Tuesday 29 December 2015

3rd Int’l Workshop on NILM — SAVE THE DATE!

The 3rd International Workshop on Non-Intrusive Load Monitoring (NILM) will be held in Vancouver, Canada from May 14 to 15, 2016. The venue for the workshop is still under consideration. Last workshop was held June/2014 at the University of Texas, Austin, in Austin, TX.

The agenda for the NILM2016 is still being defined, but the current proposal is to have two full-days of activities. There will be working groups, paper presentations and a poster session. We have two confirmed invited speakers George W. Hart and Robert Sonderegger (Itron). Website will go live first week of January 2016 (http://nilmworkshop.org/2016/).


The mission of this workshop is to create a forum that can unite all the researchers, practitioners, and students that are working on the topic of energy disaggregation. The main objective of this event is to review the main types of approaches that have been explored to date to solve the problem of electricity disaggregation, and to then discuss possible paths forward knowing what has been tried and what has yet to be experimented. We also intend to have a group discussion about possible solutions to the growing need for standardized datasets and performance metrics that can allow the field to move forward, as well as possible areas of collaboration among different research groups.


We invite all researchers working on NILM-related topics to submit 4-page papers to the conference for oral presentation or presentation during a poster session. All submissions must use IEEE style files for LaTeX or Word. We encourage authors to submit papers on research that is ongoing or contains recent results: as the workshop will only include online proceedings, submission to the workshop will not prevent any material from being submitted later to a journal or conference.


Paper Submission Deadline: March 15
Notification of Acceptance: April 10
Final Paper Submission Due: April 24
Registration Deadline: April 24

Registration Fee: FREE

Keynote Speaker: George W. Hart
Invited Speaker: Robert Sonderegger, Itron

We are looking forward to welcoming you to NILM 2016 in Vancouver!

Stephen Makonin
Workshop General Chair
3rd International Workshop on Non-Intrusive Load Monitoring (NILM)

Update 04.01.2016: deadlines have been pushed back.
Update 18.02.2016: updated CFP available on conference website

Wednesday 16 December 2015

REFIT Electrical Load Measurements dataset released

The REFIT Electrical Load Measurements dataset is an output of the REFIT: Personalised Retrofit Decision Support Tools for UK Homes using Smart Home Technology project, which is a consortium of three universities - Loughborough, Strathclyde and East Anglia. The whole team contributed to the data acquisition and dataset design. The data set contains active power measurements of the aggregate and 9 individual appliances from 20 homes in the Loughborough area of the UK, at a resolution of 1 sample every 8 seconds. This makes the REFIT the largest UK data set (in number of houses) which contains appliance level data at a sample rate great than once per minute. In addition, aggregate gas consumption data was also recorded at 30 minute intervals, although no sub-metered data was also collected. It should be noted that the data has been compressed by removing samples for which the power demand had not changed since the last reading. Further details can be found in a presentation from the EEDAL 2015 conference, a detailed technical report, and the dataset readme file.

I've updated my list of Public Data Sets to include the REFIT data set, and I'm also hoping that a NILMTK converter will follow shortly!

Edit 04.01.2016: The REFIT is not the only UK dataset to contain appliance level data at a sample rate greater than 1 minute as initially claimed! The UK-DALE data set also meets that criteria.

Tuesday 15 December 2015

Kien Trung's PhD defence

Last week I had the pleasure of being an examiner for Nguyen Kien Trung's PhD thesis at the Université Nice Sophia Antipolis. Kien's thesis focused on a an efficient electricity disaggregation algorithm which he deployed using a low-cost system-on-chip. Kien successfully defended his thesis against the panel's questions, which sometimes came in a mixture of English and French, which I found particularly very impressive!

I also managed a brief visit to Qualisteo's office, who demonstrated some the disaggregation algorithms they're applying across a range of built environments, including the Eiffel Tower, a motorway tunnel and a sports stadium. I had a great time, but hopefully my next trip to Nice will last a little longer than 24 hours!

Tuesday 1 December 2015

NILM @ GlobalSIP 2015

The 3rd IEEE Global Conference on Signal and Information Processing will be held next month (14-16 December 2015) in Orlando, Florida. I was excited to see quite how many papers in the program are related to energy disaggregation, which are split across two sessions organised as part of the Smart Buildings Symposium and the Inference and Prediction session:

  • Toward a Semi-Supervised Non-Intrusive Load Monitoring System for Event-based Energy Disaggregation. Karim Said Barsim and Bin Yang (University of Stuttgart, Germany)
  • A new approach for supervised power disaggregation by using a deep recurrent LSTM network. Lukas Mauch and Bin Yang (University of Stuttgart, Germany)
  • Blind Non-intrusive Appliance Load Monitoring using Graph-based Signal Processing. Bochao Zhao, Vladimir Stankovic and Lina Stankovic (University of Strathclyde, United Kingdom)
  • A New Unsupervised Event Detector For Non-Intrusive Load Monitoring. Benjamin Wild, Karim Said Barsim and Bin Yang (University of Stuttgart, Germany)
  • Dataport and NILMTK: A Building data set Designed for Non-intrusive Load Monitoring. Oliver Parson (University of Southampton, United Kingdom); Grant Fisher and April Hersey (Pecan Street Inc, USA); Nipun Batra (IIIT Delhi, India); Jack Kelly (Imperial College London, United Kingdom); Amarjeet Singh (IIIT-Delhi, India); William J Knottenbelt (Imperial College London, United Kingdom); Alex Rogers (University of Southampton, United Kingdom)
  • Non-Intrusive Load Monitoring: A Power Consumption Based Relaxation. Kyle Anderson, Jose Moura and Mario Berges (Carnegie Mellon University, USA)
  • A feasibility study of automated plug-load identification from high-frequency measurements. Jingkun Gao (Carnegie Mellon University, USA); Emre C Kara (Lawrence Berkeley National Laboratory, USA); Suman Giri and Mario Berges (Carnegie Mellon University, USA)
  • Single-Channel Compressive Sampling of Electrical Data for Non-Intrusive Load Monitoring. Michelle Clark and Lutz Lampe (University of British Columbia, Canada)
  • Non-Intrusive Load Monitoring of HVAC Components using Signal Unmixing. Alireza Rahimpour (The University of Tennessee at Knoxville, USA); Hairong Qi (the University of Tennessee, USA)

  • I'm really looking forward to the conference, and will do my best to update this post with links to papers as they become available.

    Tuesday 17 November 2015

    Hangout On Air: NILMTK Algorithm Interface Discussion

    This Friday, we're planning to have a discussion around how best to integrate existing disaggregation algorithms into NILMTK via a Google Hangout On Air. The Hangout will take place at 16.30 UTC on Friday 20th November 2015, which I believe translates to 08.30 PST and 22.00 IST. Jack Kelly, Nipun Batra and myself will be available give the NILMTK perspective, while Stephen Makonin and Mingjun Zhong will be joining us to discuss how they'd like to integrate their algorithms into the toolkit.

    A provisional agenda for the call is:

    I've created a Google+ event which provides more details about the call. If you'd like to join the discussion, please follow the link to the event and launch the Hangout from the event page. If you'd prefer just listen in rather than joining the discussion, you can watch and listen live on YouTube. The video from the Hangout should be available on YouTube after the call, and I will try to update this post with the corresponding link.

    Update 23.11.2015: a video from the call is available on YouTube.

    Monday 9 November 2015

    BuildSys 2015: My Disaggregation is Better Than Yours

    Akshay presenting his paper on LocED

    Last week I had the pleaure of chairing a session named 'My disaggregation is better than yours', at the 2015 BuildSys conference in Seoul. The session featured presentations of the following three papers:
    • Multi-User Energy Consumption Monitoring and Anomaly Detection with Partial Context Information. Pandarasamy Arjunan (IIIT-Delhi), Harshad D Khadilkar (IBM Research), Tanuja Ganu (IBM Research), Zainul M Charbiwala (IBM Research), Amarjeet Singh (IIIT-Delhi), Pushpendra Singh (IIIT-Delhi)
    • LocED: Location-aware Energy Disaggregation Framework. S.N. Akshay Uttama Nambi (TUDelft), Antonio Reyes Lua (TUDelft), R. Venkatesha Prasad (TUDelft)
    • Neural NILM: Deep Neural Networks Applied to Energy Disaggregation. Jack Kelly (Imperial College London), William Knottenbelt (Imperial College London) [joint best presentation]
    Even outside this session, there were a few other papers relevant to NILM, including the following papers:
    To the best of my knowlege, this is the first conference with published proceedings with such a large amount of work related energy disaggregation. I'm glad that NILM is starting to find a common home at such a great venue, and I'm already looking forward to BuildSys 2016 at Stanford!

    Monday 21 September 2015

    Energy disaggregation for health monitoring

    José and I have been working on a project to apply NILM methods to the health monitoring domain, specifically to help monitor the activity of elderly people living independently in their own homes. The approach we pursued aims to disaggregate the kettle reliably from smart meter data, without requiring any training in each home. We chose to use the kettle as the appliance of interest as it is an appliance common to almost all UK homes, is used regularly as part of most elderly people’s daily routine, and also has a signature which varies little between houses.

    The core novelty of the our work is that deviations in kettle usage from the normal routine can be recognised from the disaggregated smart meter data, allowing interventions in households to occur as soon as possible. Crucially, such routines are learned individually for each household, rather than using a static routine for all households. As such, kettle usage is used as a proxy for health, since no additional sensors were installed to directly measure health parameters (e.g. heart rate).

    An example of a routine for a single household is shown below. The top graph shows the probability that the kettle would be used at least once within a half hour interval, while the bottom graph shows the cumulative probability that the kettle would have been used at least once by that time of the day. It can be seen that for this household, a highly repeatable routine is followed, where the kettle is used by midday on about 80% of days.

    Full details can be found in our paper to be presented at BuildSys later this year:

    José Alcalá, Oliver Parson, Alex Rogers. Detecting Anomalies in Activities of Daily Living of Elderly Residents via Energy Disaggregation and Cox Processes. In: 2nd ACM International Conference on Embedded Systems For Energy-Efficient Built Environments (BuildSys), Seoul, South Korea. 2015.

    Tuesday 8 September 2015

    Dataport data released in NILMTK format

    The Dataport database is the world's largest source of disaggregated customer energy data. The database contains electricity data collected from 722 houses in the US; 631 in Texas, 49 in Colorado and 42 in California. The houses monitored include 501 single-family homes, 183 apartments, 35 town homes and 3 mobile homes. Access to the portal is free for members of universities, while commercial access is limited to members of Pecan Street's Industry Advisory Council.

    To date, the Dataport data has been available via direct access to the database. While this provides an efficient means of querying a small amount of data, large amounts of data can take a long amount of time to download since the data is transferred in an uncompressed format. Furthermore, as with most other data sets, the data set is described in a custom format, requiring researchers to parse the data and metadata before making use of the data set.

    For these reasons, we've been working with Pecan Street Inc to release a subset of the Dataport database in NILMTK HDF5 format. The HDF5 file is 1.09 GB in size, and contains one month of data from 669 of the Dataport houses, which were selected as they contain at least 8 meters. In each house, the circuit name has been converted from the Dataport names to the NILM Metadata controlled vocabulary. The produced dataset can be easily analysed using tools described in the NILMTK documentation. The HDF5 file is available via the Dataport portal under the same access control as the Dataport database.

    Below is a boxplot showing the proportion of energy consumed by each circuit category across all households in the HDF5 data.

    The data set is described in more detail in the following paper to be presented at the GlobalSIP Smart Buildings workshop:

    Oliver Parson, Grant Fisher, April Hersey, Nipun Batra, Jack Kelly, Amarjeet Singh, William Knottenbelt, Alex Rogers. Dataport and NILMTK: A Building Data Set Designed for Non-intrusive Load Monitoring. In: 1st International Symposium on Signal Processing Applications in Smart Buildings at 3rd IEEE Global Conference on Signal & Information Processing, Orlando, FL, USA, 14-16 December, 2015.

    Thursday 13 August 2015

    Reusable hold out test sets for NILM

    Overfitting is a well-cited problem in the field of Non-intrusive Load Monitoring. Overfitting refers to the high accuracy of an algorithm on one small data set, while the same algorithm generalises poorly to other data sets. In NILM, this often corresponds to algorithms which work well on data covering a short period of time or a small number of houses, but performs poorly on data covering longer time periods or other houses.

    One solution to this problem is through the use of a competition, in which the organiser releases part of the data set for training, while holding out the remaining data for testing. This approach works fine for one-off competitions where each participant can only submit one solution, but it weakens when participants are allowed to submit multiple entries. The reason for this is that participants can use information learned from their accuracy score on the test set to inform their algorithm choice.

    Belkin organised exactly this type of competition for NILM via the Kaggle platform a few years ago. The competition allowed two entries per day per participant, each of were evaluated using half of a private hold out data set and displayed on a public leaderboard. However, the competition ended after 4 months, at which point the final standings were determined by each solution's performance on the other half of the private hold out data set which had never previously been released. Through such a format, the final standings can only be calculated once and the competition cannot be re-run, as the final standings convey information about the private hold out data set which could inform the design of future algorithms.

    A recent Google Research blog post describes this exact problem in a much more general sense than NILM. Most interestingly, their recent paper even proposes a solution to this problem through the use of a reusable holdout set. The approach is that the reusable hold out set is only accessed through a differentially private algorithm, which effectively samples the holdout set in order to produce a different sample each time it is accessed.

    At the 2015 European NILM workshop, an MSc group from Imperial supervised by Jack Kelly presented a platform which could potentially be used to host NILM competitions in the future. I’d be really interested to see whether such a platform could use such a reusable hold out set in order to allow the competition to run for much longer without compromising the results relative to the classical method of evaluation.

    Tuesday 28 July 2015

    NILM 2015 Workshop Summary

    During July 2015, Imperial College London played host to nearly 70 attendees from all over the world for the European Non-Intrusive Load Monitoring (NILM) workshop, bringing together energy disaggregation researchers and professionals for this 2-day event.

    The purpose of the workshop was to provide a forum for energy disaggregation enthusiasts to learn about recent developments in the field, as well as network and discuss projects for future collaboration. The workshop was attended by academics, employees of energy disaggregation companies, multinational utility companies and a few hobbyists.

    Prof Mario Bergés, Assistant Professor at Carnegie Mellon University gave the keynote speech which focussed on the relevance of NILM within the emerging domain of the Internet of Things (IoT). Mario covered recent trends in energy disaggregation, as well as his projection of the field’s relevance into the future. His talk also proposed four ‘million dollar’ ideas which he believes will have significant impact on the domain of energy disaggregation. You can watch Mario’s full talk below.

    Workshop attendees also enjoyed talks from both academic and industry aspects, with speakers including Mingjun Zhong from the University of Edinburgh and Stephen Makonin from Simon Fraser University representing academic findings, and focussed on models for energy disaggregation, socioeconomic concerns and accuracy evaluation. From an industry perspective the workshop welcomed Bruno Charbonnier from EDF R&D, and Hjalmar Nilsonne from Watty who cemented the importance and benefits of disaggregating electricity and announced the release of a new dataset.

    Delegates were invited to bring a poster to present at a dedicated ‘lightning talk’ session, giving each presenter a chance to disseminate and discuss a NILM related topic of their choice for 1 minute. During the lunch and break sessions, posters were displayed on the walls, giving the presenter an opportunity to engage in one-to-one discussions with other attendees. The posters are available via a dropbox folder, while the lightning talk session can be watched below.

    An MSc group from Imperial College London presented a tool for evaluating NILM algorithms without requiring the NILM algorithm’s code to be released. There was a real buzz of excitement around such an initiative, and a number of improvements were suggested around the need for a real-world private data set.

    As the NILM Workshop came to a close, an agenda was decided for topics to discuss the following morning at an informal user group designed to encourage collaboration and potential projects among attendees, which included funding applications and data sharing. The afternoon session explored NILMTK; an open source toolkit for non-intrusive load monitoring and included an overview of the toolkit as well as discussions on how to encourage contributions from the community. The need for a collaborative knowledge base, where items such as public data sets can be described in an easily comparable and searchable format was also discussed, with the result being a web based wiki which will be available on the nilm.eu website soon.

    The most obvious learning from the workshop was the increasing momentum in this domain. The 2014 European Workshop was attended by around 20 people while this year saw nearly 70 attendees from around the globe. In addition, the diversity of the problems being studied by each of the attendees was also clear from the poster session, as each start up or academic project has a subtle but significantly different take on the problem of energy disaggregation. Lastly, the problem of evaluation accuracy cropped up regularly throughout the workshop. The need for standard data sets, metrics and methodologies is now more important than ever.

    The workshop was streamed live on YouTube, and videos of all talks can be seen via our YouTube playlist, while each presenter's slides can be downloaded from our dropbox folder.

    The two day workshop finished with a discussion of plans for the 2016 European workshop. While the location of the workshop is yet to be decided, it was clear that there was sufficient demand for a future meeting. We will soon be announcing a call for hosts, with the aim of hosting the workshop in a city with convenient international transport links with the rest of Europe.

    Monday 13 July 2015

    NILM 2015 presentation videos

    In case you missed the live stream of the Second European NILM Workshop, we've also uploaded each talk to a YouTube playlist.

    Oliver Parson, University of Southampton - Introduction

    Mario Bergés, CMU - NILM in the era of IoT

    A series of 1-minute lightning talks by each poster presenter

    Stephen Makonin, Simon Fraser University - From socioeconomic concerns to standardising accuracy to water NILM

    Mingjun Zhong, University of Edinburgh - Incorporating long-term and population-level information into Machine-Learning based NILM

    Imperial MSc Group - green gauge: Comparing Algorithms for Energy Disaggregation

    Bruno Charbonnier, EDF - Decortic: A method for detecting and estimating the consumption of electrical space heating

    Hjalmar Nilsonne - Connecting the world’s energy data

    Saturday 4 July 2015

    NILM 2015 Live Stream

    As the upcoming European NILM Workshop is now fully booked, we're also hoping to stream the presentation sessions via a Hangout On Air. The link to the Hangout On Air event is:

    The agenda for the day is as follows:

    • 10.00 Welcome and workshop overview - Oliver Parson
    • 10.30 Keynote talk - Mario Bergés
    • 11.30 Poster lightning talks - All poster presenters
    • 12.00 Lunch & poster session (not streamed)
    • 13:40 Academic talks - Mingun Zhong & Stephen Makonin
    • 14.30 NILM evaluation tool - Imperial MSc group
    • 15.00 Coffee & networking (not streamed)
    • 15.40 Industry talks - Bruno Charbonnier & Hjalmar Nilsonne
    • 16.30 Roundup and discussion for future workshops
    • 17.00 Stream ends

    Please note all times are local London time (BST = GMT+1).

    We might need to restart the Hangout if we encounter technical problems on the day, so please keep an eye on NILM_Workshop on twitter for links to new streams.

    Videos of the talks should be available on YouTube shortly after the event so long as everything goes smoothly on the day.

    We have also set up a LinkedIn group to allow people to introduce themselves, continue workshop discussions, or catch up on anything they've missed.

    Saturday 9 May 2015

    What even is supervised/unsupervised disaggregation?

    I've noticed a fair amount of disagreement regarding exactly what type of learning is being used by a specific energy disaggregation method. I think the confusion arises from a discrepancy between the definition of supervised learning in the general machine learning literature and the practical assumptions of energy disaggregation methods:

    Machine learning definition

    General purpose machine learning defines supervised learning methods as those which require labelled training data to train a model. Labelled data refers to both the input and answers to the problem, which in the case of energy disaggregation corresponds to both household aggregate and individual appliance energy consumption. Conversely, unsupervised learning refers the use of only unlabelled data (household-level) data to construct models.

    Practical energy disaggregation

    In the energy disaggregation field, a fundamental problem exists due to the variation in appliances between different houses. As a result, scalable methods must not require appliance-level data from the houses in which disaggregation is to be performed (test houses). As such, practical approaches can apply supervised learning to appliance-level data from houses other than the test house, but can only apply unsupervised learning to aggregate-level data from the test house.

    Semi-supervised learning

    General purpose machine learning defines semi-supervised learning as the combination of a small amount of labelled training data with a large amount of unlabelled training data. Although this sounds similar to the scenario described above, the crucial difference is that energy disaggregation requires that the supervised and unsupervised learning takes place on data from difference domains (buildings), while general purpose machine learning assumes both the labelled and unlabelled training data are drawn from the same domain. Furthermore, energy disaggregation training methods could even make use of a large amount of labelled training data from non-test houses, and only a small amount of unlabelled training data from the test house.


    I've been apprehensive to use the term semi-supervised learning to describe practical energy disaggregation methods due to the domain-specific requirements of the field. Instead, I generally refer to methods as unsupervised if they use appliance-level data from only non-test houses, which often leads to confusion. I'd be interested to hear other people's opinions on the matter, and hopefully we can reach some consensus!

    Friday 10 April 2015

    Announcing the 2015 European NILM workshop

    I'm really excited to announce that the Second European Workshop on Non-intrusive Load Monitoring will be held on 8th July 2015 at Imperial College London. The workshop is the follow up to last year's NILM @ London workshop, which provided the first European venue which brought together both academics and companies with an interest in energy disaggregation. Updates and registration information can be found at the new website: www.nilm.eu

    Some important information:

    • When: 8th July 2015
    • Where: Imperial College London, UK
    • Cost: Free
    • Objective: To provide a European venue for disaggregation researchers to discuss recent developments in the field and fuel future collaborations

    This workshop will have a more technical focus than the first workshop, and will feature a keynote from Mario Bergés in the morning and a technical session of invited talks in the afternoon. Furthermore, we're inviting all attendees to bring a poster on a topic of their choice, which could be a recent piece of work, their company's current direction, or even an invitation for collaboration on a joint project. Last, we're hoping to live stream the event online for anyone who can't be there in person, though this is a little experimental!

    Tuesday 31 March 2015

    EEme release disaggregation accuracy calculated by Pecan Street

    I recently came across EEme, a spin-out from Carnegie Mellon University, who apply energy disaggregation to 15-minute smart meter data to provide demand side management analytics. EEme are particularly interesting, since they’re the first company (to the best of my knowledge) to go completely public regarding the accuracy of their product, as calculated by a third party. EEme used Pecan Street’s 3rd party evaluation tool, which provided EEme with 15-minute aggregate smart meter data and weather data from hundreds of homes, and required EEme to return monthly totals for four of the largest energy consuming appliances. Since Pecan Street also measured the appliance-level energy consumption as part of their deployment, they are able to calculate the exact level of accuracy of EEme’s disaggregation. The full 7 page report is available for request from EEme’s website. Although EEme focus on lower resolution data than many other energy disaggregation companies, I’m interested to see whether this report sparks the beginning of a more public competition between energy disaggregation companies to release accuracy statistics as confirmed by a third party.

    Wednesday 25 March 2015

    Overview of the NILM field

    This post aims to use trends in recent publications to provide an overview of the field of non-intrusive load monitoring / energy disaggregation.

    Recent Growth

    Researchers often refer to a recent explosion in the number of NILM publications. The graph below shows the number of papers published per year, from which the upward trend since 2010 is clearly visible. This renewed interest is likely due to recent country-wide roll outs of smart meters.

    Significant Publications

    In such a rapidly growing field, it's often hard to understand which publications are the most significant. The graph below shows the number of citations of the most cited papers. Unsurprisingly, Hart's 1992 seminal paper is the most cited by far, with a number of other papers from the 90s also appearing high up the list.

    Since older papers have had more time to accumulate citations, it's also interesting to look at citations per year to get a better idea of recent trends in the field, as shown by the graph below. Unlike before, there is no stand-out paper, with recent review papers and data set papers receiving the greatest citation velocity. Besides these papers, a number of the remaining highly cited papers propose techniques based upon principled machine learning models.


    I am often asked which are the most popular venues for NILM research. The graph below shows the most popular publishers, with ieeexplore clearly publishing the most papers in this field. Unfortunately, I couldn't get hold of high quality data for conferences/journals, which I'm sure would have been useful.


    Finally, it's also interesting to analyse common keywords in existing publications. The graph below shows the most commonly occurring words in paper titles, with words such as 'a' and 'the' omitted. Besides the obvious terms such as 'nonintrusive', 'appliance', 'load', 'monitoring', 'energy' and 'disaggregation', other interesting terms pop up, such as 'smart', 'identification', 'residential' and 'home'.

    Update 12.09.2016: cleaned up graph appearance.
    Update 12.09.2016: added data and notebook used to generate these graphs to the nilm-papers github repository.

    Sunday 1 March 2015

    GridCarbon app updated to Android 5

    Yesterday I pushed v2.1 of the GridCarbon app to the Google Play store. GridCarbon is an Android and iOS app that lets you track the carbon intensity of the UK electricity grid on your smartphone or tablet.

    This latest release includes the following changes:

    • Updated to Android 5 material design
    • Improved robustness when generation mix data received from server contains missing or repeated elements
    • New about page
    The app also allows you track carbon intensity and generation sources over the past 24 hours via the landscape view.

    If you've you have any comments, I'd love to hear them!

    Monday 23 February 2015

    Collaboration with José Alcalá on using NILM for health monitoring

    José has recently been visiting our lab in Southampton as part his PhD programme at the University of Alcalá. He's interested in how energy disaggregation can be applied within the healthcare domain, specifically to support elderly people living independently in their own homes. I've really enjoyed the collaboration so far, with José making great use of the data sets we've collected over the past few years. Despite only arriving in September last year, we've achieved a lot and recently co-authored a paper together. I hope José has found the visit useful, with him gaining experience from other members of our lab way beyond the domain of energy disaggregation, in fields such as Gaussian processes.

    If you're also studying towards a PhD within the energy disaggregation domain and are interested in visiting our lab as part of your programme, please get in touch!

    Friday 13 February 2015

    British Gas to acquire AlertMe

    Today, both British Gas and AlertMe pushed out press releases announcing the acquisition of AlertMe by British Gas, for an estimated total of £65m. As a fresh member of British Gas' connected homes team and a long-term customer of AlertMe, I was really excited to hear the news and am eager to be part of the team to build the next generation of domestic energy feedback products. This move makes British Gas' position clear within the Internet of things / connected homes market, and will undoubtedly have a big impact on the field on energy disaggregation in the UK.

    Thursday 29 January 2015

    NILM 2015 @ London discussion

    Jack Kelly and I are thinking about organising another NILM meet up in London this summer, and would love to get your thoughts on what you would find most useful. Jack has started an Energy Disaggregation Discussion Group which seems like a great place to throw some ideas around. I'd encourage everyone to head over to the forum and add your voice to the discussion! And don't forget to subscribe to the group so you receive updates!

    Tuesday 27 January 2015

    DECC Workshop: Specifying and Costing Monitoring Equipment for a Longitudinal Energy Study

    Yesterday I attended a DECC workshop aiming to specify and estimate the costs of monitoring equipment for a longitudinal energy study. The scale of the study would be to use questionnaires and monitoring equipment to study energy use in 10,000s of homes. However, only £500 per household could be spent on hardware at such scale. It was concluded that this budget would not go very far beyond aggregate gas, electricity and water monitoring equipment.

    In contrast to this, the LUKES project proposes aggregate monitoring of 10,000s of homes, while extensively monitoring up to 400 homes for up to 4 years. Although not the primary aim of the study, such a data set would have considerable impact for the energy disaggregation community. I was keen to point out that such uses have arisen due to the Household Energy Study, and it is important to take this data set as a case study when designing new surveys.

    However, it is also important that lessons are learned from HES, such that the same mistakes are not repeated. In particular, I’d hope that a new data set would:

    • Collect both aggregate-level and circuit-level data as well as appliance level data
    • Specify and maintain each household’s metering hierarchy and appliance names using a consistent metadata schema, such as the NILM Metadata project
    • Collect aggregate data at a higher resolution than 2 minute energy readings. Ideally, I believe 1 second power data would be best trade-off between cost and frequency

    Friday 16 January 2015

    Joining the data science team at British Gas Connected Homes

    I joined the data science team at British Gas Connected Homes at the start of this year, who focus on extracting meaningful insight from smart meter data to help their customers better understand their energy consumption. The plan is to split my time roughly 50/50 between my current research fellowship at the University of Southampton and my new role with British Gas. The good news for you is that this means I'll continue to maintain this blog for at least the next year. This partnership with British Gas is particularly natural given my PhD research in energy disaggregation, and I am looking forward to the new challenges this role will bring. Below is a photo of one of Connected Homes' London offices, which is probably not what you might expect from the UK's largest gas and electricity supplier!

    Monday 5 January 2015

    JAIR paper published: A Hidden Markov Model-Based Acoustic Cicada Detector for Crowdsourced Smartphone Biodiversity Monitoring

    We recently had a paper published in the Journal for Artificial Intelligence Research based on our work searching for the New Forest Cicada with the aid of a smartphone app.

    The full abstract is:

    In recent years, the field of computational sustainability has striven to apply artificial intelligence techniques to solve ecological and environmental problems. In ecology, a key issue for the safeguarding of our planet is the monitoring of biodiversity. Automated acoustic recognition of species aims to provide a cost-effective method for biodiversity monitoring. This is particularly appealing for detecting endangered animals with a distinctive call, such as the New Forest cicada. To this end, we pursue a crowdsourcing approach, whereby the millions of visitors to the New Forest, where this insect was historically found, will help to monitor its presence by means of a smartphone app that can detect its mating call. Existing research in the field of acoustic insect detection has typically focused upon the classification of recordings collected from fixed field microphones. Such approaches segment a lengthy audio recording into individual segments of insect activity, which are independently classified using cepstral coefficients extracted from the recording as features. This paper reports on a contrasting approach, whereby we use crowdsourcing to collect recordings via a smartphone app, and present an immediate feedback to the users as to whether an insect has been found. Our classification approach does not remove silent parts of the recording via segmentation, but instead uses the temporal patterns throughout each recording to classify the insects present. We show that our approach can successfully discriminate between the call of the New Forest cicada and similar insects found in the New Forest, and is robust to common types of environment noise. A large scale trial deployment of our smartphone app collected over 6000 reports of insect activity from over 1000 users. Despite the cicada not having been rediscovered in the New Forest, the effectiveness of this approach was confirmed for both the detection algorithm, which successfully identified the same cicada through the app in countries where the same species is still present, and of the crowdsourcing methodology, which collected a vast number of recordings and involved thousands of contributors.

    and the full reference is:

    D. Zilli, O. Parson, G. V. Merrett and A. Rogers (2014) "A Hidden Markov Model-Based Acoustic Cicada Detector for Crowdsourced Smartphone Biodiversity Monitoring", Volume 51, pages 805-827