Download or read book Scalable Data driven Modeling and Analytics for Smart Buildings written by Srinivasan Iyengar and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Buildings account for over 40% of the energy and 75% of the electricity usage. Thus, by reducing our energy footprint in buildings, we can improve our overall energysustainability. Further, the proliferation of networked sensors and IoT devices in recent years have enabled monitoring of buildings to provide data at various granularity. For example, smart plugs monitor appliance level usage inside the house, while solar meters monitor residential rooftop solar installations. Furthermore, smart meters record energy usage at a grid-scale. In this thesis, I argue that data-driven modeling applied to the IoT data from a smart building, at varying granularity, in association with third party data can help to understand and reduce human energy consumption. I present four data-driven modeling approaches - that use sophisticated techniques from Machine Learning, Optimization, and Time Series Analysis - applied at different granularities. First, I study IoT devices inside the house and discuss an approach called NIMD that automatically models individual electrical loads found in a household. The analytical model resulting from this approach can be used in several applications. For example, these models can improve the performance of NILM algorithms to disaggregate loads in a given household. Further, faulty or energy-inefficient appliances can be identified by observing deviations in model parameters over its lifetime. Second, I examine data from solar meters and present a machine learning framework called SolarCast to forecast energy generation from residential rooftop installations. The predictions enable exploiting the benefits of locally-generated solar energy. Third, I employ a sensorless approach utilizing a graphical model representation to report city-scale photovoltaic panel health and identify anomalies in solar energy production. Immediate identification of faults maximizes the solar investment by aiding in optimal operational performance. Finally, I focus on grid-level smart meter data and use correlations between energy usage and external weather to derive probabilistic estimates of energy, which is leveraged to identify the least efficient buildings from a large population along with the underlying cause of energy inefficiency. The identified homes can be targeted for custom energy efficiency programs.

Download or read book Enabling Scalable Smart Building Analytics written by Arka Bhattacharya and published by . This book was released on 2016 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern buildings are being integrated with myriad (often >1000s) networked sensors to improve convenience, occupant comfort accessibility and energy-efficient operations. These technological improvements hold the promise of significant advances in centralized operation and management, fault diagnosis, and integration to an emerging smart grid. As of 2012, 14% of the buildings in the U.S. deployed Building Management Systems (BMS) to provide some kind of programmatic interface to the the sensors, actuators, and historical data management. Innovations in "Internet of Things" (IoT) devices have further led to connected lights, power meters, occupancy sensors and appliances that are capable of interfacing with the underlying BMS systems used in building automation. New buildings are installed with a BMS by design, and older buildings are being continuously retrofitted with networked systems for improved efficiency. However, whether provided by novel sensor networks or legacy instrumentation, extracting meaningful information from sensor data and taking actions based on that data depends fundamentally on the metadata available to interpret it. There are more than 5 million commercial buildings in the US, with the sensors in each building set up with customized and obscure metadata. One cannot achieve scalable deployments of software analytics and applications across buildings if deploying them requires vendors and domain experts spending spending 100s of hours fixing each building. Today even well-established applications do not get deployed at scale because of this very reason. Thus, the major challenge is scalability , i.e a paradigm where an application can be written once and deployed on 100s or 1000s of buildings. This thesis evaluates the challenges with existing metadata of sensors in smart-buildings and proposes ways to normalize it to uniform standard that would allow scalable (write-once and deploy everywhere) application development. We develop three empirical criteria for successful metadata schemas -- (a) completeness, or the ability to capture all sensors, (b) ability to capture all relationships between sensors required by state-of-the-art applications, and (c) flexibility in incorporating novel sensors and applications, and usability. We empirically demonstrate that no existing smart-building sensor metadata schema satisfy these properties and develop a schema based on an underlying graphical data model, Brick, that does. We validate Brick across 6 large and diverse commercial buildings (comprising more than 17,000 sensors) in two different continents and set up by different BMS vendors. We also develop a human-in-the-loop synthesis technique which uses syntactic and data-driven steps to parse legacy metadata into a common schema. This technique allows building-experts, who might not be conversant with sophisticated regular expression programs, to parse more than 70% of the legacy metadata in a building to a common schema by providing example parses of only about 1% of the sensors. We also show how to use active perturbations of subsystems in a building to construct functional relationships between subsystems that may have been missing or incorrectly captured in legacy metadata with an accuracy of ~80%. Finally, we demonstrate the power of our normalized smart-building metadata schema paradigm by using the standardized sensor and relationship representations to implement both simple (e.g. finding errant zones, identifying inefficient air handling subsystems to save energy) and sophisticated applications (e.g finding rough occupancy estimates) that scale across real-world smart-buildings.

Download or read book Data Driven Mining Learning and Analytics for Secured Smart Cities written by Chinmay Chakraborty and published by Springer Nature. This book was released on 2021-04-28 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides information on data-driven infrastructure design, analytical approaches, and technological solutions with case studies for smart cities. This book aims to attract works on multidisciplinary research spanning across the computer science and engineering, environmental studies, services, urban planning and development, social sciences and industrial engineering on technologies, case studies, novel approaches, and visionary ideas related to data-driven innovative solutions and big data-powered applications to cope with the real world challenges for building smart cities.

Download or read book Data driven Multivalence in the Built Environment written by Nimish Biloria and published by Springer. This book was released on 2019-07-01 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book sets the stage for understanding how the exponential escalation of digital ubiquity in the contemporary environment is being absorbed, modulated, processed and actively used for enhancing the performance of our built environment. S.M.A.R.T., in this context, is thus used as an acronym for Systems & Materials in Architectural Research and Technology, with a specific focus on interrogating the intricate relationship between information systems and associative material, cultural and socioeconomic formations within the built environment. This interrogation is deeply rooted in exploring inter-disciplinary research and design strategies involving nonlinear processes for developing meta-design systems, evidence based design solutions and methodological frameworks, some of which, are presented in this issue. Urban health and wellbeing, urban mobility and infrastructure, smart manufacturing, Interaction Design, Urban Design & Planning as well as Data Science, as prominent symbiotic domains constituting the Built Environment are represented in this first book in the S.M.A.R.T. series. The spectrum of chapters included in this volume helps in understanding the multivalence of data from a socio-technical perspective and provides insight into the methodological nuances involved in capturing, analysing and improving urban life via data driven technologies.

Download or read book Artificial Intelligence in Performance Driven Design written by Narjes Abbasabadi and published by John Wiley & Sons. This book was released on 2024-05-29 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: ARTIFICIAL INTELLIGENCE IN PERFORMANCE-DRIVEN DESIGN A definitive, interdisciplinary reference to using artificial intelligence technology and data-driven methodologies for sustainable design Artificial Intelligence in Performance-Driven Design: Theories, Methods, and Tools explores the application of artificial intelligence (AI), specifically machine learning (ML), for performance modeling within the built environment. This work develops the theoretical foundations and methodological frameworks for utilizing AI/ML, with an emphasis on multi-scale modeling encompassing energy flows, environmental quality, and human systems. The book examines relevant practices, case studies, and computational tools that harness AI’s capabilities in modeling frameworks, enhancing the efficiency, accuracy, and integration of physics-based simulation, optimization, and automation processes. Furthermore, it highlights the integration of intelligent systems and digital twins throughout the lifecycle of the built environment, to enhance our understanding and management of these complex environments. This book also: Incorporates emerging technologies into practical ideas to improve performance analysis and sustainable design Presents data-driven methodologies and technologies that integrate into modeling and design platforms Shares valuable insights and tools for developing decarbonization pathways in urban buildings Includes contributions from expert researchers and educators across a range of related fields Artificial Intelligence in Performance-Driven Design is ideal for architects, engineers, planners, and researchers involved in sustainable design and the built environment. It’s also of interest to students of architecture, building science and technology, urban design and planning, environmental engineering, and computer science and engineering.

Download or read book Technological Paradigms and Digital Eras written by Giacomo Chiesa and published by Springer. This book was released on 2019-07-24 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book connects the ICT and the architectural worlds, analyzing modeling, materialization and data-driven visions for design issues at different scales. Furthermore, using sample modeling and materialization tools, it explores the links between performance-driven design approaches and the application of new digital technologies. Intended for architects and urbanists, it provides a theoretical framework to address the implications of the digital revolution in building design and operation. Furthermore, combining insights from IT and ICT with architectural and urban design know-how, it offers engineering professionals a technology-driven interpretation of the building design field.

Download or read book Real time Data Driven Model Predictive Control for Efficient Energy Consumption in Smart Buildings written by Fatma Mtibaa and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book 5D Building Information Modeling written by Pardis Pishdad-Bozorgi and published by . This book was released on 2021-08-03 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: 5D+ Building Information Modeling: A Data-Driven Approach to Construction Supply Chain Integration, the third book in the Practical Revolutions series, is a valuable guide for AEC professionals who want to learn more about 5D+ BIM and how implementing this technology can optimize work efficiency. Starting with a brief introduction to BIM and the history of its emerging applications, this book highlights the unleveraged power of 5D+ in addressing the inefficiencies associated with current fragmented construction supply chains. This 5D+ guide focuses on the benefits of applying the power of data-driven BIM in achieving supply chain integration today and in the foreseeable future. Architects, engineers, contractors, and owners will find an implementation roadmap that includes state-of-the-art technologies, standards, workflows, and contractual framework established to achieve an integrated construction supply chain. About the series: Practical Revolutions: Disruptive Technologies and their Applications to Building Design and Construction drives the conversation of the practical deployment of emerging technologies in the building industries. It is a central information source for building professionals seeking to advance their individual capabilities and their firm's practices. Each volume in the series will cover an emerging technology paradigm. Volumes in the series will cover: Digital Sketching; Design Automation; 5D Building Information Modeling; Construction Automation and Robotics; Building Data Modeling; and Smart Buildings and Environments.

Download or read book Scalable Tuning of Building Models to Hourly Data written by and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Energy models of existing buildings are unreliable unless calibrated so they correlate well with actual energy usage. Manual tuning requires a skilled professional, is prohibitively expensive for small projects, imperfect, non-repeatable, non-transferable, and not scalable to the dozens of sensor channels that smart meters, smart appliances, and cheap/ubiquitous sensors are beginning to make available today. A scalable, automated methodology is needed to quickly and intelligently calibrate building energy models to all available data, increase the usefulness of those models, and facilitate speed-and-scale penetration of simulation-based capabilities into the marketplace for actualized energy savings. The ``Autotune'' project is a novel, model-agnostic methodology which leverages supercomputing, large simulation ensembles, and big data mining with multiple machine learning algorithms to allow automatic calibration of simulations that match measured experimental data in a way that is deployable on commodity hardware. This paper shares several methodologies employed to reduce the combinatorial complexity to a computationally tractable search problem for hundreds of input parameters. Accuracy metrics are provided which quantify model error to measured data for either monthly or hourly electrical usage from a highly-instrumented, emulated-occupancy research home.

Download or read book Smart Buildings Systems written by Eoin O'Driscoll and published by Butterworth-Heinemann. This book was released on 2021-06-15 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: The most fundamental feature of a smart building is that the core systems within it are linked. Water meters, pumps, fire alarms, power, and lighting are all connected, making it easier to adjust settings and monitor a building's energy output, both remotely and in situ. Smart & Sustainable Buildings Systems: Data Collection, Management and Analytics provides a detailed description of how big-data and analytics can be leveraged to deliver smart and sustainable buildings. Brief and readable, Smart Buildings Systems: Data Collection, Management and Analytics provides a broad range of data sources and a thorough explanation of the methods used to extract information from the vast quantities of available data. This reference provides a complete spectrum of smart building inputs. This includes the heuristics, rule-based logic engines, and machine learning algorithms that generate informational data. Written by an author with 10 years of experience, this reference offers data-driven approaches to improve equipment lifetimes, enhance occupant experience, and minimize energy consumption. Written from a building professionals' perspective, construction engineers, design engineers architects, owners and facilities managers, Smart Buildings Systems: Data Collection, Management and Analytics is a roadmap to technical details to the design, implementation, and ongoing operation of a smart building systems. This includes discussions on energy metering instrumentation, control loop theory, automation system architectures, and various computational approaches to fault detection. Step-by-step descriptions of large-scale practical implementations will provide the reader with a roadmap to elevate a building to a 'smart' operating state. Covers energy metering instrumentation, control loop theory, automation system architectures Includes the heuristics, rule-based logic engines, and machine learning algorithms Offers data-driven approaches to improve equipment lifetimes, enhance occupant experience, and minimize energy consumption Provides step-by-step descriptions of large-scale practical implementations with case studies

Download or read book Distributed Knowledge in the Building Management Systems Architecture for Smart Buildings written by Adrian Taboada Orozco and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The future of cities is at stake. Over the previous few decades, the population distribution has shifted substantially. Since 1980, people's ways of cohabiting have been challenged by the change from rural to urban migration. Cities now hold 60 % of the world's population. This massive concentration of people has resulted in poor connectivity, ineffective transportation, pollution, inadequate security, and energy waste. As a result, ensuring the sustainable growth of cities necessitates scalable technological breakthroughs that must give quality of life while maximizing resources. The main concern in cities is dealing with energy waste, especially in buildings, which represent 40 % of energy in the total consumption of cities.Therefore, this thesis addresses the emergent Smart Building field. The main goal is to work toward the concept of Building Operating Systems (BOS). BOS is a data-driven system that facilitates and enables the development of applications. Our studies have identified that the main barrier to BOS development is the integration of data and lack of context in a naturally and physically dispersed Building Management System (BMS). BMS is the underlying system that supports services in Building, and its understanding of its features is fundamental to achieving the main goal of this thesis. Therefore, this thesis first reviews the Smart Building field and then focuses on the BMS architecture. The results of the review serve as the basis for conceiving the main approach of this thesis, which is the WITTYM Approach. It aims to create and distribute buildings' knowledge by leveraging Building Information Modeling (BIM) and other heterogeneous data sources. WITTYM Approach is a conjunction of Ontologies, Knowledge, and Distribution Methods. The WITTYM Approach is evaluated through research hypotheses over use cases. Results have shown an optimization of BMS for data integration, applications, security, and decision-making response. Our work sets the basis for further research and applications on BMS.

Download or read book A Scalable and Secure System Architecture for Smart Buildings written by Georgios Lilis and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Mots-clés de l'auteur: smart building ; systems thinking ; scalable architectures ; building management systems ; distributed computing ; message-oriented middleware ; ICT interoperability architectures ; discrete event system ; parallel architectures ; building emulation.

Download or read book Data Driven Models Applied in Building Load Forecasting for Residential and Commercial Buildings written by SM Mahbobur Rahman and published by . This book was released on 2015 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: A significant portion of the operating costs of utilities comes from energy production. Machine learning methods are widely used for short-term load forecasts for commercial buildings and also the utility grid. These forecasts are used to minimize unit power production costs for the energy managers for better planning of power units and load management. In this work, three different state-of-art machine learning methods i.e. Artificial Neural Network, Support Vector Regression and Gaussian Process Regression are applied in hour ahead and 24 –hour ahead building energy forecasting. The work uses four residential buildings and one commercial building located in Downtown, San Antonio as test-bed using energy consumption data from those buildings monitored in real-time. Uncertainty quantification analysis is conducted to understand the confidence in each forecast using Bayesian Network. Using a combination of weather variables and historical load, forecasting is done in a supervised way based on a moving window training algorithm. A range of comparisons between different forecasting models in terms of relative accuracy are then presented.

Download or read book Data driven Model Predictive Control of Buildings written by Kui Weng and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Dynamic Bayesian Network Framework for Data Driven Fault Diagnosis and Prognosis of Smart Building Systems written by Ojas Man Singh Pradhan and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Buildings are subject to faults in their heating, ventilation and air-conditioning (HVAC) systems that can lead to excessive energy wastage, poor indoor climate, equipment failures and high maintenance costs. Field studies have shown that employing fault detection, diagnosis and prognosis (FDDP) tools followed up with equipment services and corrections can help achieve up to 40% of energy savings within the HVAC system and improve indoor climate, increase equipment lifecycle and reduce maintenance costs. The increasing adoption of building automation systems (BAS), Internet of Things (IoT) and other smart technologies in recent years have allowed large amounts of data to be continuously collected from building systems. This data-rich environment, along with the surge in data analytics and machine learning tools, has made cost-effective data-driven FDDP strategies possible. Compared to purely physics-based methods, data-driven methods require less explicit knowledge of the underlying physical system, thus are often easier to develop, and can learn certain intricate relationships that exist among data. Within the reported data-driven FDDP methods, there exists a few research gaps: 1) data imputation methods that leverage mutual information from correlated measurements to defy poor data quality from BAS have not been utilized efficiently; 2) there lacks a systematic and scalable fault diagnosis framework that incorporates probabilistic temporal relationships to track fault evolution; 3) existing fault diagnosis strategies typically focus on traditional rule-based control strategies and their scalability for advanced control strategies such as Guideline 36 have not been explored yet; 4) active threats information such as cyber-attacks, are typically not incorporated in an FDDP framework; 5) fault prognosis strategies to preemptively identify gradual faults for predictive maintenance have rarely been studied. This research attempts to address the above-mentioned research gaps through the following: Data Imputation: reported data imputation methods that are suitable for handling and repairing multi-source BAS data are evaluated. Data collected from a medium-sized, mixed-use institution building situated in Stockholm, Sweden and a small commercial building simulated in a laboratory setup is used to evaluate five different data imputation methods. Results demonstrate that incorporating time-lagged cross-correlations within the k-Nearest Neighbor (kNN) method helps to significantly improve the imputation accuracy and minimize the impact of repaired data on data-driven algorithms. Dynamic Bayesian Network (DBN)-based Framework for Cyber-Physical Fault Diagnosis: a DBN framework with discretized conditional probabilities parameters to represent the temporal relationships among building measurements is developed. Both domain knowledge and machine learning methods are used to develop the DBN structure and parameter model. The developed framework is evaluated for both traditional rule-based and Guideline 36 controls using datasets from a real building, a laboratory building, and a virtual testbed. Results show that the developed DBN framework is effective in diagnosing and isolating faults in systems even with different control strategies. The framework also successfully distinguishes whether system abnormalities originate from cyber-attacks or naturally occurring physical faults. Potential future direction to improve fault isolation using modified DBN topological structure is also reported in this study. DBN-based Framework for Fault Prognosis: an extension of the DBN framework in conjunction with Robust Multivariate Temporal (RMT) variate selection is proposed for fault prognosis. The RMT variate selection is used to extract localized temporal features from high dimensional datasets to determine the best inputs for training forecasting models. The expected fault-free behavior of multiple target variates, selected using domain knowledge, is forecasted using incoming data. The prediction errors generated from the forecasting phase are used as evidences in the DBN inference to estimate future fault probabilities. Gradual faults simulated in the virtual testbed are used to evaluate the prognosis framework. Results show that the developed framework is effective in prognosing gradual faults by leveraging the trending growth on the prediction errors. The research presented in this thesis contributes to the overall objective of developing a robust and cost-effective DBN-based framework for fault diagnosis and prognosis of building HVAC systems. Potential solutions to other existing challenges of implementing data-driven FDDP strategies, such as obtaining high-quality datasets, handling and repairing missing data, establishing a baseline model for detecting abnormalities despite other disturbances such as weather and internal conditions changes, and extracting temporal features from timeseries data are also examined.

Download or read book Smart Buildings Digitalization Two Volume Set written by O.V. Gnana Swathika and published by CRC Press. This book was released on 2022-05-27 with total page 747 pages. Available in PDF, EPUB and Kindle. Book excerpt: A smart building is the state-of-art in building with features that facilitates informed decision making based on the available data through smart metering and IoT sensors. This set provides useful information for developing smart buildings including significant improvement of energy efficiency, implementation of operational improvements and targeting sustainable environment to create an effective customer experience. It includes case studies from industrial results which provide cost effective solutions and integrates the digital SCADE solution. Describes complete implication of smart buildings via industrial, commercial and community platforms Systematically defines energy-efficient buildings, employing power consumption optimization techniques with inclusion of renewable energy sources Covers data centre and cyber security with excellent data storage features for smart buildings Includes systematic and detailed strategies for building air conditioning and lighting Details smart building security propulsion. This set is aimed at graduate students, researchers and professionals in building systems, architectural, and electrical engineering.

Download or read book Smart Buildings Digitalization written by O.V. Gnana Swathika and published by CRC Press. This book was released on 2022-02-23 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses various artificial intelligence and machine learning applications concerning smart buildings. It includes how renewable energy sources are integrated into smart buildings using suitable power electronic devices. The deployment of advanced technologies with monitoring, protection, and energy management features is included, along with a case study on automation. Overall, the focus is on architecture and related applications, such as power distribution, microgrids, photovoltaic systems, and renewable energy aspects. The chapters define smart building concepts and their related benefits. FEATURES Discusses various aspects of the role of the Internet of things (IoT) and machine learning in smart buildings Explains pertinent system architecture and focuses on power generation and distribution Covers power-enabling technologies for smart cities Includes photovoltaic system-integrated smart buildings This book is aimed at graduate students, researchers, and professionals in building systems engineering, architectural engineering, and electrical engineering.