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In this paper, we investigate whether "big-data" is more valuable than"precise" data for the problem of energy disaggregation: the process ofbreaking down aggregate energy usage on a per-appliance basis. Existingtechniques for disaggregation rely on energy metering at a resolution of 1minute or higher, but most power meters today only provide a reading once permonth, and at most once every 15 minutes. In this paper, we propose a newtechnique called Neighbourhood NILM that leverages data from 'neighbouring'homes to disaggregate energy given only a single energy reading per month. Thekey intuition behind our approach is that 'similar' homes have 'similar' energyconsumption on a per-appliance basis. Neighbourhood NILM matches every homewith a set of 'neighbours' that have direct submetering infrastructure, i.e.power meters on individual circuits or loads. Many such homes already exist.Then, it estimates the appliance-level energy consumption of the target home tobe the average of its K neighbours. We evaluate this approach using 25 homesand results show that our approach gives comparable or better disaggregation incomparison to state-of-the-art accuracy reported in the literature that dependon manual model training, high frequency power metering, or both. Results showthat Neighbourhood NILM can achieve 83% and 79% accuracy disaggregating fridgeand heating/cooling loads, compared to 74% and 73% for a technique called FHMM.Furthermore, it achieves up to 64% accuracy on washing machine, dryer,dishwasher, and lighting loads, which is higher than previously reportedresults. Many existing techniques are not able to disaggregate these loads atall. These results indicate a potentially substantial advantage to installingsubmetering infrastructure in a select few homes rather than installing newhigh-frequency smart metering infrastructure in all homes.

Neighbourhood NILM: A Big-data Approach to Household Energy Disaggregation