A. H. Buckman

Bio: A. H. Buckman is an academic researcher from University of Sheffield. The author has contributed to research in topics: Building automation & Smart grid. The author has an hindex of 2, co-authored 3 publications receiving 154 citations.

What is a Smart Building

Abstract: Purpose – Within the building sector a lack of clarity in terminology does not help designers, clients or researchers. Non-domestic buildings have shown rapid increases in the use of advanced technology and control systems with varying drivers, many of which are labelled as intelligent. The term smart has been used interchangeably with intelligent without any clear distinction between the two. If the term Smart Buildings represented a separate, more advanced grouping, it would provide an opportunity to focus the future progress of non-domestic building development. The paper aims to discuss these issues. Design/methodology/approach – Drawing upon academic and industrial literature and experience, this paper reviews the scope of Intelligent Buildings and the current available definitions of Smart Buildings to form a clear definition of both smart and Intelligent Buildings. Findings – These definitions define the border between the intelligent and the (more advanced) Smart Building. The upper bound of the S...

What are smart grid optimised buildings

Abstract: Smart Grid Optimised Building (SGOB) can be thought of as meeting its service obligations to its occupants and minimising its operational cost and footprint to its owner while actively engaging with the electricity provider, enabling in this way the best use of the available resources. SGOBs differ from Smart Buildings, regarding their aim and objectives, as their design and energy systems are optimised for the needs of the Smart Grid. Conceptually, they must have an active interaction with the energy network through responses to dynamic electricity prices and carbon emissions, similarly to Active Buildings. Instead of being considered as a passive element of the energy equation like conventional buildings, SGOBs follow an original and innovative approach and have the capacity to transform to prosumers, with the deployment of on-site renewable energy sources and by participating in a 2-direction power exchange with the Network Operator. The current literature and research have followed an ad-hoc approach by focusing on conventional strategies on existing buildings, such as increasing the building energy efficiency or reducing the current energy loads. On the other hand, SGOBs are expected to consist of several optimised design elements, including thermal mass, shape, orientation, insulation and glazing. Furthermore, SGOBs can meet their energy loads with electricity, either directly from the grid or using their incorporated energy storage systems e.g. batteries. Electricity can be stored at times of low demand when the electricity tariffs are cheaper, and used on the following day to cover part of the peak load. Another possibility includes the load-levelling service, where the building is notified by the Network Operator to maintain its consumption below a power limit for a specific time period. This paper is the first to present the concept and the philosophy on which SGOBs are based, along with initial results, demonstrating how a building can adjust its loads to reduce stress on the grid.

[ACM Press the 2nd ACM Workshop - Zurich, Switzerland (2010.11.02-2010.11.02)] Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building - BuildSys \'10 - Occupancy-driven energy management for smart building automation

Abstract: Buildings are among the largest consumers of electricity in the US. A significant portion of this energy use in buildings can be attributed to HVAC systems used to maintain comfort for occupants. In most cases these building HVAC systems run on fixed schedules and do not employ any fine grained control based on detailed occupancy information. In this paper we present the design and implementation of a presence sensor platform that can be used for accurate occupancy detection at the level of individual offices. Our presence sensor is low-cost, wireless, and incrementally deployable within existing buildings. Using a pilot deployment of our system across ten offices over a two week period we identify significant opportunities for energy savings due to periods of vacancy. Our energy measurements show that our presence node has an estimated battery lifetime of over five years, while detecting occupancy accurately. Furthermore, using a building simulation framework and the occupancy information from our testbed, we show potential energy savings from 10% to 15% using our system.

Comprehensive analysis of the relationship between thermal comfort and building control research - A data-driven literature review

Abstract: Buildings are responsible for about 30–40% of global energy demand. At the same time, we humans spend almost our entire life, up to 80–90% of the time, inside of buildings. Reducing energy demand through optimal operation is the subject of building control research, while human satisfaction in buildings is studied in the thermal comfort community. Thus, balancing the two is necessary for a sustainable and comfortable building stock. We review both research fields and their relationship using a data-driven approach. Based on specific search terms, all relevant abstracts from the Web Of Science database are downloaded and analyzed using the text mining software VOSviewer. We visualize the scientific landscapes of historic and recent trends, and analyze the citation network to investigate the interaction between thermal comfort and building control research. We find that building control focuses predominantly on energy savings rather than incorporating results from thermal comfort, especially when it comes to occupant satisfaction. We identify potential research directions in terms of bridging the two fields.

Micro-location for Internet of Things equipped Smart Buildings

Abstract: Micro-location is the process of locating any entity with high accuracy (possibly in centimeters), while geofencing is the process of creating a virtual fence around a so-called Point of Interest (PoI). In this paper, we present an insight into various micro-location enabling technologies and services. We also discuss how these can accelerate the incorporation of Internet of Things (IoT) in smart buildings. We argue that micro-location based location-aware solutions can play a significant role in facilitating the tenants of an IoT equipped smart building. Also, such advanced technologies will enable the smart building control system through minimal actions performed by the tenants. We also highlight the existing and envisioned services to be provided by using micro-location enabling technologies. We describe the challenges and propose some potential solutions such that micro-location enabling technologies and services are thoroughly integrated with IoT equipped smart building.

Microlocation for Internet-of-Things-Equipped Smart Buildings

Abstract: Microlocation is the process of locating any entity with a very high accuracy (possibly in centimeters), whereas geofencing is the process of creating a virtual fence around a point of interest (PoI). In this paper, we present an insight into various microlocation enabling technologies, techniques, and services. We also discuss how they can accelerate the incorporation of Internet of Things (IoT) in smart buildings. We argue that micro-location-based location aware solutions can play a significant role in facilitating the tenants of an IoT-equipped smart building. Also, such advanced technologies will enable the smart building control system through minimal actions performed by the tenants. We also highlight the existing and envisioned services to be provided by using microlocation enabling technologies. We describe the challenges and propose some potential solutions, such that microlocation enabling technologies and services are thoroughly integrated with IoT-equipped smart building.

IoT Security Framework for Smart Cyber Infrastructures

Abstract: The Internet of Things (IoT) will connect not only computers and mobile devices, but it will also interconnect smart buildings, homes, and cities, as well as electrical grids, gas, and water networks, automobiles, airplanes, etc. IoT will lead to the development of a wide range of advanced information services that need to be processed in real-time and require data centers with large storage and computing power. The integration of IoT with Cloud and Fog Computing can bring not only the required computational power and storage capacity, but they enable IoT services to be pervasive, cost-effective, and can be accessed from anywhere using any device (mobile or stationary). However, IoT infrastructures and services will introduce grand security challenges due to the significant increase in the attack surface, complexity, heterogeneity and number of resources. In this paper, we present an IoT security framework for smart infrastructures such as Smart Homes (SH) and smart buildings (SB). We also present a general threat model that can be used to develop a security protection methodology for IoT services against cyber-attacks (known or unknown). Additionally, we show that Anomaly Behavior Analysis (ABA) Intrusion Detection System (ABA-IDS) can detect and classify a wide range of attacks against IoT sensors.