Improving the efficiency of home heating

My research group recently launched myJoulo, a project aimed at increasing awareness of how energy is being consumed in the home. The project gives away free temperature sensors, which collect data about how the temperature of your home varies relative to the temperature outside. It then asks you to upload the data, in order to provide personalised feedback detailing how you can reduce the price of your heating bill. The whole process is available for free to anyone living in the UK. For more details see the press release.

Video on unsupervised training methods for NIALM

I recently created a video describing my recent work on unsupervised training methods for non-intrusive appliance load monitoring systems. A high quality version of the video is available from the ORCHID project website, although I thought I'd also include it here for convenience:

Popular Mechanics predicts NIALM to become reality in next 110 years

A Popular Mechanics article recently compiled a list of technology predictions for the next 110 years. One of the forecasts sounded oddly familiar:

"Smart homes will itemize electric, water, and gas bills by fixture and appliance. Shwetak Patel, a 30-year-old MacArthur Fellow, is working on low-cost sensors that monitor electrical variations in power lines to detect each appliance's signature."

Good to hear people are getting excited about energy disaggregation, although personally I hope it doesn't take the full 110 years to become widely available.

New name, new domain

I recently decided my blog could do with a little refreshing to more closely reflect its content. I've therefore changed its name to Disaggregated Homes, which can now be found at blog.oliverparson.co.uk. Please update your links, although any visits to the old URL should be automatically redirected.

Opening up the “black box” of the Home

Yesterday I attended a Smart Demand seminar titled 'Opening up the “black box” of the Home', organised by Pilgrim Beart, founder of AlertMe. The purpose of the seminar was to present a range of ideas related to smart metering in the UK, and to discuss what would need to be done in order to collect a data set that accurately describes domestic electricity consumption. The seminar was well attended by UK energy monitoring companies, such as Moixa and Onzo, but also represented views from academia. I particularly enjoyed Miroslav Hamouz's talk on the abilities and limitations of disaggregation, and completely agree that it is essential to understand what disaggregation can realistically achieve using actual smart meter data.

My home energy disaggregation system

This post describes my home energy disaggregation system, using tools from AlertMe and PlotWatt. I've cross-posted it from its own page on my website, and although this post probably won't stay up to date, the page on my website should. If you're interested in setting up a similar system, feel free to leave a comment!

In order to monitor the electricity consumption of my home, I use an AlertMe SmartEnergy kit.  This consists of a battery powered SmartMeter reader and a mains powered SmartHub. The SmartMeter reader is a current clamp, which clips onto the electricity input to my home within my circuit breaker box. This clamp calculates the flow of electricity through the wire by measuring the magnetic field surrounding it, and therefore doesn't need to physically break the circuit. The current clamp sends second-by-second readings of my home's power demand to the SmartHub via a ZigBee wireless network. The hub is attached to my router via an Ethernet cable, which allows it to upload my electricity data to AlertMe's cloud storage.

My AlertMe SmartMeter reader.

My PhD has built up my interest in non-intrusive appliance monitoring; software which calculates appliance-level energy feedback using only home-level energy data. Although AlertMe don't offer such a service, this is where PlotWatt, a cloud-based software company, comes in. To make use of PlotWatt's appliance-level analysis, I needed to transfer my data from AlertMe's data cloud to PlotWatt's data cloud. To do so, I set up my Raspberry Pi to periodically download my data from AlertMe and upload it to PlotWatt. I've since open-sourced the project to allow anyone to use or modify my code. This software and PlotWatt's algorithms have allowed me to find out how much money I spend keeping each appliance running using PlotWatt's online dashboard.

My Raspberry Pi.

A breakdown of my household's monthly energy costs is available at plotwatt.com. However, even enthusiasts like myself don't check this daily. Therefore, I wanted an energy display from which I could pick up this information as I walked past. To create such a display, I set up an old monitor attached to my Raspberry Pi, which displays the dashboard from plotwatt.com.

My home energy monitoring system.

Public NIALM Reference Library

Today I decided to make my NIALM reference library public. Over the past few years I've collected about 150 references which I'd like to share with the wider NIALM community. Since I use Mendeley to manage my references, the easiest way to share it was through a public group:

Oliver Parson's NIALM library

This group will be updated as new papers are published, so feel free to join it to receive updates.

Happy reading!

UPDATE: Please feel free to follow the group, which should give you updates when I add references to the library. However, I'll just ignore any requests to join the group, which would give you write access!