Diagnostic Visualisation of Building Management System Energy Data
- Paul Shabajee, Chris Preist, Daniel Schien, John Brenton, Chris Jones
- Corresponding Author
- Paul Shabajee
Available Online August 2016.
- https://doi.org/10.2991/ict4s-16.2016.29How to use a DOI?
- building management systems, energy management, visualization
- This poster presents work done at the University of Bristol as part of the IODiCUS (Interoperable Open Digital Control Unit System) project . The project is, in part, focused on exploring campus scale energy management and improving the usefulness of data from Building Management Systems (BMS) across the campus. Student halls of residence are in some re-spects comparable to social housing. A key aspect of that activ-ity is to use BMS data to support and evaluate programmes that, for example, aim to help reduce overall energy use and reduce electricity demand during (expensive) peak periods. Building Management Systems provide large volumes of fine grained and rich data about building operations and in par-ticular energy use. For example, for electrical heating in stu-dent residential halls, a BMS provides device/room status every few minutes. Data available includes current temperature, thermostatic set-point, whether the device relay is 'on', opera-tional 'program' (rules) that are active for the device and whether the program has been overridden by the resident. However, gaining actionable oversight and insight from such data can be problematic. As part of the IODiCUS project  we have developed a prototype interactive diagnostic visualisation tool that uses BMS data to support the work of University of Bristol building facilities managers. The software behind the interface combines sensor data from the BMS with 'metadata' about the devices, (e.g. power rating) and buildings, (e.g. type of 'node' - room heater, kitchen heater, water heater, etc.). Using this integrated data, the interface provides the both oversight of energy use across a set of buildings (kWh) and interactive multi-dimensional filtering based on BMS data parameters and key temporal dimensions. Filtering enables the user to drill down to see energy use (kWh) of, for example, particular types of node, on particular days of the week, under specific operational pro-grams, etc. The interfaces are browser based and developed using open source software, including, d3.js (https://d3js.org/), Crossfilter (http://d3js.org/), dc.js (https://dc-js.github.io/dc.js/), bootstrap (http://getbootstrap.com/) & jQuery (http://jquery.com/). The tools enable facilities managers to carry out key diag-nostic tasks - identified in consultation with University of Bris-tol Estates Department. Examples of diagnostic tasks supported include: i) obtaining an overview of energy use and energy using behaviours across device types, spaces/facilities and timescales, ii) identifying patterns, energy use 'hot spots' and variations in behaviours of buildings, spaces, equipment and residents, iii) evaluation of existing and piloted energy man-agement initiatives and iv) spotting issues and anomalies that require further investigation. The poster presents an illustrative example of the inter-face's ability to support facilities managers in investigating the effectiveness of BMS controlled energy reduction pro
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Paul Shabajee AU - Chris Preist AU - Daniel Schien AU - John Brenton AU - Chris Jones PY - 2016/08 DA - 2016/08 TI - Diagnostic Visualisation of Building Management System Energy Data BT - ICT for Sustainability 2016 PB - Atlantis Press SN - 2352-538X UR - https://doi.org/10.2991/ict4s-16.2016.29 DO - https://doi.org/10.2991/ict4s-16.2016.29 ID - Shabajee2016/08 ER -