2 Papers accepted at AAMAS and IUI on AgentSwitch

As part of the ORCHID project, we recently had two papers accepted at international conferences based on AgentSwitch; a domestic energy recommendation platform. AgentSwitch utilises electricity usage data collected from households over a period of time, to realise a range of smart energy-related recommendations on energy tariffs, load detection and usage shifting. I've been responsible for the load detection module of AgentSwitch over the past few months, and am looking forward to improving it over the final year of my PhD. The following two papers provide two different perspectives on the project. The first gives the algorithmic detail and accuracy evaluations of the individual system modules. The second describes a user evaluation of AgentSwitch, which reveals the strengths and weaknesses of the system as an energy-related recommender system.

• Sarvapali Ramchurn, Michael Osborne, Oliver Parson, Sasan Maleki, Talal Rahwan, Trung Dong Huynh, Steve Reece, Muddasser Alam, Joel Fischer, Greg Hines, Enrico Costanza, Luc Moreau, Tom Rodden. AgentSwitch: Towards Smart Energy Tariff Selection. In: 12th International Conference on Autonomous Agents and Multi-Agent Systems. Saint Paul, Minnesota, USA. 2013.

• Joel Fischer, Sarvapali Ramchurn, Michael Osborne, Oliver Parson, Trung Dong Huynh, Muddasser Alam, Nadia Pantidi, Stuart Moran, Kaled Bachour, Steve Reece, Enrico Costanza, Tom Rodden, Nicholas Jennings. Recommending Energy Tariffs and Load Shifting Based on Smart Household Usage Profiling. In: International Conference on Intelligent User Interfaces. Santa Monica, CA, USA. 2013.

It seems that papers follow the same rule as British buses; you wait ages for one and then two arrive at the same time.

Top papers of 2012 for Non-Intrusive Appliance Load Monitoring (NIALM)

A little over a year ago I posted a list of my top 10 papers on non-intrusive appliance load monitoring. It quickly became my blog's most popular post, and also inspired more comments than any other post. Collecting and summarising academic literature is clearly useful to the community, and as a result I decided to collect the papers published during 2012 that I found most useful. There were many more papers published than I have time to describe here, but a more comprehensive list can be found in my on-line Mendeley reference library. I hope you find this list useful, and as always feel free to leave a comment!

• Armel, K. C., Gupta, A., Shrimali, G., & Albert, A. (2013). Is disaggregation the holy grail of energy efficiency? The case of electricity. Energy Policy, 52, 213–234.
• The authors provide an argument for applying disaggregation algorithms to smart meter data. They focus on many practical problems which are often ignored in academia, such as data availability, transmission capabilities and deployment costs. The authors conclude with a set of recommendations for both disaggregation algorithms and smart meter deployments, which if followed will aid the deployment of such technology at national scales.
• Kolter, J. Z., & Jaakkola, T. (2012). Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation. International Conference on Artificial Intelligence and Statistics (pp. 1472–1482). La Palma, Canary Islands.
• This paper describes an algorithm for efficiently disaggregating appliances by modelling the problem as a factorial hidden Markov model. In such a model, sudden increases or decreases in meter measurements are used to identify appliances turning on or off. The authors extend the model to include an additional component which means that not all appliances within the household are required to be modelled. The proposed model and inference algorithm are evaluated using both simulated data and the REDD data set.
• Parson, O., Ghosh, S., Weal, M., & Rogers, A. (2012). Non-intrusive load monitoring using prior models of general appliance types. Twenty-Sixth Conference on Artificial Intelligence (AAAI-12).
• This paper contributes an unsupervised training method for NIALM systems. The approach uses prior appliance models which describe generalisable appliance behaviour (e.g. behaviour of all refrigerators), which are tuned to match a specific appliance instance (e.g. one particular refrigerator) using only aggregate data. The approach is benchmarked against two different training methods: a variant in which the prior models are not tuned, and a variant in which the prior models are tuned using sub-metered data.
• Reinhardt, A., Baumann, P., Burgstahler, D., Hollick, M., Chonov, H., Werner, M., & Steinmetz, R. (2012). On the Accuracy of Appliance Identification Based on Distributed Load Metering Data. 2nd IFIP Conference on Sustainable Internet and ICT for Sustainability (SustainIT).
• The core contribution of this paper is an approach to solving the appliance identification problem. However, I've included it here since I believe the tracebase data set released with this paper is highly relevant to the NIALM community. To the best of my knowledge, the data set contains the largest public collection of appliance power data, as described in my previous blog post. As a result, it provides the potential for households to be simulated by summing arbitrary combinations of actual appliance loads to produce artificial aggregate loads.
• Wang, Y., Hao, X., Song, L., Wu, C., Wang, Y., Hu, C., & Yu, L. (2012). Tracking states of massive electrical appliances by lightweight metering and sequence decoding. Proceedings of the Sixth International Workshop on Knowledge Discovery from Sensor Data (pp. 34–42). New York, NY, USA.
• The authors of this paper address two problems in NIALM. First, they present an algorithm to perform efficient inference in factorial hidden Markov models for appliance disaggregation by forgetting unlikely state transitions. Second, they present an approach to determine the number and positions of additional circuit-level meters so as to ensure a minimum accuracy of disaggregation. They evaluate their approaches using both simulated data and data from Stanford's Powernet data set.
• Zoha, A., Gluhak, A., Imran, M. A., & Rajasegarar, S. (2012). Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey. Sensors, 12, 16838–16866.
• This overview paper gives a description of the current state of the art in academia. In addition, a list of accuracy metrics and publicly available data sets is given. The authors highlight some limitations of the field, such as the invasiveness of manual training processes. They conclude with a set of directions to advance the field, including a suggestion to replace the manual construction of appliance databases with unsupervised training methods.

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.