entorno BIM, existen problemas asociados a la extracción, el almacenamiento, la gestión, la integración y la difusión de datos para garantizar la interoperabilidad. Teniendo en cuenta los aspectos antes mencionados, el objetivo de esta tesis es facilitar la interoperabilidad entre los modelos BIM y los SSD relacionados con el rendimiento de los edificios, su estado de conservación y el confort de los ocupantes. Esta integración automatiza el proceso de transferencia de datos que puede ayudar a los gestores de edificios a establecer correctamente las medidas necesarias para mejorar su rendimiento y el confort de sus ocupantes. Esta integración también va a proporcionar a los gestores de edificios una plataforma eficaz para la visualización de datos de una manera fácil de usar que puede ayudar a integrar resultados de los SSD y convertirlos en acciones estratégicas. Para demostrar la aplicabilidad y la eficiencia de este integración, ésta se valida a través de casos de estudio. También se realizan pruebas de verificación sobre la información exportada en los diferentes sistemas. Los resultados demuestran un aumento de la eficiencia en la recopilación de datos de alta calidad para diferentes tipos de DSS, lo que reduce el tiempo y el esfuerzo que los gestores de edificios dedican a buscar información e introducir datos en la diferentes aplicaciones. Un script de Dynamo está diseñado para permitir que los gestores incluyan tanta información como deseen en los modelos BIM. Además, se propone un enfoque novedoso para crear una nueva categoría en BIM para ayudar a las administraciones públicas y empresariales a gestionar los activos de manera eficiente. Además, los aspectos del rendimiento del edificio también se pueden analizar utilizando el método propuesto de integrar los comentarios de los ocupantes en los modelos BIM. Al implementar el enfoque propuesto, los gestores de edificios pueden establecer correctamente las medidas que se pueden aplicar para mitigar los efectos negativos en los edificios, mejorando así su rendimiento y el confort de sus ocupantes. Además, la integración propuesta permite que BIM sea una herramienta más útil para la visualización mediante el uso de los gráficos y las opciones de formato más apropiados, guiando a la toma de decisiones para gestionar los edificiosPostprint (published version

Acoustic Design Criteria in Naturally Ventilated Residential Buildings: New Research Perspectives by Applying the Indoor Soundscape Approach

The indoor-outdoor connection provided by ventilation openings has been so far a limiting factor in the use of natural ventilation (NV), due to the apparent conflict between ventilation needs and the intrusion of external noise. This limiting factor impedes naturally ventilated buildings meeting the acoustic criteria set by standards and rating protocols, which are reviewed in this paper for residential buildings. The criteria reflect a general effort to minimize noise annoyance by reducing indoor sound levels, typically without a distinction based on a ventilation strategy. Research has developed a number of solutions, discussed here, that try to guarantee ventilation without compromising façade noise insulation, but, currently, none have been adopted on a large scale. This concept paper highlights the main limits of the current approach. First, a fragmented view towards indoor environmental quality has not included consideration of the following acoustic criteria: (i) how buildings are designed and operated to meet multiple needs other than acoustical ones (e.g., ventilation, visual, and cooling needs) and (ii) how people respond to multiple simultaneous environmental factors. Secondly, the lack of a perceptual perspective has led acoustic criteria to neglect the multiple cognitive and behavioral factors impinging on comfort in naturally ventilated houses. Indeed, factors such as the connection with the outside and the sense of control over one’s environment may induce “adaptive acoustic comfort” opportunities that are worth investigating. The mere use of different sound level limits would not be enough to define criteria tailored to the complex user–building interaction that occurs under NV conditions. More holistic and human-centered approaches are required to guarantee not only neutrally but even positively perceived indoor acoustic environments. For this reason, this paper considers this apparent conflict from a soundscape viewpoint, in order to expose still unexplored lines of research. By underpinning a perceptual perspective and by contextualizing it, the indoor soundscape approach provides a framework capable of overcoming the limits of the traditional noise control approach. This could provide the opportunity to foster a wider adoption of NV as a passive design strategy that enhances user health and well-being, while enabling low-cost, and low-energy cooling and ventilation, thereby contributing to current climate change challenge

Machine Learning for Smart and Energy-Efficient Buildings

Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the U.S., and similar numbers are being reported from countries around the world. This significant amount of energy is used to maintain a comfortable, secure, and productive environment for the occupants. So, it is crucial that the energy consumption in buildings must be optimized, all the while maintaining satisfactory levels of occupant comfort, health, and safety. Recently, Machine Learning has been proven to be an invaluable tool in deriving important insights from data and optimizing various systems. In this work, we review the ways in which machine learning has been leveraged to make buildings smart and energy-efficient. For the convenience of readers, we provide a brief introduction of several machine learning paradigms and the components and functioning of each smart building system we cover. Finally, we discuss challenges faced while implementing machine learning algorithms in smart buildings and provide future avenues for research at the intersection of smart buildings and machine learning

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

Modeling and Optimizing Energy Supply and Demand in Home Area Power Network (HAPN)

Internet of energy based smart power grids demonstrate high in-feed from renewable energy resources (RESs) and lofty out-feed to energy consumers. Uncertainties evolved by incorporating RESs and time-varying energy consumption present immense challenges to the optimal control of smart power networks. To deal with these challenges, it is important to make the system deterministic by making time-ahead prediction and scheduling of power supply and demand. The present work confers a model of a co-scheduling framework, organizing cost-efficient activation of energy supply entities (ESEs) and load demands in a home area power network (HAPN). It integrates roof-top photovoltaic (PV) panels, diesel energy generator (DE), energy storage devices (ESDs), and smart load demands (SLDs) along with grid-supplied power. The scheduling model is based on mixed-integer linear programming (MILP) framework, incorporates a “min-max” optimization algorithm that reduces the daily energy bills, maintains high comfort level for the energy consumers, and increases the self-sufficiency of the home. The proposed strategy exploits the flexibility in dynamic energy price signals and SLDs of various classes, providing day-ahead cost-optimal scheduling decisions for incorporated energy entities. A linearized component-based model is developed, considering inefficiencies, taking various power phase modes of the SLDs along with the cost of operation, maintenance, and degradation of the equipment. A case study based on numerical analysis determines the particular features of the proposed HAPN model. Simulation results demonstrate the real prospect of our implemented strategy, utilizing a cost-effective optimal blend of distinct energy entities in a smart home