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Smart Meter Wiki: A curated collection of software libraries and resources for analyzing smart meter data.

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Smart Meter Wiki

There are now over 5.8 million smart meters operating across homes and businesses in the UK, and this number is expected to rise to over 50 million by 2020, driven by the Smart Meter Implementation Programme. This programme will provide high-resolution electricity and gas consumption data across millions of household across the UK, offering enormous potential for energy research and data-driven policymaking. However, the sheer volume of the data (e.g. > 20GB) means that even basic exploratory analysis is difficult using traditional desktop-driven methods of analysis. The aim of this page is to provide a curated list of useful software libraries and resources for analyzing smart meter data at scale. This list may also be useful for analyzing any regularly-spaced time series data, e.g. half-hourly weather data, monthly unemployment data, etc.

Inspired by awesome-python and awesome-R.

Data

The table below lists some existing UK smart meter data sets available for download on the web.

Name Period No. of Households No. of Obs. Sample Freq. Data Recorded Obs. Type Data Format Data License Meter Type
Energy Demand Research Project 2007-2010 14,000 400 million 30 mins Electricity, Gas Aggregate csv EULA SMETS 1
Low Carbon London Project 2013-2014 5,536 100 million 30 mins Electricity Aggregate csv Open SMETS 1
REFIT Electrical Load Measurements 2015-2017 20 1 billion 8 seconds Electricity Aggregate, Appliance csv Open Own device
UK Domestic Appliance-Level Electricity 2015-2017 5 1 billion 6-8 seconds Electricity Aggregate, Appliance csv, FLAC Open Own device
  • EULA (End User License Agreement): Sign up and register with the UK Data Service to download.
  • SMETS 1 (Smart Meter Equipment Technical Standards): UK government standard for smart metering equpiment.

Some other good resources include:

A number of small samples of smart meter datasets can be found in the data directory. These have been converted into feather format for speed and seamless interoperability between R and other programming languages (e.g. Python).

Software

A list of popular R libraries for analyzing smart meter data is provided below. A hands-on tutorial can be found for each library in the examples directory as an R notebook. This is to ensure that the code provided is both readable and reproducible. We follow Google's R Style Guide for R code for consistency and maintainability, with the exception of using = instead of <- for variable assignment. Whilst this is largely a matter of preference, = does allow for greater compatibility with other mainstream programming languages such as C, C++, Java, etc, and generally results in less obscure bugs in code as it disallows assignment within control structures (e.g. if()).

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