REFIT: Personalised Retrofit Decision Support Tools for UK Homes using Smart Home Technology
Lead Research Organisation:
University of Strathclyde
Department Name: Electronic and Electrical Engineering
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Organisations
Publications
Stankovic V
(2013)
Distributed Compression for Condition Monitoring of Wind Farms
in IEEE Transactions on Sustainable Energy
Seeam, A.
(2013)
Improving Energy Efficiency with Smart Home Appliance Monitoring
Liao, J
(2014)
Disaggregation for low sampling rate data
Stankovic V
(2014)
A graph-based signal processing approach for low-rate energy disaggregation
Murray, D
(2014)
How to make efficient use of kettles
Altrabalsi H
(2014)
A low-complexity energy disaggregation method: Performance and robustness
| Description | The team has successfully tackled the challenges of energy disaggregation, termed Non-Intrusive appliance Load Monitoring (NILM) of low-granularity, one dimensional smart-meter data via the development of a range of low complexity algorithms for a large range of appliances. These NILM algorithms formed the basis of further analytical work developed in the group, namely informing appliance retrofit/upgrade decisions, predicting demand from appliances, finding opportunities for load shifting and developing activity recognition algorithms that map appliances or technologies to activities in order to understand households' daily routines. The latter challenge of how to interpret real-time energy data in terms of activities from smart meter data is tackled in conjunction with our social science partner, showing the effectiveness of jointly considering quantitative and qualitative data through mixed methods. In summary, we have demonstrated that, from smart meter data alone, we can generate itemised billing down to appliance level or down to activity level and extracting the time-use profile and energy-profile of any particular activity, such as cooking, laundering or washing, in a particular household and across households and what are the cost implications. |
| Exploitation Route | Our findings answer the following questions: • How can signal information processing turn low resolution smart meter data into meaningful information? • Can we develop consistently accurate and practical non-intrusive disaggregation solutions from low-resolution smart meter one-dimensional data? • How much do activities, such as cooking and energy services like refrigeration, account for in a household's total electricity consumption?, • Can we generalise when energy-intensive activities occur within households of similar occupancy? • Which are the largest energy-consuming activities, and what are the implications for demand management and feedback? Load disaggregation is seen as the next step towards providing effective energy feedback. Load disaggregation providers supply energy disaggregation through a combination of hardware submetering and software analysis. However, these solutions are currently limited to disaggregating high loads and industry is keen to adopt approaches that can operate at Smart Meter data rates, are practical, simple, accurate, and robust for a range of training periods. Our new algorithms are good candidates for future industry uptake. Our activity recognition method provides an alternative, less expensive option that does not depend on the traditional approach of time diaries, for generating daily time-use profiles of a subset of energy using activities, which can then be compared with national time-use statistics to identify variability, or to segment households. This is attractive for service providers in the Smart Home market to provide value-added feedback to users. Finally, automatic activity recognition is an important enabler of home automation and effective home energy management systems, as well as Assisted Daily Living with further implications for remote healthcare. |
| Sectors | Communities and Social Services/Policy,Digital/Communication/Information Technologies (including Software),Energy,Environment |
| URL | http://dx.doi.org/10.15129/31da3ece-f902-4e95-a093-e0a9536983c4 |
| Description | The research supported by this award led to two unique dataset of time-stamped power load measurements, at household and appliance levels. The first dataset contains raw readings from 20 houses in England monitored for a continuous period of about two years as the households went about their daily lives. The second dataset is a cleaned version of the first dataset, where corrupt or missing measurements and wrongly labelled submetered appliances have been checked and corrected. These are the only such UK datasets at this longitudinal scale with a sampling rate below 1min, that is, sampling is carried out at 8 second resolution which is akin to the planned load measurements from the UK smart meters being rolled out, via the Consumer Access Device. The datasets are publicly available on the Strathclyde repository, and the data collection, cleaning of the data and overall description are described in the corresponding Nature Scientific Data Journal paper. The curated datasets have already attracted the attention of many academic research groups as well as the energy and smart home industry as evidenced by citations. The work also supported creating two additional datasets on appliance-level anomaly detection. Additionally, the datasets, non-intrusive appliance load monitoring (NILM) and appliance mining methods developed have yielded additional research directions, e.g., activity recognition where energy consumption is quantified through the lens of activities, load-shifting (exploiting flexibility in time-of-use of appliances to manage peak demand), retrofit advice and smart home automation. The core NILM methodology developed in REFIT has been refined over the years to further tune the load disaggregation solutions for a wider range of appliances, in more realistic conditions and higher levels of accuracy, resulting in many highly-cited publications, indicating timeliness and relevance of the work. The work has also enabled further collaboration with UK and international community. The innovative work on the foundational algorithms are being tested at scale as part of the H2020 Eco-Bot project with energy utilities and SMEs across Europe, with a view to further exploit the algorithms in a larger range of applications such as Energy Feedback and Food Systems in other projects, and potentially commercialisation. |
| First Year Of Impact | 2016 |
| Sector | Digital/Communication/Information Technologies (including Software),Energy |
| Impact Types | Societal,Policy & public services |
| Description | (Eco-Bot) - Personalised ICT-tools for the Active Engagement of Consumers Towards Sustainable Energy |
| Amount | € 2,521,566 (EUR) |
| Funding ID | 767625 |
| Organisation | European Commission |
| Sector | Public |
| Country | European Union (EU) |
| Start | 10/2017 |
| End | 04/2021 |
| Description | Appliance modelling and usage analysis |
| Amount | £37,115 (GBP) |
| Organisation | NESTEC, Inc. |
| Sector | Private |
| Country | United States |
| Start | 08/2016 |
| End | 03/2017 |
| Description | MSCA-RISE - Marie Sklodowska-Curie Research and Innovation Staff Exchange (RISE) |
| Amount | € 859,500 (EUR) |
| Funding ID | H2020-MSCA-RISE-2016 734331 |
| Organisation | European Commission |
| Sector | Public |
| Country | European Union (EU) |
| Start | 01/2017 |
| End | 12/2020 |
| Description | Personalised ICT-tools for the Active Engagement of Consumers Towards Sustainable Energy |
| Amount | € 1,964,145 (EUR) |
| Funding ID | 767625 |
| Organisation | European Commission H2020 |
| Sector | Public |
| Country | Belgium |
| Start | 10/2017 |
| End | 12/2020 |
| Title | REFIT: Electrical Load Measurements |
| Description | The REFIT Electrical Load Measurements dataset includes electrical consumption data in Watts for 20 households at aggregate and appliance level, timestamped and sampled at 6-8 second intervals. This dataset is intended to be used for consumption statistics, load disaggregation algorithm development and testing, time and energy use profiles and time use statistics. This data repository is hosted at the University of Strathclyde, and has been populated from data obtained from a remote monitoring platform, whose server was at the University of Strathclyde, capturing data in real-time from households in the Loughborough area of the UK. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2015 |
| Provided To Others? | Yes |
| Impact | To date, the REFIT dataset is the largest UK electrical measurements repository, containing measurements sampled at under 1 minute and gathered continuously over a period of 2 years. As such, it is very valuable for designing and testing non-intrusive load monitoring (NILM) algorithms. To this effect, the creators of NILMTK, which is an open source toolkit designed specifically to enable the comparison of energy disaggregation algorithms in a reproducible manner, have included a REFIT data converter so that algorithms from anyone can be tested. |
| URL | https://pure.strath.ac.uk/portal/en/datasets/refit-electrical-load-measurements(31da3ece-f902-4e95-a... |
| Title | REFIT: Electrical Load Measurements (Cleaned) |
| Description | The REFIT Electrical Load Measurements dataset includes cleaned electrical consumption data in Watts for 20 households at aggregate and appliance level, timestamped and sampled at 8 second intervals. This dataset is intended to be used for research into energy conservation and advanced energy services, ranging from non-intrusive appliance load monitoring, demand response measures, tailored energy and retrofit advice, appliance usage analysis, consumption and time-use statistics and smart home/building automation. When using this dataset please cite the following paper in Scientific Data, http://dx.doi.org/10.1038/sdata.2016.122 This version of the dataset has been cleaned in the following ways: - Timestamp duplicates have been merged. - IAM (Individual Appliance Monitor) readings set to 0 Watts if above 4000 Watts (above the rated limit of the sensor). - Each IAM has been processed to ensure that it only shows readings for one appliance, where possible. - The ReadMe file has been updated with information about monitored appliance changes. - NaN values have been forward filled (< 2 minute gaps) or zeroed (> 2 minute gaps). This work has been carried out as part of the REFIT project (`Personalised Retrofit Decision Support Tools for UK Homes using Smart Home Technology', Grant Reference EP/K002368/1/1). REFIT is a consortium of three universities - Loughborough, Strathclyde and East Anglia - and ten industry stakeholders funded by the Engineering and Physical Sciences Research Council (EPSRC) under the Transforming Energy Demand in Buildings through Digital Innovation (BuildTEDDI) funding programme. A raw data version of this dataset (deposited 23/09/2015) is also available from the Data Sets link below. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2016 |
| Provided To Others? | Yes |
| Impact | n/a |
| Description | Contribution to the EPSRC-funded METER workshop at the Environmental Change Institute, University of Oxford |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | The Expert workshop, an event from the EPSRC METER project, gathered academics working in Energy Demand and Time-use research to ensure that the project aligned with research priorities of the invited experts and produced relevant data. What was relevant to this workshop was REFIT's work on activity recognition work to understand and quantify energy intensity of activities in a household. Discussions generated new ideas and potential collaboration with Dr Grunewald and others, |
| Year(s) Of Engagement Activity | 2015 |
| Description | DECC Workshop: Specifying and Costing Monitoring Equipment for a Longitudinal Energy Study |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Policymakers/politicians |
| Results and Impact | DECC Workshop: Specifying and Costing Monitoring Equipment for a Longitudinal Energy Study Invitation to a workshop organised by DECC, where a number of key stakeholders across Government, Devolved Administrations, research councils and the academic community were invited to contribute their ideas to development a longitudinal survey on energy use in the domestic sector to explore evidence needs for the potential survey and to gather views on the most useful applications for data that could be collected by this survey. |
| Year(s) Of Engagement Activity | 2015 |
| Description | Invited talk on REFIT public dataset |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | The talk described the REFIT Electrical Loads Measurement dataset, released to the public in Sept 2015 under a creative commons licence. Specifically, we discussed the collection methodology, data cleaning, data formatting and data quality assessment, as well as how the dataset differs from similar public datasets. The talk sparked further questions and debate, which helped shape the 'Nature Scientific Data' as an open access journal article. We also appreciated the usefulness to the academic research groups and companies of this dataset and how it was being used, and how to design metadata. |
| Year(s) Of Engagement Activity | 2016 |
| URL | http://www.nilm.eu/nilm-workshop-2016/ |
| Description | Invited talk on domestic energy feedback algorithms @ University of Edinburgh |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Postgraduate students |
| Results and Impact | Invited talk for ANC/DTC Seminar at the Institute for Adaptive and Neural Computation at the School of Informatics, University of Edinburgh Attended by researchers and academics in energy monitoring and feedback. Sparked debate on the possibilities and limitations of data monitoring for making useful inferences from smart meter data. This has enabled further opportunities for collaboration in the area of data monitoring and load disaggregation. |
| Year(s) Of Engagement Activity | 2015 |
| URL | http://www.anc.ed.ac.uk/events/lina-stankovic-2015 |
| Description | Reasoning daily activities in elderly homes based on energy monitoring presented at OB-14 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Talk generated interest in the developed data monitoring and processing methods and the conclusions from the study. Increased visibility of the project and its results |
| Year(s) Of Engagement Activity | 2014 |
| Description | Scientific contributor to workshop on 21st century standards and labelling programmes |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Third sector organisations |
| Results and Impact | Scientific contributor to workshop on 21st century standards and labelling programmes Invited to present and lead discussions in the workshop to explore how technology innovations can be used to improve appliance and equipment energy efficiency policies and programmes. The workshop was organised by International Energy Agency (IEA) and the IEA Implementing Agreement for Energy Efficient End-use Equipment (4E) the Super-efficient Equipment and Appliance Deployment (SEAD) initiative of the Clean Energy Ministerial and the International Partnership for Energy Efficiency. The presentation was entitled "Analytical tools for understanding appliance usage patterns and the potential for energy savings." Audience included European Energy Agencies, DECC and policy markers. |
| Year(s) Of Engagement Activity | 2015 |
| URL | https://www.iea.org/workshops/21st-century-energy-efficiency-standards-and-labelling-programmes.html |