Load Signature Study—Part I: Basic Concept, Structure, and Methodology

Liang J, Ng S, Kendall G, Cheng J. Load Signature Study - Part I: Basic Concept, Structure, and Methodology. Power Delivery, IEEE Transactions on. 2010;25(2):551-560.

This is the first of a two part study of domestic appliance load signatures. The authors spend the bulk of this article defining concepts and mathematical terms which are built upon in subsequent sections. A description of existing load concepts, signature features and disaggregation methods was given, each of which was assigned an acronym to aid identification.

The paper's contribution lies in the proposal of:

• Committee Decision Mechanisms - methods which combine various feature extraction and classification methods in order find the best final solution.
• Disaggregation algorithm accuracy measures - metrics which can be used to evaluate the individual accuracy of event detection or classification, or the combination of the two.
• Appliance signature similarity measures - metrics which can be used to compare the similarity between various signatures.
• Disaggregation algorithm Complementary Ratio - metrics which can be used to calculate whether a disaggregation algorithm adds any additional information to that given by a second disaggregation algorithm.

I am now planning to read the second part of this study:

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.

Using smoothness to detect appliances

This post follows up on a previous post describing how if we subtract an appliance's signature from the aggregate consumption, we end up with a smoother plot. This post gives an evaluation of the effectiveness of using smoothness to detect the operation of the fridge, washing machine and dishwasher from the household aggregate signal.

Aggregate & Truth Values

The graph below shows a plot of the household aggregate power demand (W) and sub-metered appliances over a 24-hour period. The household aggregate is shown in black, the fridge in blue, the washing machine in green and the dishwasher in red.

Calculating the individual appliance's demand from the household aggregate is equivalent to calculating the blue, green and red lines from the black line. To do this, at each possible position within the 24 hour period, the known signature of the appliance was subtracted from the aggregate, and the smoothness of the resulting data was calculated. This was repeated for each appliance in turn. Below are plots of the smoothness property at each position in the 24 hour period for the fridge, washing machine and dishwasher.

Fridge

Below is a plot of the smoothness function calculated for each possible subtraction of the fridge's signature from the aggregate plot. The y-axis is the smoothness measure, where a positive value represents a smoother plot after the signature was subtracted, while a negative value represents a less smooth plot.

We can see that this method works very well during the overnight period. However, during the day there are a number of false positives in addition to the correct detections. This is due to the fridge's relatively small power demand (90W) compared to the household average (500W).

Washing Machine

Below is a plot of the smoothness function calculated for each possible subtraction of the washing machine's signature from the aggregate plot.

This has worked well compared to the fridge. We can see one peak at around 300 on the x axis, which corresponds to a correct identification of the washing machine's cycle. However, there are also two false positives later in the day.

Dishwasher

Below is a plot of the smoothness function calculated for each possible subtraction of the dishwasher's signature from the aggregate plot.

The application of this technique to the dishwasher has been the most successful. We can see there are two clear peaks, both of which correspond to the two times the dishwasher was run in the 24 hour period. There are no false positives as with the other appliances, although the peaks do have some close matches of slightly lower values. The success of this method was due the dishwasher's unique signature; two cycles separated by a short interval.

Conclusions

This method works best for appliances:

• With large power demands compared to the average step change in the aggregate demand
• With signatures which describe a unique overall pattern

IEEE Pervasive Computing 10 (1): Special Issue on Smart Energy Systems - part 3

Marchiori A, Hakkarinen D, Han Q, Earle L. Circuit-Level Load Monitoring for Household Energy Management. Pervasive Computing, IEEE. 2011;10(1):40-48.

This article compares a heuristic and a probabilistic classification method for steady-state features extracted from a household's aggregate power consumption. The heuristic approach used a clustering approach to train the classifier of each appliance's states. The probability mass function of each cluster was calculated, before combining all possible combinations to find the maximum joint probability. In the probabilistic approach a naive Bayesian classifier was trained using each appliance's states and state transitions. The classifier could then calculate the probability that an appliance in a given state would undergo a specific state transition. In each case, circuit level metering was used.

Although it was not the main focus of the article, it was interesting to learn that authors believe circuit-level metering allows appliances with power demands as small as 10W to be recognised. This extends the potential of NIALM beyond the recognition of appliances drawing as little as 100 or 150W as was possible through premise-level metering.

The authors describe some very promising results for the recognition of a small number of appliances tested. However, the approaches are yet to be tested in real household settings with a greater range of appliances and less usage predictability.

IEEE Pervasive Computing 10 (1): Special Issue on Smart Energy Systems - part 2

Froehlich J, Larson E, Gupta S, et al. Disaggregated End-Use Energy Sensing for the Smart Grid. Pervasive Computing, IEEE. 2011;10(1):28-39.

This article provides an excellent overview of a wide range current approaches towards NIALM. In addition, the authors provide detail of their approach using high-granularity sampling of voltage noise. Support Vector Machines were used to classify the extracted features against a database of known appliance signatures.

Although the described approach uses a very different method to sample data, it raises some issues relevant to my work. The discussion of which features are able to be generalised between appliance types or households is of particular relevance to any unsupervised NIALM systems. The authors also mention that current smart meters report consumption data with 15-minute granularity. I'm not sure where this figure came form so is therefore worth investigating.

Other papers I want to read from this special issue:

• Marchiori A, Hakkarinen D, Han Q, Earle L. Circuit-Level Load Monitoring for Household Energy Management. Pervasive Computing, IEEE. 2011;10(1):40-48.
• Bergman DC, Jin D, Juen JP, et al. Nonintrusive Load-Shed Verification. Pervasive Computing, IEEE. 2011;10(1):49-57.

Interesting papers from elsewhere:

• L.G. Swan and V.I. Ugursal, “Modeling of End-Use Energy Consumption in the Residential Sector: A Review of Modeling Techniques,” Renewable and Sustainable Energy Reviews, vol. 13, no. 8, 2009, pp. 1819–1835.
• 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. Available at: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5337970.

IEEE Pervasive Computing 10 (1): Special Issue on Smart Energy Systems

I been reading more from the special issue on smart energy systems. Here are my thoughts:

Paradiso J, Dutta P, Gellersen H, Schooler E. Guest Editorsʼ Introduction: Smart Energy Systems. Pervasive Computing, IEEE. 2011;10(1):11-12.

This introductory article provides an overall motivation for the application of smart energy systems within the domestic environment. In doing so, it describes the need for agent controlled energy systems and a motivation to study human-agent interaction within the domestic energy domain.

Sundramoorthy V, Cooper G, Linge N, Liu Q. Domesticating Energy-Monitoring Systems: Challenges and Design Concerns. Pervasive Computing, IEEE. 2011;10(1):20-27.

This article focuses on how domestic energy monitoring systems can induce positive behavioural change. The authors report findings from the first of three stages of deployment. The first stage involved the installation of household aggregate electricity meters to 77 houses in the UK. The hardware used was the CurrentCost clamp-on meters, which collected power readings every six seconds. The next stage of deployment will involve the installation of household aggregate electricity meters on more houses in addition to 10 individual appliance sub-meters. The aim is to deploy this equipment in 250 houses in the UK and Bulgaria.

Although the focus of the article was on energy feedback technology, it raised many points of interest to the field of non-intrusive load monitoring. Most interesting. the next deployment stage will investigate the financial evaluation of deployment appliance sub-meters. This will provide a clear motivation for NILM should the study find that the cost of deployment exceeds the financial savings through reduction of energy consumption. The article also covers areas of interest to NILM, such as consumer energy awareness and data privacy.

Next on my list of things to read are three papers directly related to NILM:

• Froehlich J, Larson E, Gupta S, et al. Disaggregated End-Use Energy Sensing for the Smart Grid. Pervasive Computing, IEEE. 2011;10(1):28-39.
• Marchiori A, Hakkarinen D, Han Q, Earle L. Circuit-Level Load Monitoring for Household Energy Management. Pervasive Computing, IEEE. 2011;10(1):40-48.
• Bergman DC, Jin D, Juen JP, et al. Nonintrusive Load-Shed Verification. Pervasive Computing, IEEE. 2011;10(1):49-57.

Look Back before Leaping Forward: Four Decades of Domestic Energy Inquiry

I recently read the following article on domestic energy monitoring in the IEEE Pervasive Computing magazine:

Hazas M, Friday A, Scott J. Look Back before Leaping Forward: Four Decades of Domestic Energy Inquiry. Pervasive Computing, IEEE. 2011;10(1):13-19.

This article really highlights the interdisciplinary nature of domestic energy management. It covers the following areas to a shallow level of detail:

• Personalised feedback
• Energy monitoring hardware
• Consumption analysis software (including NIALM)
• Consumer behaviour

It was actually this article which made me aware of Microsoft's Hohm, among many other existing products for domestic energy monitoring. The authors also mention the AlertMe system as one solution to online energy management. However, the AlertMe system is described as a 'closed system', I assume due to lack of public knowledge of their data APIs.

Although the article covers the area of non-intrusive energy monitoring in little depth, it was still of great value to me. The wide range of references has encouraged me to look specifically at domestic energy audits and studies of energy feedback mechanisms. These will be most relevant to my field when describing the real-world setting of the problem and the barriers faced so far.

Microsoft Hohm

I've just come across Microsoft's Hohm, an energy visualisation web app similar to Google's powermeter. It's only open to US buildings at the moment, but looks pretty sharp and seems to pull data from a variety of sources. Here's a screen shot of the main dashboard:


This shows the building's overall Hohm score, a measure of its efficiency from 0 to 100, 100 being the best. It also compares the annual cost of energy use of the building compared to the local area's average.

From the dashboard you can select an option to breakdown this annual consumption by appliance type, giving the screen show below:

Now the information shown here is very interesting. Without uploading any data collected from meters at the building, Hohm has estimated the breakdown of energy use. Not only is the overall consumption broken down by appliance type, but selecting an appliance type breaks it down further by individual appliances.

The estimates shown are based on data from a range of sources:

• Building properties (size, age etc.)
• Appliance properties (chosen boiler type etc.)
• Weather data

I'd be really interested in how these estimated breakdowns change when the household aggregate energy data is uploaded. In addition, I'd like to see how these estimations differ from those produced by NIALMs.