Sustainable Energy Consumption Monitoring in Residential Settings Akshay Uttama Nambi S. N. 1 , Thanasis G. Papaioannou 1 , Dipanjan Chakraborty 2 and Karl Aberer 1 1 School of Computer and Communication Sciences Ecole Polytechnique F´ ed´ erale de Lausanne CH-1015 Lausanne, Switzerland 2 IBM Research - India Lab, New Delhi, India akshay.uttama@gmail.com, {thanasis.papaioannou, karl.aberer}@epfl.ch, cdipanjan@in.ibm.com Abstract—The continuous growth of energy needs and the fact that unpredictable energy demand is mostly served by unsustainable (i.e. fossil-fuel) power generators have given rise to the development of Demand Response (DR) mechanisms for flattening energy demand. Building effective DR mechanisms and user awareness on power consumption can significantly benefit from fine-grained monitoring of user consumption at the appliance level. However, installing and maintaining such a monitoring infrastructure in residential settings can be quite expensive. In this paper, we study the problem of fine-grained appliance power-consumption monitoring based on one house- level meter and few plug-level meters. We explore the trade-off between monitoring accuracy and cost, and exhaustively find the minimum subset of plug-level meters that maximize accuracy. As exhaustive search is time- and resource-consuming, we define a heuristic approach that finds the optimal set of plug-level meters without utilizing any other sets of plug-level meters. Based on experiments with real data, we found that few plug-level meters - when appropriately placed - can very accurately disaggregate the total real power consumption of a residential setting and verified the effectiveness of our heuristic approach. Keywords-Energy disaggregation; Hidden Markov Models; FHMM; NILM; plug-level meter I. I NTRODUCTION Demand Response (DR) mechanisms aim to provide price- based or other incentives to the users to shift their energy loads and flatten their daily energy consumption. The effectiveness of DR mechanisms in turn depends on how much fine- grained information is available about users. For instance, if we knew appliance-level usage of residential users, it can be used for selection of the pricing or incentive mechanism that maximizes the effectiveness of DR policies. According to [1], there are 4 different consumption types of appliances, namely i) Permanent consumer devices, (ii) On-off appliances, (iii) Multistate appliances, (iv) Continuously variable consumer devices, where price-based incentive mechanisms can be ef- fective only for types i) and ii). Based on appliance-level consumption profiling, the DR mechanism designer knows the highest energy consuming appliances (subject to power consumption reduction) and time of appliance usage (for de- riving load shiftability during peak hours). Moreover, accurate consumption profiling is essential for the calculation of the consumption baseline, based on which the effectiveness of any DR mechanism is measured. Fine-grained monitoring and modeling energy consumption in houses can be achieved by a) an intrusive method, where each appliance in the house is monitored separately, b) a non-intrusive load monitoring (NILM) [2], where a single residential energy meter is used for estimating the individual appliance usage information based on appliance consumption signatures (i.e. consumption states, e.g. on/stand-by/off, real power, reactive power, voltage and current waveforms, etc.), historical data or user annotations. While NILM has been investigated extensively [1], the accuracy of achievable disaggregation is limited, while the generalization of the models trained from certain houses for the energy con- sumption disaggregation of others is ineffective. On the other hand, intrusive methods involve installing plug-level meters for each appliance in the house and have high investment and maintenance cost. This paper explores an approach to minimize the intrusion, while maximizing the disaggregation performance. It is based on the intuition that a few selected appliances, if monitored can help to disaggregate the total load quite efficiently. In order to achieve sustainability of our monitoring approach, we try to minimize intrusion and associ- ated capital and operational expenses of plug-level monitoring by selectively installing the minimum number of plug-level meters per house, so as to achieve very high disaggregation accuracy. The optimal locations of plug-level meters are found based on exhaustive search. For avoiding time-consumption and resource-expenses, we propose a heuristic approach that is experimentally proved to always find the optimal set of plug-level meters. Based on experiments with real power consumption data, we find that our approach can achieve very accurate load disaggregation with few plug-level meters installed in residential settings. The remainder of this paper is organized as follows: In Section II, we overview the related work. In Section III, we set the goals of our monitoring approach. In Section IV, we describe the model for appliance-level energy consumption in houses. In Section V, we describe our approach for sustainable appliance monitoring based on exhaustive search and based on a heuristic. In Section VI, we experimentally verify the high accuracy of our monitoring approach and the effectiveness of the proposed heuristic. Finally, in Section VII, we conclude our work.