For the past three decades extensive research has been underway in efforts to disaggregate total power consumption of a house to appliance level detail. The first paper that introduced this concept in 1992 (by George Hart) has been cited 341 times so far, which says on average 16 papers come out a year that deal with this subject directly or indirectly. [245 of these papers came out in the last three years alone!]. The reason behind so much research interest in this field is because it is conveniently located in an academic space where people from Electrical Engineering, Civil and Environmental Engineering, Computer Science and Machine learning can simultaneously work on different aspects of it. It is an intriguing theoretical problem for the theory minded (as it provides a new use case for the source separation paradigm), promises ample opportunities in terms of hardware design and sensing for the more application/experiment oriented, and comes with the benefit of energy savings for the implication oriented- which is also an easy sell to the general public.
From the implication point of view, disaggregated energy is useful because of the following reasons:
1. Users can take necessary actions to save energy if they know which appliance is consuming how much.
2. Utilities can provide personalized recommendations to users if they know which user is consuming more than the average in their group.
3. Utilities can also perform demand response based on the information they have about what appliance is operating at any given time.
4. Improved load forecasting.
5. Potential for fault detection and diagnosis in larger systems.
Testing the validity of reasons 3 and 4 is difficult because until appliance level data is available in a large scale, the actual implication of demand response based on appliance level data cannot be evaluated. Also, current state of demand response algorithms is not advanced enough where we can efficiently decide what appliances to turn off to achieve the required peak shifting (or whatever the requirement is). Very little work has been done on using appliance signatures for fault diagnosis and pre-emptive action. So, right now the only real motivation behind working towards a NILM solution that can be tested is its utility in helping consumers take control of their consumption habits and, in the process, save energy.
A quick literature review reveals that not many people have looked into the potential of appliance level feedback in saving energy. Of the 38 studies (from 1979 to 2006) looked at by Darby in her seminal report in 2006, not a single one dealt with the impact of disaggregated feedback in terms of energy savings attainable. The studies mostly looked at savings from aggregate feedbacks and recommendations and realistic savings that were achievable ranged from 5-15%.
Ehrhardt-Martinez el al. in 2010 performed a comprehensive literature review and found that there are 5 studies that have looked into the impact of disaggregated feedback. The following is the list of the studies:
1. Dobson and Griffin (1993, Ontario Hydro, Canada) : Experiment done on 25 households found that over a period of two months energy savings of 12.9% were achieved as compared to a control group of 75 other houses. The households were given real disaggregated time data on a computer screen. No details on how the savings resulted (new devices, behavioral changes etc.). No details on persistence (although the behavior was reported to be persistent over the time of the study).
2. Ueno, Inada et al. (2006, Japan) : Two month long experiment on 10 houses where information was logged every 30 minutes. Every morning an email was sent to the users detailing their appliance level energy usage. They found average savings of 18%. [Although there was temperature difference between the period of data collection and baseline, which makes the savings lower. Some have estimated it to be at around 9%].
4. Wood and Newborough(2003, UK): Four month long study on 20 households with 22 more as control. Users showed savings of up to 15% (varied from 11%-39%). The feedback was only on cooking appliances, so the findings aren’t generalizable.
5. Karbo and Larson (2005 Denmark). This study is on 3000 households with 50 households given appliance level feedback. Expected savings of 10%. But the actual study is still being implemented at the time of the paper and results aren’t back yet. No information on what appliances (if all) were monitored.
In summary, savings from 10-15% seems to be a safe bet based on these studies. In my next post, I’ll outline the issues pertaining to NILM that these studies fail to address. Then I’ll build a case for the ideal study that might need to be created to make a definitive claim about savings from NILM methods.