Energy Management and Analysis Exploiting Existing Building Management Systems Infrastructure and Data
Lead Research Organisation:
London South Bank University
Department Name: School of Engineering
Abstract
Non-domestic buildings were responsible for 120TWh energy consumption and 48MtCO2e emissions in the UK in 2010. There is an acknowledged 'performance gap' between the design expectation and operational reality of most buildings (typically 30-35%). The reasons for the gap are many but research has identified the following among them:
'Building controls and systems are overly complex'
'Design intent is not delivered on-site during construction'
'Commissioning is inadequate or not completed satisfactorily'
'There are insufficient means of measuring and managing the building systems' performance once operational'
'The building is not operated properly by the facilities team or occupiers'
Research has also identified that 'there is still insufficient bottom-up data on building energy use broken down by different building types.'
Our proposal will provide tools to reduce the performance gap using Building Management Systems (BMS) and dedicated platforms that provide analysis and feedback to users on sensor data. It will deliver from the bottom-up, converting large volumes of data input to the platform into simple user-friendly views, indicators, fault detection and diagnoses, while retaining full capabilities. Finally and crucially, it will reduce complexity and information overload by directing attention to actionable solutions by users to performance issues.
Our aim is to acquire and apply a broad range of knowledge and skills to substantially expand the capabilities and applications of an existing Ashden Award 2014-nominated building performance analysis software system that can used for multiple end users.
The project will build upon an existing software system that extracts data from existing BMS that undertakes analysis far beyond the capabilities of the current BMS (designed for control, not analysis). The current platform has already identified £500k of annual savings at King College London University and extensive savings at other sites.
The multidisciplinary and close collaboration between London South Bank University, Demand Logic, Environmental Design Solutions Limited and Verco aims to draw upon data-mining techniques to increase speed and scope of analysis, interface with modelling software to compare design versus performance, interface with strategic carbon management software, enable dynamic demand control and develop interfaces for a range of audiences - including education and training.
The system platform will also be used to educate future engineers at university level by providing them with real-world examples of sensor data, allowing them to perform data analysis techniques and solution provision based upon simulated problems identified in an example building.
'Building controls and systems are overly complex'
'Design intent is not delivered on-site during construction'
'Commissioning is inadequate or not completed satisfactorily'
'There are insufficient means of measuring and managing the building systems' performance once operational'
'The building is not operated properly by the facilities team or occupiers'
Research has also identified that 'there is still insufficient bottom-up data on building energy use broken down by different building types.'
Our proposal will provide tools to reduce the performance gap using Building Management Systems (BMS) and dedicated platforms that provide analysis and feedback to users on sensor data. It will deliver from the bottom-up, converting large volumes of data input to the platform into simple user-friendly views, indicators, fault detection and diagnoses, while retaining full capabilities. Finally and crucially, it will reduce complexity and information overload by directing attention to actionable solutions by users to performance issues.
Our aim is to acquire and apply a broad range of knowledge and skills to substantially expand the capabilities and applications of an existing Ashden Award 2014-nominated building performance analysis software system that can used for multiple end users.
The project will build upon an existing software system that extracts data from existing BMS that undertakes analysis far beyond the capabilities of the current BMS (designed for control, not analysis). The current platform has already identified £500k of annual savings at King College London University and extensive savings at other sites.
The multidisciplinary and close collaboration between London South Bank University, Demand Logic, Environmental Design Solutions Limited and Verco aims to draw upon data-mining techniques to increase speed and scope of analysis, interface with modelling software to compare design versus performance, interface with strategic carbon management software, enable dynamic demand control and develop interfaces for a range of audiences - including education and training.
The system platform will also be used to educate future engineers at university level by providing them with real-world examples of sensor data, allowing them to perform data analysis techniques and solution provision based upon simulated problems identified in an example building.
Planned Impact
Technological advancements and implementation success require user engagement as it underpins our objectives: (a) long-term sustainability; (b) visible and informed assistance to provide improved plant management and energy wastage reductions within non-domestic environments ensuring the onward utilisation of the research data, proposed output tools (the developed GUI, integrated platform) and results. We will achieve this by implementing a two-pronged approach built around open research/data and active engagement.
The project will adopt an 'open research' model to ensure maximum result dissemination and exploitation. Thus, all outputs (papers, reports, materials and data which are not confidential/disclosive) will be placed in the public domain via the project website and/or an appropriate archive service as soon as practical, ensuring appropriate re-use terms (e.g. the Creative Commons-like attribution, non-commercial re-use model) for knowledge codified in papers or reports and for the data generated. This approach will also apply to any intellectual property codified in the system platform itself although it is envisaged more stringent commercial re-use terms will apply.
In order to leverage the open research and open data approaches, we will develop closely managed list of activities which aim to a) raise awareness of our research, b) engage potential research users in our research and c) draw some of those users into deeper interactions with the project. To do this, we cluster our users into academic peers; government and NGO policy analysts and strategists; commercial researchers, building developers, plant item manufacturers, tenants, landlords, consultancy firms and the general public. We will actively seek the engagement of all these groups using a range of
methods suited to the specific target audiences. For each, we plan tailored activities and these groups and the methods used together with our intended outcomes are set out in the appended Pathways to Impact.
At the project outset we will instigate a website and Facebook page/Twitter feed. The social media channels will be used for regular dissemination of project news, events, publications and summary results (c.f. open research). They will also be used to engage all potential user groups with our progress including potentially experimental elicitation of system requirements. These channels will also be used to support our open research/open data approach through the provision of fora by which research and data users outside the project can share experiences, expertise and results.
Once developed, the platform will be used on London South Bank University (LSBU) short courses, modules and lab demonstrations for students who are on our engineering courses. This platform will engage students on sensor monitoring, data mining, information identification, extraction and understanding. We will use this to create real-world experiences to engage and inform student interest in building energy management and will create projects at undergraduate and postgraduate levels in these areas, subsequently, producing experienced graduates aiming to enter the industry. We hope
to be able to market the educational platform to interested parties (subject to IP discussions between partners).
In parallel to traditional academic conference and journal paper outputs we will develop a mini-exhibition of the system amongst LSBU, Demand Logic and the other partners to display, perform larger scale (live test bed itself) and smaller scale demonstrations, publicise the system and its results. We intend this programme of activities to lead to: the creation of a long term self-sustaining community of scholars; the identification of opportunities for additional or new studies, the exploitation of all aspects of the system platform results and the wider engagement of the business sectors and general public in the re-consideration of their energy-use practice.
The project will adopt an 'open research' model to ensure maximum result dissemination and exploitation. Thus, all outputs (papers, reports, materials and data which are not confidential/disclosive) will be placed in the public domain via the project website and/or an appropriate archive service as soon as practical, ensuring appropriate re-use terms (e.g. the Creative Commons-like attribution, non-commercial re-use model) for knowledge codified in papers or reports and for the data generated. This approach will also apply to any intellectual property codified in the system platform itself although it is envisaged more stringent commercial re-use terms will apply.
In order to leverage the open research and open data approaches, we will develop closely managed list of activities which aim to a) raise awareness of our research, b) engage potential research users in our research and c) draw some of those users into deeper interactions with the project. To do this, we cluster our users into academic peers; government and NGO policy analysts and strategists; commercial researchers, building developers, plant item manufacturers, tenants, landlords, consultancy firms and the general public. We will actively seek the engagement of all these groups using a range of
methods suited to the specific target audiences. For each, we plan tailored activities and these groups and the methods used together with our intended outcomes are set out in the appended Pathways to Impact.
At the project outset we will instigate a website and Facebook page/Twitter feed. The social media channels will be used for regular dissemination of project news, events, publications and summary results (c.f. open research). They will also be used to engage all potential user groups with our progress including potentially experimental elicitation of system requirements. These channels will also be used to support our open research/open data approach through the provision of fora by which research and data users outside the project can share experiences, expertise and results.
Once developed, the platform will be used on London South Bank University (LSBU) short courses, modules and lab demonstrations for students who are on our engineering courses. This platform will engage students on sensor monitoring, data mining, information identification, extraction and understanding. We will use this to create real-world experiences to engage and inform student interest in building energy management and will create projects at undergraduate and postgraduate levels in these areas, subsequently, producing experienced graduates aiming to enter the industry. We hope
to be able to market the educational platform to interested parties (subject to IP discussions between partners).
In parallel to traditional academic conference and journal paper outputs we will develop a mini-exhibition of the system amongst LSBU, Demand Logic and the other partners to display, perform larger scale (live test bed itself) and smaller scale demonstrations, publicise the system and its results. We intend this programme of activities to lead to: the creation of a long term self-sustaining community of scholars; the identification of opportunities for additional or new studies, the exploitation of all aspects of the system platform results and the wider engagement of the business sectors and general public in the re-consideration of their energy-use practice.
Publications
Siddiqui HUR
(2021)
Non-Invasive Driver Drowsiness Detection System.
in Sensors (Basel, Switzerland)
Siddiqui H
(2022)
Emotion classification using temporal and spectral features from IR-UWB-based respiration data
in Multimedia Tools and Applications
Shafique R
(2021)
A Novel Approach to Railway Track Faults Detection Using Acoustic Analysis.
in Sensors (Basel, Switzerland)
Riaz M
(2020)
Experimentally validated smart card ultra-high frequency tag antenna for free space and near body scenarios
in IET Microwaves, Antennas & Propagation
Rana SP
(2023)
Radiation-Free Microwave Technology for Breast Lesion Detection Using Supervised Machine Learning Model.
in Tomography (Ann Arbor, Mich.)
Rana SP
(2018)
A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building.
in Sensors (Basel, Switzerland)
Rana S
(2021)
3-D Gait Abnormality Detection Employing Contactless IR-UWB Sensing Phenomenon
in IEEE Transactions on Instrumentation and Measurement
Rana S
(2019)
Non-Contact Human Gait Identification Through IR-UWB Edge-Based Monitoring Sensor
in IEEE Sensors Journal
Rana S
(2022)
Markerless Gait Classification Employing 3D IR-UWB Physiological Motion Sensing
in IEEE Sensors Journal
Rana S
(2017)
UWB localization employing supervised learning method
Oladimeji M
(2016)
Iterated local search algorithm for clustering wireless sensor networks
Oladimeji M
(2017)
HACH: Heuristic Algorithm for Clustering Hierarchy protocol in wireless sensor networks
in Applied Soft Computing
Oladimeji M
(2016)
Applications of Evolutionary Computation
Khalid B
(2022)
3D Huygens Principle Based Microwave Imaging Through MammoWave Device: Validation Through Phantoms
in IEEE Access
Hajderanj L
(2020)
Single- and Multi-Distribution Dimensionality Reduction Approaches for a Better Data Structure Capturing
in IEEE Access
Gupta M
(2020)
Knowledge Discovery Using Topological Analysis for Building Sensor Data
in Sensors
Dey M.
(2018)
Smart Building Creation in Large Scale HVAC Environments through Automated Fault Detection and Diagnosis
in Future Generation Computer Systems
Dey M
(2020)
Smart building creation in large scale HVAC environments through automated fault detection and diagnosis
in Future Generation Computer Systems
Description | Inefficient heating in large non-domestic buildings (e.g. high rise office space and shopping centres) is a major contributor to carbon emissions and climate change. Her work has helped to improve the efficiency of Building Energy Management Systems (BEMS, i.e. the main controlling systems of large building heating and cooling) by using artificial intelligence to improve detection of BEMS faults. Working with Demand Logic a UK based SME that produces buildings' BEMS platforms to optimise energy savings) and their products by: o Reduced energy costs spend for their UK and EU clients by 10%-30% o Delivered £1.8 million energy cost savings annually o Annual savings of 11,800 tonnes of CO2. |
Exploitation Route | Large data sets with little information or shared standards in data keeping can be processed and mined to produce useful information in the energy sector. Findings from this project can be used to a) Emphasise the importance of agreed standards, 2) the professionalism in which a building is fitted and not just the design process. |
Sectors | Construction Digital/Communication/Information Technologies (including Software) Energy Environment Healthcare Retail Transport |
URL | https://www.gov.uk/government/case-studies/demand-logic-energy-savings-breakthrough-in-buildings |
Description | The work has resulted in savings of over 4 million pounds in energy costs for a number of companies in London. The work is ongoing with software product development in solar farm systems for predictive maintenance , flexibility and resilience development. |
First Year Of Impact | 2019 |
Sector | Construction,Digital/Communication/Information Technologies (including Software),Education,Electronics,Energy,Environment,Healthcare |
Impact Types | Economic Policy & public services |