Sensor Characteristics Reference Guide

The Buildings Technologies Office (BTO), within the U.S. Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy (EERE), is initiating a new program in Sensor and Controls. The vision of this program is: • Buildings operating automatically and continuously at peak energy efficiency over their lifetimes and interoperating effectively with the electric power grid. • Buildings that are self-configuring, self-commissioning, self-learning, self-diagnosing, self-healing, and self-transacting to enable continuous peak performance. • Lower overall building operating costs and higher asset valuation. The overarching goal is to capture 30% energy savings by enhanced management of energy consuming assets and systems through development of cost-effective sensors and controls. One step in achieving this vision is the publication of this Sensor Characteristics Reference Guide. The purpose of the guide is to inform building owners and operators of the current status, capabilities, and limitations of sensor technologies. It is hoped that this guide will aid in the design and procurement process and result in successful implementation of building sensor and control systems. DOE will also use this guide to identify research priorities, develop future specifications for potential market adoption, and provide market clarity through unbiased informatio

Developing a dynamic digital twin at a building level: Using Cambridge campus as case study

A Digital Twin (DT) refers to a digital replica of physical assets, processes and systems. DTs integrate artificial intelligence, machine learning and data analytics to create dynamic digital models that are able to learn and update the status of the physical counterpart from multiple sources. A DT, if equipped with appropriate algorithms will represent and predict future condition and performance of their physical counterparts. Current developments related to DTs are still at an early stage with respect to buildings and other infrastructure assets. Most of these developments focus on the architectural and engineering/construction point of view. Less attention has been paid to the operation & maintenance (O&M) phase, where the value potential is immense. A systematic and clear architecture verified with practical use cases for constructing a DT is the foremost step for effective operation and maintenance of assets. This paper presents a system architecture for developing dynamic DTs in building levels for integrating heterogeneous data sources, support intelligent data query, and provide smarter decision-making processes. This will further bridge the gaps between human relationships with buildings/regions via a more intelligent, visual and sustainable channels. This architecture is brought to life through the development of a dynamic DT demonstrator of the West Cambridge site of the University of Cambridge. Specifically, this demonstrator integrates an as-is multi-layered IFC Building Information Model (BIM), building management system data, space management data, real-time Internet of Things (IoT)-based sensor data, asset registry data, and an asset tagging platform. The demonstrator also includes two applications: (1) improving asset maintenance and asset tracking using Augmented Reality (AR); and (2) equipment failure prediction. The long-term goals of this demonstrator are also discussed in this paper

GreeDi: Energy Efficient Routing Algorithm for Big Data on Cloud

The ever-increasing density in cloud computing parties, i.e. users, services, providers and data centres, has led to a significant exponential growth in: data produced and transferred among the cloud computing parties; network traffic; and the energy consumed by the cloud computing massive infrastructure, which is required to respond quickly and effectively to users requests. Transferring big data volume among the aforementioned parties requires a high bandwidth connection, which consumes larger amounts of energy than just processing and storing big data on cloud data centres, and hence producing high carbon dioxide emissions. This power consumption is highly significant when transferring big data into a data centre located relatively far from the users geographical location. Thus, it became high-necessity to locate the lowest energy consumption route between the user and the designated data centre, while making sure the users requirements, e.g. response time, are met. The main contribution of this paper is GreeDi, a network-based routing algorithm to find the most energy efficient path to the cloud data centre for processing and storing big data. The algorithm is, first, formalised by the situation calculus. The linear, goal and dynamic programming approaches used to model the algorithm. The algorithm is then evaluated against the baseline shortest path algorithm with minimum number of nodes traversed, using a real Italian ISP physical network topology

Data Network Equipment Energy Use and Savings Potential in Buildings

Network connectivity has become nearly ubiquitous, and the energy use of the equipment required for this connectivity is growing. Network equipment consists of devices that primarily switch and route Internet Protocol (IP) packets from a source to a destination, and this category specifically excludes edge devices like PCs, servers and other sources and sinks of IP traffic. This paper presents the results of a study of network equipment energy use and includes case studies of networks in a campus, a medium commercial building, and a typical home. The total energy use of network equipment is the product of the stock of equipment in use, the power of each device, and their usage patterns. This information was gathered from market research reports, broadband market penetration studies, field metering, and interviews with network administrators and service providers. We estimate that network equipment in the USA used 18 TWh, or about 1percent of building electricity, in 2008 and that consumption is expected to grow at roughly 6percent per year to 23 TWh in 2012; world usage in 2008 was 51 TWh. This study shows that office building network switches and residential equipment are the two largest categories of energy use consuming 40percent and 30percent of the total respectively. We estimate potential energy savings for different scenarios using forecasts of equipment stock and energy use, and savings estimates range from 20percent to 50percent based on full market penetration of efficient technologies