Abstract
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.
Highlights
The introduction of smart meters makes possible to collect energy consumption readings at fine-grained spatio-temporal resolution, enabling the extraction of detailed information about individual energy usage habits
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 and for the presence of unknown loads
We first briefly introduce the main approaches discussed in literature for solving the energy disaggregation problem, we introduce our algorithm, and we evaluate its performance by comparing it against two state of the art disaggregation algorithms applied to a publicly available dataset
Summary
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), enabling the extraction of detailed information about individual energy usage habits. 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, we introduce our algorithm, and we evaluate its performance by comparing it against two state of the art disaggregation algorithms applied to a publicly available dataset. State of the art of energy use characterization 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. Synthetic load consumption traces generated by open source software such as Loadprofilegenerator can be adopted
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