NILMTK An Open Source Toolkit for Non-intrusive Load Monitoring - - PowerPoint PPT Presentation

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NILMTK An Open Source Toolkit for Non-intrusive Load Monitoring - - PowerPoint PPT Presentation

Nov 05, 2022 688 likes •1.28k views

1 NILMTK An Open Source Toolkit for Non-intrusive Load Monitoring NILMTK team 2 Haimonti Alex Rogers Dutta Nipun Batra Oliver Parson Amarjeet Singh Mani Srivastava William Jack Kelly Knottenbelt 3 Non-intrusive load monitoring

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NILMTK

An Open Source Toolkit for Non-intrusive Load Monitoring

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NILMTK team

Nipun Batra

Amarjeet Singh Mani Srivastava Jack Kelly William Knottenbelt Haimonti Dutta Oliver Parson Alex Rogers

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Non-intrusive load monitoring (Energy disaggregation)

“Process of estimating the energy consumed by individual appliances given just a whole-house power meter reading”

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Wait a minute! This sounds complicated Would it help?

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Jane goes to the market

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Jane spends 200 pounds on her purchases

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Jane’s husband John is worried with the expenses

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He spends some time and looks at the purchase list

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Do you think the itemized billing helped him?

NILM is the same, but for energy!

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Quiz time!

Identify this famous CS scientist

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Quiz time!

Identify this famous CS scientist

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That ain’t any great

  • scientist. That’s me on my

first birthday in 1990… This is not too far from the time when NILM was first discussed

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Giving credit where it is due

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NILM interest explosion

  • 1. National smart meter rollouts
  • 2. Reduced hardware costs
  • 3. International meetings

– NILM workshop 2012, 2014; EPRI NILM 2013

  • 4. Public datasets
  • 5. Startups

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“Data is the new oil”

  • 9 NILM datasets and counting (few not

specific to NILM)

  • Across 6 countries (India, UK, US,

Canada, EU)

  • Measure aggregate and appliance level

data

  • Across 3 colors 

– REDD – BLUED – GREEND

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The industry is interested!

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So, is everything so rosy? Not quite! Else we won’t be here

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The scientific method

“The scientific method is a body of techniques for investigating phenomena, acquiring new knowledge, or

correcting and integrating previous knowledge” as per

wiki

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3 core obstacles

preventing comparison of state-of-the-art

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  • 1. Hard to assess generality
  • Subtle differences in aims of

different data sets

  • Previous contributions evaluated only
  • n single dataset.
  • Non-trivial to set up similar

experimental conditions for direct comparison.

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  • 2. Lack of comparison against

same benchmarks

  • Newly proposed algorithms rarely

compared against same benchmarks.

  • Lack of “open source” reference

algorithms  often lead to reimplementation.

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  • 3. “Inconsistent”

disaggregation performance metrics

  • Different performance metrics proposed

in the past.

  • Different formulae for same metric, eg.

4+ versions of “energy assigned”

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What is NILMTK?

Open source NILM toolkit

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What does it do?

Enable easy comparative analysis of NILM algorithms across data sets.

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How does it do that?

Provides a pipeline from data sets to metrics to lower the entry barrier for researchers.

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NILMTK pipeline

REDD BLUED UK- DALE Statistics NILMTK- DF Training Preprocessing Model Disaggregation Metrics

Data interface

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Data Format

REDD BLUED UK- DALE Statistics NILMTK- DF Training Preprocessing Model Disaggregation Metrics

Data interface

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Data Format

  • We propose NILMTK-DF: a common data

format.

  • Provide importers for 6 datasets: REDD,

SMART*, Pecan street, iAWE, AMPds, UK-DALE

  • Both flat file and efficient binary storage

format

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The fun of data!

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Standardizing nomenclature

Fridge Refrigerator FGE 29

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Metadata

  • Geographic coordinates
  • Type of appliance- hot, cold, dry?
  • Metering hierarchy
  • Parameters measured

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Standard nomenclature + Metadata + Datasets =

Comparing power draw of washing machines across US (REDD) and UK (UK-DALE)

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Standard nomenclature + Metadata + Datasets =

Top 5 appliance according to energy consumption across geographies

32 US UK INDIA

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NILMTK pipeline

REDD BLUED UK- DALE Statistics NILMTK- DF Training Preprocessing Model Disaggregation Metrics

Data interface

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Statistics

10 20 30 40 50 60 70 80 90 100 REDD Smart* Pecan AMPds iAWE UK_DALE

% energy submetered

  • Energy submetered: Sum of energy of all

appliance/Energy at mains level

  • More energy submetered  More ground truth

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Statistics

  • Appliance usage patterns
  • Correlations with weather
  • Appliance power demands

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Diagnostics

  • Every data set has problems  NILMTK provides

diagnostic functions for common problems.

  • %Lost samples (per interval and whole), uptime

% lost samples in house 1 of REDD dataset

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Preprocessing

REDD BLUED UK- DALE Statistics NILMTK- DF Training Preprocessing Model Disaggregation Metrics

Data interface

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Preprocessing

  • Correct common problems (as per

diagnosis).

  • Other standard NILM preprocessors:

– Interpolating, filtering implausible – Downsample to lower frequency – Select Top-k-appliances by energy consumption

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Heart of NILMTK

REDD BLUED UK- DALE Statistics NILMTK- DF Training Preprocessing Model Disaggregation Metrics

Data interface

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Training

  • NILMTK provides two benchmark

algorithms

–Combinatorial optimization (CO) [Proposed by Hart] –Factorial hidden Markov model (FHMM) [More recent, more complex]

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Model

  • Beyond the usual train and disaggregate,

NILMTK allows importing and exporting learnt models

  • Allows NILM to be deployed in “real world

settings”

  • Action speaks louder than words!! Demo

follows!

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Disaggregate!

  • Quite a bit of work before we

disaggregate

  • We performed

– CO and FHMM based disaggregation across first home of each dataset – Detailed disaggregation analysis across the home in iAWE (dataset from India)

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Disaggregation across multiple datasets

  • CO as good as FHMM across iAWE,

UKPD, Pecan datasets

–Space heating contributes 60% in Pecan and 35% in iAWE. Both approaches able to detect with fair ease

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And I thought that CO was really

  • utdated…
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Disaggregation across multiple datasets

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  • FHMM outperforms CO across REDD,

Smart*, AMPds

  • This is expected as FHMM models time

variations.

  • CO exponentially quicker than FHMM
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Detailed disaggregation in iAWE dataset (India)

  • CO and FHMM perform similar
  • Appliances such as air conditioners

way easier to disaggregate

  • Complex appliances (laptops and

washing machines) – not so good 

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NILMTK pipeline

REDD BLUED UK- DALE Statistics NILMTK- DF Training Preprocessing Model Disaggregation Metrics

Data interface

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Metrics

  • NILMTK provides:

–General machine learning metrics

  • Precision, Recall, F-score

–Specialized metrics for NILM

  • Error in total energy assigned, RMS error in

assigned power,..

–Both event based and total power based NILM metrics.

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Demo time!!

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Conclusions

Three core challenges in NILM research

  • 1. Hard to address generality
  • 2. Lack of comparison against same benchmarks
  • 3. Inconsistent disaggregation performance metrics

How NILMTK addresses these challenges

  • 1. Standard input and output formats (Addresses #1)
  • 2. Parsers for 6 NILM data sets (Addresses #1, #2)
  • 3. Two benchmark NILM algorithms (Addresses #1, #2)
  • 4. Statistics, diagnostics and preprocessing (Addresses #1,

#2)

  • 5. Metrics for different NILM use cases (Addresses #1)

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Backup

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Combinatorial optimization

  • Seeks to find the optimal combination of

appliances’ power draw to minimize residual energy.

  • Similar to subset-sum problem and thus

NP-complete 

  • Power draw is not related in time

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Combinatorial optimization

Appliance Off power On power Air conditioner (AC) 2000 Refrigerator 200

If total power observed = 210  AC is OFF and Refrigerator is ON

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Combinatorial optimization

Appliance Off power On power Air conditioner (AC) 2000 Refrigerator 200

If total power observed = 2000  AC is ON and Refrigerator is OFF

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Combinatorial optimization

Appliance Off power On power Air conditioner (AC) 2000 Refrigerator 200

If total power observed = 2230  AC is ON and Refrigerator is ON

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FHMM

  • Each appliance modeled as HMM

– Power draw related in time If TV is on right now, likely to be on next second.

  • Exact inference scales worse than CO

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A bit of history

Seminal work on NILM done at MIT dates back to early 1980s – A good 6-7 years before I was born!

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Field progress

10 20 30 40 50 60 70 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

# Papers citing the seminal work per year

What happened here?

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