NILM Metadata is a framework for modelling appliances, meters, measurements, buildings and datasets. Default properties for appliances and meters are also provided.
NILM Metadata aims to provide:
- domestic appliances
- a controlled vocabulary for appliance names and categories
- a list of time periods when each appliance was active
- each appliance can contain any number of components (e.g. a light fitting can contain multiple lamps and a dimmer)
- prior knowledge about the distribution of variables such as:
- on power
- on duration
- usage in terms of hour per day
- appliance correlations (e.g. that the TV is usually on if the games console is on)
- electricity meters (whole-home and individual appliance meters)
- a controlled vocabulary for measurements
- wiring hierarchy of meters
- a mapping of which appliances are connected to which meters
- standard properties and priors for each appliance
- valid additional properties for each appliance
NILM Metadata uses JSON Schema to define
the syntactic elements of the schema and then uses a simple but
powerful inheritance mechanism to allow 'objects' (e.g. a
specification of an appliance) to inherit from an existing object.
For example, laptop computer is a specialisation of computer
and the two share several properties (e.g. both are in the ICT
category). So laptop computer inherits from computer and modifies
and adds any properties it needs. In this way, we can embrace the
"don't repeat yourself (DRY)"
principal by exploiting the relationship between appliances.
The metadata itself can be either YAML or JSON.
The aim is that NILM Metadata can be used as a stand-alone project to specify the metadata for any NILM dataset; and that metadata can then be used with the open-source energy disaggregation and analytics framework NILMTK.
Please jump in and add to or modify the schema and database of objects!
The following paper describes NILM metadata in detail:
- Jack Kelly and William Knottenbelt. Metadata for Energy Disaggregation. arXiv:1403.5946 [cs.DB] March 2014
Please cite this paper if you use NILM metadata.
To demonstrate the inheritance system, let's look at specifying a boiler.
First, NILM Metadata specifies a heating appliance object, which is
can be considered the "base class":
heating appliance:
parent: appliance
categories:
traditional: heating
size: largeNext, we specify a boiler object, which inherits from heating appliance:
#------------- BOILERS ------------------------
boiler: # all boilers except for electric boilers
parent: heating appliance
synonyms: [furnace]
# Categories of the child object are appended
# to existing categories in the parent.
categories:
google_shopping:
- climate control
- furnaces and boilers
# Here we specify that boilers have a component
# which is itself an object whose parent
# is `water pump`.
components:
- parent: water pump
# Boilers have a property which most other appliances
# do not have: a fuel source. We specify additional
# properties using the JSON Schema syntax.
additional_properties:
fuel:
enum: [natural gas, coal, wood, oil, LPG]
subtypes:
- combi
- regular
# We can specify the different mechanisms that
# control the boiler. This is useful, for example,
# if we want to find all appliances which
# must be manually controlled (e.g. toasters)
control: [manual, timer, thermostat]
# We can also declare prior knowledge about boilers.
# For example, we know that boilers tend to be in
# bathrooms, utility rooms or kitchens
distributions:
room:
distribution_of_data:
categories: [bathroom, utility, kitchen]
values: [0.3, 0.2, 0.2]
# If the values do not add to 1 then the assumption
# is that the remaining probability mass is distributed equally to
# all other rooms.
source: subjective # These values are basically guesses!Next, we keep a catalogue of specific makes and models of appliances
in NILM Metadata. Here's a specific boiler (which inherits from boiler):
Worcester~Greenstar 30CDi Conventional natural gas:
manufacturer: Worcester
model: Greenstar 30CDi Conventional natural gas
parent: boiler
fuel: natural gas
subtype: regular
part_number: 41-311-71
efficiency_rating:
certification_name: SEDBUK
rating: A
nominal_consumption:
on_power: 70
distributions:
on_power:
- model:
distribution_name: normal
mu: 73
sigma: 12Finally, in the 'minimal' version of the metadata for the UKPD dataset, we just need to specify the following:
parent: Worcester~Greenstar 30CDi Conventional natural gas
room:
name: bathroom
instance: 1
year_of_purchase: 2011
This is combined with information from its ancestors and the result is shown below).
Below is a snippet of the metadata for the UKPD dataset. The snippet first describes the dataset itself, then describes the first building and describes the boiler and one meter in the building.
name: UKPD
full_name: UK Power Dataset
institution: Imperial College London
contact: [email protected]
description: Recording from 4 domestic homes in or near to London, UK.
number_of_buildings: 4
timezone: Europe/London
urls: ['http://www.doc.ic.ac.uk/~dk3810/data']
buildings:
1:
geo_location:
city: London
country: UK
latitude: 51.464462
longitude: -0.076544
timezone: Europe/London
timeframe:
start: 2012-11-09
end: 2014-03-11
utilities:
electric:
mains_voltage:
nominal: 230
tolerance_lower_bound: 10
tolerance_upper_bound: 6
appliances:
- name: boiler
instance: 1
model: Greenstar 30CDi Conventional natural gas
manufacturer: Worcester
fuel: natural gas
subtype: regular
meter_ids: [2]
nominal_consumption:
on_power: 70 # watts
part_number: 41-311-71
room:
name: bathroom
instance: 1
year_of_purchase: 2011
control: [manual, timer, thermostat]
efficiency_rating:
certification_name: SEDBUK
rating: A
categories:
size: large
traditional: heating
google_shopping:
- climate control
- furnaces and boilers
electrical:
- single-phase induction motor
components:
- name: water pump
parent: water pump
categories:
electrical: [single-phase induction motor]
distributions:
room:
- distribution_of_data:
categories: [bathroom, kitchen, utility]
values: [0.3, 0.2]
source: subjective
on_power:
- model:
distribution_name: normal
mu: 73 # watts
sigma: 12
meters:
- model: EcoManager Whole House Transmitter
manufacturer: Current Cost / Sailwider
id: 1
sample_period: 6
max_sample_period: 120
measurements:
- physical_quantity: power
type: apparent
cummulative: false
minimum: 0
maximum: 25000
dates_active:
- start: 2012-11-09
end: 2014-03-11
model_url: https://shop.edfenergy.com/Item.aspx?id=547
site_meter: true
wireless: trueThe current release is version 0.1.0. There are definitely plenty of improvements that can be made so please submit pull requests for the dev version!
sudo python setup.py install
Or, if you want to develop:
sudo python setup.py install
Say you have written a new metadata file called my_metadata.yaml and
you want to concatenate (merge) your data with the data in
NILM_metadata and then validate the resulting metadata.
$ cd scripts
$ ./process.py my_metadata.yaml -o my_metadata_concatenated.yaml
Loading my_metadata.yaml ... done loading.
Concatenating... done concatenating.
Dumping output to my_metadata_concatenated.yaml ... Done dumping output.
Validating... done validation! It passed!
- Project Haystack, to quote their
website, "is an open source initiative to develop tagging
conventions and taxonomies for building equipment and operational
data. We define standardized data models for sites, equipment, and
points related to energy, HVAC, lighting, and other environmental
systems." Haystack is an awesome project but it does not specify a
controlled vocabulary for appliances, which is the meat of the
nilm_metadataproject. Where appropriate,nilm_metadatadoes use similar properties to Haystack (e.g. the "site_meter" property is borrowed directly from Haystack). - WikiEnergy "A Universe of Energy Data, Available Around the World".
nosetests --nocapture