An algorithm for the non-intrusive disaggregation of energy consumption into its end-uses, also known as non-intrusive appliance load monitoring (NIALM), is presented. The algorithm solves an optimisation problem where the objective is to minimise the error between the total energy consumption and the sum of the individual contributions of each appliance. The algorithm assumes that a fraction of the loads present in the household is known (e.g. washing machine, dishwasher, etc.), but it also considers unknown loads, treating them as a single load. The performance of the algorithm is then compared to that obtained by two state of the art disaggregation approaches implemented in the publicly available NILMTK framework. The first one is based on Combinatorial Optimization, the second one on a Factorial Hidden Markov Model. The results show that the proposed algorithm performs satisfactorily and it even outperforms the other algorithms from some perspectives.
The introduction of smart meters makes possible to collect energy consumption readings at fine-grained spatio-temporal resolution (i.e., measurements with granularity in the order even of a few seconds, for single households), thus enabling the extraction of detailed information about individual energy usage habits. In turn, such knowledge allows for the construction of more accurate mathematical models to characterize individual and collective energy consumption behaviors. Energy end-use disaggregation aims at breaking down the total energy consumption measured at household level into the contributions of single electrical appliances. The use of such disaggregated information is twofold: on one side, it can be leveraged to develop predictive models capable of forecasting future energy consumption behaviours, on the other side it can be directly provided to customers, so that household’s components gain a detailed knowledge of their energy usage. For instance, through an App developed in the context of the enCOMPASS project Footnote 1 , customers can visualize their hourly consumption, as well as charts on their energy end-uses patterns across major end-use categories (e.g., washing machine, dishwasher, clothes dryer, fridge) and they can be alerted of occurring consumption anomalies. Furthermore, personalized hints for reducing energy consumption can be directly delivered to the users. These stimuli are aimed at fostering the adoption of energy saving actions, such as replacing low-efficient appliances into high-efficient ones and reducing energy waste (e.g. turning off lights when rooms are empty).
In this paper we present a novel algorithm for end-use energy disaggregation that evolves the features of a previous work by Piga et al. (2016) accounting for the coarse granularity of standard smart metering systems (a data point every 15 min) and for the presence of unknown loads. To this purpose, we first briefly introduce the main approaches discussed in literature for solving the energy disaggregation problem, then we introduce our algorithm, and finally we evaluate its performance by comparing it against two state of the art disaggregation algorithms applied to a publicly available dataset.
There is a rich literature on automatic disaggregation methods (known as Non-Intrusive Appliance Load Monitoring – NIALM – algorithms) (Batra et al. 2014) aimed at decomposing the aggregate household energy consumption data collected from a single measurement point into device-level consumption data, requiring limited or even no interaction with the user.
The first algorithm for NIALM was proposed by (Hart 1992). Hart’s approach is based on the segmentation of the aggregate power signal into successive steps, which are then matched to the appliance signatures. However, this method is not able to detect multistate appliances and it is neither able to decompose power signals made of simultaneous on/off events on multiple appliances. Since Hart’s contribution, the NIALM problem has been extensively studied in the literature. The survey papers by Zoha et al. (2012) and by Zeifman & Roth (2011) give a complete review on the state-of-the-art of NIALM methods.
Note that the vast majority of the studies on NIALM algorithms validate the proposed solutions using publicly accessible datasets of real energy consumption measurements. The most widely used datasets made available in the last years are reported in Table 1. Alternatively, synthetic load consumption traces generated by open source software such as Loadprofilegenerator Footnote 2 can be adopted.
It can be noted that the fraction of energy consumption correctly assigned by the ILP algorithm to the top 5 consuming appliances is slightly lower than that assigned by the CO and FHMM algorithms. However, the normalized error achieved by the ILP algorithm is always consistently smaller than the one obtained by the two benchmark algorithms, while the root mean square error achieved by the ILP algorithm is slightly lower than that obtained by CO and FHMM.
The true positive rate of the ILP algorithm remains lower than that of the CO and FHMM algorithms, with FHMM outperforming CO. However, an increase in the true positive rate of the ILP algorithm is observed at coarse granularities (45 and 60 min epochs). The relatively poor performance of the ILP in terms of true positive rate is compensated by the very low false positive rate, which is much smaller than that achieved by the benchmark algorithms. This means that, though the ILP algorithm sometimes does not detect some activity periods of the appliances, it almost never fails in detecting off periods, whereas the CO and FHMM algorithms often incorrectly turns on appliances). Overall, the ILP algorithm achieves accuracy and precision ranges comparable to those of the benchmarks, slightly outperforming the benchmarks and showing remarkably smaller interquantile ranges at coarse measurement granularities Footnote 4 .
While there is not a single algorithm that clearly dominates the other ones, the low false positive rate and the relatively good precision and accuracy seems to be features of some importance when feedback is provided to real users, as higher false positives might eventually reduce the user confidence in the algorithm output.
In this paper we have described a novel algorithm for the disaggregation of the overall energy consumption pattern of a household into the single end-uses of each appliance. The proposed algorithm is based on the solution of a quadratic programming problem with mixed integer constraints. In this paper we report the training and the validation of the algorithm on one well known publicly available dataset and its performance has been evaluated for different granularities of the aggregated energy consumption measurements, showing that graceful degradation of the disaggregation results is achieved and that still accurate results can be obtained also in the case of data with 15 min resolution, that is a common data temporal resolution available in most commercial smart-metering solutions, where submetering devices are not or cannot be installed.
The source code and references to the datasets can be found at https://github.com/encompass-project-eu/disaggregator.
As in the dataset used for our numerical assessment no information on presence/absence of house dwellers was included, ua,t was set to 1 by default.
Note that the basic version of the algorithm in (Piga et al. 2016) achieves lower accuracy than the ILP algorithm with the considered dataset, mainly because the too coarse granularity of the measurements above 15 min resolution violates the assumption of piecewise linearity of consumption measurements required in (Piga et al. 2016) and because the fraction of energy consumed by unknown appliances (which are not modelled in (Piga et al. 2016)) is erroneously attributed to the top 5 consuming appliances.
This article has been published as part of Energy Informatics Volume 2 Supplement 1, 2019: Proceedings of the 8th DACH+ Conference on Energy Informatics. The full contents of the supplement are available online at https://energyinformatics.springeropen.com/articles/supplements/volume-2-supplement-1
This work has been partially supported by the Horizon 2020 project enCOMPASS (Grant N. 723059). Publication of this supplement was funded by Austrian Federal Ministry for Transport, Innovation and Technology.
