Top 10 papers for Non-Intrusive Appliance Load Monitoring (NIALM) - updated for 2011

Update: Top papers of 2012

About a year ago I posted a short list of some of the most useful papers for NIALM. Since then I have learnt a lot, found much more material and read many newly published papers. This post is intended as an update to the original list, containing what I now believe to be the most relevant and useful academic papers. Below is my top 10 papers for NIALM (in alphabetical order). Any comments and discussions are welcome!

• Berges ME, Goldman E, Matthews HS, Soibelman L. Enhancing Electricity Audits in Residential Buildings with Nonintrusive Load Monitoring. Journal of Industrial Ecology. 2010;14(5):844-858.
• This article provides an up-to-date overview of the practicalities of NIALM. Since many approaches require a specific monitoring device, it is useful to understand the range of hardware available and their associated costs. The authors relate the capabilities of such hardware to the potential for NIALM, including an overview of general appliance behaviour and their power demands.
• Chang H-H, Lin C-L, Lee J-K. Load identification in nonintrusive load monitoring using steady-state and turn-on transient energy algorithms. In: International Conference on Computer Supported Cooperative Work in Design.; 2010:27-32.
• This paper combines steady-state and transient-state NIALM approaches with strong classification methods from the field of machine learning. The authors use a trained neural network as the disaggregation decision engine based on the extracted features. It is clear from the paper that the use of transient energy can project previously indistinguishable inputs into unique feature space.
• Hart GW. Nonintrusive appliance load monitoring. Proceedings of the IEEE. 1992;80(12):1870-1891.
• This is the seminal work by Hart which defines the field of NIALM. The paper provides insight into the aims and applications of such technology, in addition to summarising both his own and other related work in the late 80s and early 90s. Hart explains the intuition behind many approaches to NIALM, including his 'total load model' (also known as integer programming or combinatorial optimisation) and 'appliance model' (which has since been extended using HMMs). Finally, the paper gives an unbiased evaluation of the realistic potential and limitations for the field.
• Kim H, Marwah M, Arlitt MF, Lyon G, Han J. Unsupervised Disaggregation of Low Frequency Power Measurements. In: The 11th SIAM International Conference on Data Mining. Mesa, Arizona; 2011:747-758.
• This paper describes how Factorial HMMs (FHMM) from the machine learning domain can be used for NIALM. The authors cover the tractability of both training and inference over such models. Two extensions of the basic FHMM are described and evaluated; the use of additional data sources (e.g. time of day) and the dependencies between appliances (PC and monitor).
• Kolter JZ, Johnson MJ. REDD : A Public Data Set for Energy Disaggregation Research. In: Workshop on Data Mining Applications in Sustainability (SIGKDD). San Diego, CA; 2011:1-6.
• This paper describes a recently published public data set for evaluating NIALM methods. This tackles the issue that, generally, energy disaggregation approaches are evaluated on different data sets, making direct performance comparison impossible. The data collected includes energy monitors at premises-level, circuit-level and appliance-level in 6 homes in the US. However, only premises-level data in kHz range and circuit-level data in Hz range have been realised in v1.0.
• Laughman C, Lee K, Cox R, et al. Power signature analysis. IEEE Power and Energy Magazine. 2003;1(2):56-63.
• This article provides a high level overview of the state of the art at the time of publishing. The approaches discussed include those applicable to high granularity data, such as harmonic and transient analysis, in addition to steady-state disaggregation methods. Finally, some intuition is given into how appliances with a continuously varying power draw can be disaggregated.
• Liang J, Ng S, Kendall G, Cheng J. Load Signature Study - Part I: Basic Concept, Structure, and Methodology. IEEE Transactions on Power Delivery. 2010;25(2):551-560.
• The first part of this pair of papers gives an in-depth and up-to-date discussion of the various approaches to NIALM. The authors also provide a useful description of how multiple approaches can be combined to give a single, more accurate classification. Finally, an evaluation of the many accuracy metrics which have been used is also given.
• Liang J, Ng SKK, Kendall G, Cheng JWM. Load Signature Study - Part II: Disaggregation Framework, Simulation, and Applications. IEEE Transactions on Power Delivery. 2010;25(2):561-569.
• The second part of this pair of papers gives a detailed description of a household simulator that would generate data that could be used to test NIALM algorithms. This allowed the authors to analyse the effect of many different situations, such as noise, concentration of appliance events, the impact of simultaneously operating appliances and the impact of similar appliances. The simulations confirm the first paper's proposal that the fusion of NIALM techniques can improve the disaggregation performance.
• Norford LK, Leeb SB. Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms. Energy and Buildings. 1996;24(1):51-64.
• This article gives a description of how NIALM techniques successful in domestic settings can be extended of commercial buildings. Interestingly, commercial buildings do not only present a new set of appliances, but also bring new applications to the field, such as fault detection. The authors discuss the performance of state-of-the-art steady-state and transient-state disaggregation methods at the time of publishing.
• Zeifman M, Roth K. Nonintrusive appliance load monitoring: Review and outlook. IEEE Transactions on Consumer Electronics. 2011;57(1):76-84.
• This paper is focussed entirely on giving a description and critique of all techniques applied within the NIALM field over the past 25 years. The range of approaches identified by the authors make this paper very useful when viewing the field as a whole. Accuracy metrics are also discussed, with the authors concluding that there is no agreed metric on which disaggregation performance should be assessed.

The Reference Energy Disaggregation Data Set

Accurately comparing the performance of energy disaggregation (Non-intrusive Appliance Load Monitoring) methods is almost impossible unless the same test data is used. For this reason, a team at MIT have built The Reference Energy Disaggregation Data Set (REDD). A few weeks ago, an initial (v1.0) snapshot of this data set was released for use in academic work. The paper describing the equipment used for data collection is:

Kolter JZ, Johnson MJ. REDD : A Public Data Set for Energy Disaggregation Research. In: Workshop on Data Mining Applications in Sustainability (SIGKDD). San Diego, CA; 2011

The dataset contains data for 6 houses. For each house:

• The current and voltage of the two mains circuits are monitored at 15kHz. This is also available for download as power readings down sampled to 1Hz
• The power of each circuit in the home is also monitored to a frequency of 1 reading every 3-4 seconds. Each house contains 11-26 circuits, with large appliances often operating on their own dedicated circuit
• The power drawn by individual appliances is also being monitored, although not enough data had been collected to warrant its release in v1.0

Collecting energy consumption data is a time consuming and expensive process. The lack of available data about appliance behaviour and energy consumption often limits the understanding necessary to design energy disaggregation methods. Through the study of this dataset, I hope to provide some insight into how the energy is consumed in the home and how energy disaggregation methods can take advantage of this.

Energy Breakdown

The aim of energy disaggregation methods is often to maximise the disaggregation accuracy, or minimise the difference between the predicted and actual energy consumption of each appliance. Therefore, the penalty for not recognising an appliance has consumed any energy increases with the energy consumption of the appliance. Consequently, it is more important for energy disaggregation methods to be able to disaggregate an appliance which consumes a high amount of energy than one that consumes a low amount of energy. So which appliances are most important? The figure below shows the relative contributions of each household circuit for house 1 of REDD.

Energy breakdown by circuit of house 1

The above figure shows that just 7 circuits collectively contribute over 75% of the house's total energy consumption. Admittedly, not all circuits contain a single appliance, although I doubt any circuit contains more than a handful of appliances. I'm a little surprised that the fridge comes out as the highest energy consumer, but I don't think it's unusual that it would contribute a fair portion of a house's energy consumption.

Appliance Behaviour

In order to disaggregate a household's energy consumption into the contributing appliances, it is often necessary to build models of such appliances. Analysing the power demand of each appliance is often a good place to start to decide what information such a model should contain. Since the fridge came out as the highest consumer, I've chosen to take a closer look the fridge's power demand in 5 of the REDD houses. Below is a plot of the power demand over a 45 minute period.

Almost all fridges have a different power demand, duration of use and periodicity. However, interestingly, they all share an identical model of usage, in that they all exhibit similar cyclic behaviour. For each fridge, the cycle begins with a single increase in power, which gradually decreases during the cycle and finally ends sharply. Although not ideal, this is encouraging for the purpose of general appliance models. Such a general model of fridges could be built, and would only need to be tweaked in order to match a specific fridge's signature.