Paper accepted at NILM 2014

My paper titled 'A Scalable Non-intrusive Load Monitoring System for Fridge-Freezer Energy Efficiency Estimation' was recently accepted for presentation at NILM 2014. The paper gives an overview of chapter 6 of my thesis, which describes a case study deployment of the theory presented in my thesis applied to the disaggregation of fridge/freezers in 117 households in the UK. This involved the collection of a huge amount of aggregate electricity data, for which we used AlertMe current clamps as shown below:

AlertMe current clamp

Here is the full abstract of the paper:

In this paper we propose an approach by which the energy efficiency of individual appliances can be estimated from an aggregate load. To date, energy disaggregation research has presented results for small data sets of 7 households or less, and as a result the generality of results are often unknown. In contrast, we have deployed household electricity sensors to 117 households and evaluated the accuracy by which our approach can identify the energy efficiency of refrigerators and freezers from an aggregate load. Crucially, our approach does not require training data to be collected by sub-metering individual appliances, nor does it assume any knowledge of the appliances present in the household. Instead, our approach uses prior models of general appliance types that are used to first identify which households contain either a combined fridge-freezer or separate refrigerator and freezer, and subsequently to estimate the energy efficiency of such appliances. Finally, we calculate the time until the energy savings of replacing such appliances have offset the cost of the replacement appliance, which we show can be as low as 2.5 years.

Introducing NILMTK: an open source toolkit for non-intrusive load monitoring

Today, Nipun Batra, Jack Kelly and Oliver Parson are really pleased to announce the release of NILMTK: an open source toolkit for non-intrusive load monitoring. The toolkit will allow researchers to easily develop algorithms which disaggregate a household’s total electricity consumption into individual appliances.

Specifically, the toolkit includes:

• a number of parsers to read public data sets into a common format
• a suite of statistical functions to analyse such data sets and identify potential problems
• two benchmark energy disaggregation algorithms
• a suite of evaluation metrics to compare disaggregation algorithms

Further details can be found in the accompanying paper recently accepted at e-Energy 2014 available via arXiv and Soton ePrints:

• Batra, N., Kelly, J., Parson, O., Dutta, H., Knottenbelt, W., Rogers, A., Singh, A., Srivastava, M. (2014). NILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring. In Fifth International Conference on Future Energy Systems (ACM e-Energy). Cambridge, UK. arXiv:1404.3878

This release is hopefully just the beginning of the toolkit’s contribution to energy disaggregation, and as such we welcome feedback and contributions to all aspects of the project.

This has been cross posted via Nipun Batra’s blog, Jack Kelly’s blog and the ORCHID project blog.

Thesis available online

Today I can finally say that the finished version of my thesis has been submitted and is now available online. Here's the full reference:

Parson, Oliver (2014) Unsupervised Training Methods for Non-intrusive Appliance Load Monitoring from Smart Meter Data. University of Southampton, Electronics and Computer Science, Doctoral Thesis.

Here's a Wordle of my thesis:

And also here's the abstract:

Non-intrusive appliance load monitoring (NIALM) is the process of disaggregating a household’s total electricity consumption into its contributing appliances. Smart meters are currently being deployed on national scales, providing a platform to collect aggregate household electricity consumption data. Existing approaches to NIALM require a manual training phase in which either sub-metered appliance data is collected or appliance usage is manually labelled. This training data is used to build models of the house- hold appliances, which are subsequently used to disaggregate the household’s electricity data. Due to the requirement of such a training phase, existing approaches do not scale automatically to the national scales of smart meter data currently being collected.

In this thesis we propose an unsupervised training method which, unlike existing approaches, does not require a manual training phase. Instead, our approach combines general appliance knowledge with just aggregate smart meter data from the household to perform disaggregation. To do so, we address the following three problems: (i) how to generalise the behaviour of multiple appliances of the same type, (ii) how to tune general knowledge of appliances to the specific appliances within a single household using only smart meter data, and (iii) how to provide actionable energy saving advice based on the tuned appliance knowledge.

First, we propose an approach to the appliance generalisation problem, which uses the Tracebase data set to build probabilistic models of household appliances. We take a Bayesian approach to modelling appliances using hidden Markov models, and empirically evaluate the extent to which they generalise to previously unseen appliances through cross validation. We show that learning using multiple appliances vastly outperforms learning from a single appliance by 61–99% when attempting to generalise to a previously unseen appliance, and furthermore that such general models can be learned from only 2–6 appliances.

Second, we propose an unsupervised solution to the model tuning problem, which uses only smart meter data to learn the behaviour of the specific appliances in a given house-hold. Our approach uses general appliance models to extract appliance signatures from a household’s smart meter data, which are then used to refine the general appliance models. We evaluate the benefit of this process using the Reference Energy Disaggregation Data set, and show that the tuned appliance models more accurately represent the energy consumption behaviour of a given household’s appliances compared to when general appliance models are used, and furthermore that such general models can per- form comparably to when sub-metered data is used for model training. We also show that our tuning approach outperforms the current state of the art, which uses a factorial hidden Markov model to tune the general appliance models.

Third, we apply both of these approaches to infer the energy efficiency of refrigerators and freezers in a data set of 117 households. We evaluate the accuracy of our approach, and show that it is able to successfully infer the energy efficiency of combined fridge freezers. We then propose an extension to our model tuning process using factorial hidden semi-Markov models to model households with a separate fridge and freezer. Finally, we show that through this extension our approach is able to simultaneously tune the appliance models of both appliances.

The above contributions provide a solution which satisfies the requirements of a NIALM training method which is both unsupervised (no manual interaction required during training) and uses only smart meter data (no installation of additional hardware is required). When combined, the contributions presented in this thesis represent an advancement in the state of the art in the field of non-intrusive appliance load monitoring, and a step towards increasing the efficiency of energy consumption within households.

International WikiEnergy Data Conference 2014

The WikiEnergy Data Conference has just been announced, and will be co-hosted by Carnegie Mellon University and Pecan Street Research Institute. The conference will feature presentations from computer science, public policy, and engineering graduate students selected as finalists for the Pike Powers energy research fellowship.

Some important information:

• When: 4-5 June 2014
• Austin, TX, USA
• Objective: To convene the WikiEnergy researcher community, highlighting the results of research conducted by graduate students selected as finalists for the Pike Powers Energy Research Fellowship.

The first day of the workshop will be dedicated to the Pike Powers Fellowships, while the second day is scheduled as an industry day. Attendance is free, although attendees should  register via the conference website. The workshop will also be co-located with the NILM 2014 workshop on 3 June.