Now that I’ve (finally!) submitted my PhD thesis, I tin give the axe focus on designing together with implementing a contest for unloosen energy disaggregation algorithms. EDF Energy induce got kindly given me post-doc funding from straightaway until the goal of Dec 2016 to come about the NILM competition.

The broad programme is to starting fourth dimension consult alongside the NILM community together with create a specification for the NILM contest which industrial plant for everyone. Then I programme to implement a spider web application which tin give the axe run the NILM competition.

Right now, I’m writing a survey on the blueprint of a contest for unloosen energy disaggregation algorithms. The aim of the survey is to systematically collect feedback virtually the blueprint of the competition. I programme to launch the survey soon. Prior to the launch, I’m genuinely eager to remove heed feedback on the survey itself. For example: is the survey missing whatever vital questions? Do unopen to questions non supply sufficient options? Do unopen to questions non brand sense?!

Please banknote that, prior to the launch of the survey, my aim is to acquire feedback on the blueprint of the survey itself. So delight don’t genuinely submit whatever answers yet! Feel costless to select options together with click “next” but simply delight don’t click “submit” at the goal of the survey. I’ll write unopen to other spider web log post when the survey is gear upward to bring answers.

It’s in all probability best to supply feedback virtually the survey inward world on the relevant thread on the Energy Disaggregation Google Group. If you lot desire your feedback to survive private then, past times all means, e-mail me straight at jack.kelly@imperial.ac.uk!

And delight produce become far deport on if you lot induce got feedback on whatever aspect of the proposed NILM competition.

17 February 2012 | PhD, smart meters, introducing smart meter disaggregation

This spider web log entry is purpose of a serial of posts introducing the topic of smart meter disaggregation. In previous posts we’ve looked at the wider reasons for wanting to cut back unloosen energy consumption and we’ve taken a brief await at smart meters. In the next spider web log post, I desire to innovate the concept of smart meter disaggregation, also known every bit “non-intrusive charge monitoring” or NILM for short1{#footnoteref1_thfpd27 .see-footnote}. The primary aim of smart meter disaggregation is to infer ii things from a smart meter signal: 1) which appliances are active inward the betoken together with 2) how much unloosen energy has each device consumed. This spider web log post volition summarise the arguments for disaggregation together with we’ll await at unopen to of the primary challenges.

Why mightiness disaggregated smart meter information survive useful to anyone?

Let us assume that people are motivated to ameliorate their unloosen energy management. Do they induce got a sufficiently quantitative agreement of their unloosen energy consumption to prioritise correctly?

Prior to the availability of mains unloosen energy supplies, most individuals would induce got had an intuitive, quantitative agreement of the amount of unloosen energy consumed past times the household. If the stove needed to a greater extent than fuel together with hence someone had to manually shovel company fuel into it; you lot couldn’t help but honor how much unloosen energy was beingness consumed. In this situation, most individuals would induce got an intuitive experience for how much unloosen energy it took to, say, estrus the living room for i eventide or create i meal.

recently revolutionised a number of well-studied machine learning together with betoken processing problems, such every bit icon recognition together with handwriting recognition. Furthermore, long short-term retention architectures induce got demonstrated the effectiveness of applying recurrent neural networks to fourth dimension serial problems, such as speech synthesis. In improver to the impressive functioning of these models, the elegance of learning features from information rather than paw crafting intuitive features is a highly compelling payoff over traditional methods.

In the past times year, deep learning methods induce got also started to survive applied to unloosen energy disaggregation. For example, Jack Kelly demonstrated at BuildSys 2015 how such models outperform mutual disaggregation benchmarks together with are able to generalise to previously unseen homes. In addition, Lukas Mauch presented a newspaper at GlobalSIP 2015 describing how sub-metered information tin give the axe survive used to develop networks to disaggregate unmarried appliances from a building's full load. Most recently, Pedro Paulo Marques produce Nascimento's master's thesis compared a diversity of convolutional together with recurrent neural networks across a number of appliances introduce inward the REDD information set. Each slice of enquiry demonstrates that there's existent potential to apply deep learning to the work of unloosen energy disaggregation.

However, ii critical issues yet remain. First, are the huge volumes of sub-metered information available which are required to develop such models? Second, are the computational requirements of grooming these models practical? Fortunately, grooming tin give the axe survive performed offline if exclusively full general models of appliance types are to survive learned. However, if learning is required for each private household, for certain this volition bespeak to bring identify on cloud infrastructure rather than embedded hardware. I promise we'll acquire closer to answering these questions at this year's international NILM conference inward Vancouver!

Branches of mechanical engineering: Nilm Links

NILM:

NILM 2015 - Imperial MSc Group

https://www.youtube.com/watch?v=VU_tXNnTVBQ

UCL-Energy seminar: 'Modelling Urban Energy Systems: Disaggregate activity-based models of demand'

Build Sys 2015 - Neural NILM: Deep Neural Networks Applied to Energy Disaggregation

Is deep learning the futurity of NILM?

Does disaggregated electricity feedback cut back domestic electricity consumption? H5N1 systematic review of the literature

Smart Meter Disaggregation

H5N1 contest for unloosen energy disaggregation algorithms

Why mightiness disaggregated smart meter information survive useful to anyone?

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