83,822 research outputs found
Energy Disaggregation via Adaptive Filtering
The energy disaggregation problem is recovering device level power
consumption signals from the aggregate power consumption signal for a building.
We show in this paper how the disaggregation problem can be reformulated as an
adaptive filtering problem. This gives both a novel disaggregation algorithm
and a better theoretical understanding for disaggregation. In particular, we
show how the disaggregation problem can be solved online using a filter bank
and discuss its optimality.Comment: Submitted to 51st Annual Allerton Conference on Communication,
Control, and Computin
Robust energy disaggregation using appliance-specific temporal contextual information
An extension of the baseline non-intrusive load monitoring approach for energy disaggregation using temporal contextual information is presented in this paper. In detail, the proposed approach uses a two-stage disaggregation methodology with appliance-specific temporal contextual information in order to capture time-varying power consumption patterns in low-frequency datasets. The proposed methodology was evaluated using datasets of different sampling frequency, number and type of appliances. When employing appliance-specific temporal contextual information, an improvement of 1.5% up to 7.3% was observed. With the two-stage disaggregation architecture and using appliance-specific temporal contextual information, the overall energy disaggregation accuracy was further improved across all evaluated datasets with the maximum observed improvement, in terms of absolute increase of accuracy, being equal to 6.8%, thus resulting in a maximum total energy disaggregation accuracy improvement equal to 10.0%.Peer reviewedFinal Published versio
NILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring
Non-intrusive load monitoring, or energy disaggregation, aims to separate
household energy consumption data collected from a single point of measurement
into appliance-level consumption data. In recent years, the field has rapidly
expanded due to increased interest as national deployments of smart meters have
begun in many countries. However, empirically comparing disaggregation
algorithms is currently virtually impossible. This is due to the different data
sets used, the lack of reference implementations of these algorithms and the
variety of accuracy metrics employed. To address this challenge, we present the
Non-intrusive Load Monitoring Toolkit (NILMTK); an open source toolkit designed
specifically to enable the comparison of energy disaggregation algorithms in a
reproducible manner. This work is the first research to compare multiple
disaggregation approaches across multiple publicly available data sets. Our
toolkit includes parsers for a range of existing data sets, a collection of
preprocessing algorithms, a set of statistics for describing data sets, two
reference benchmark disaggregation algorithms and a suite of accuracy metrics.
We demonstrate the range of reproducible analyses which are made possible by
our toolkit, including the analysis of six publicly available data sets and the
evaluation of both benchmark disaggregation algorithms across such data sets.Comment: To appear in the fifth International Conference on Future Energy
Systems (ACM e-Energy), Cambridge, UK. 201
Complex patterns on the plane: different types of basin fractalization in a two-dimensional mapping
Basins generated by a noninvertible mapping formed by two symmetrically
coupled logistic maps are studied when the only parameter \lambda of the system
is modified. Complex patterns on the plane are visualised as a consequence of
basins' bifurcations. According to the already established nomenclature in the
literature, we present the relevant phenomenology organised in different
scenarios: fractal islands disaggregation, finite disaggregation, infinitely
disconnected basin, infinitely many converging sequences of lakes, countable
self-similar disaggregation and sharp fractal boundary. By use of critical
curves, we determine the influence of zones with different number of first rank
preimages in the mechanisms of basin fractalization.Comment: 19 pages, 11 figure
Does disaggregated electricity feedback reduce domestic electricity consumption? A systematic review of the literature
We examine 12 studies on the efficacy of disaggregated energy feedback. The
average electricity reduction across these studies is 4.5%. However, 4.5% may
be a positively-biased estimate of the savings achievable across the entire
population because all 12 studies are likely to be prone to opt-in bias hence
none test the effect of disaggregated feedback on the general population.
Disaggregation may not be required to achieve these savings: Aggregate feedback
alone drives 3% reductions; and the 4 studies which directly compared aggregate
feedback against disaggregated feedback found that aggregate feedback is at
least as effective as disaggregated feedback, possibly because web apps are
viewed less often than in-home-displays (in the short-term, at least) and
because some users do not trust fine-grained disaggregation (although this may
be an issue with the specific user interface studied). Disaggregated
electricity feedback may help a motivated sub-group of the population to save
more energy but fine-grained disaggregation may not be necessary to achieve
these energy savings. Disaggregation has many uses beyond those discussed in
this paper but, on the specific question of promoting energy reduction in the
general population, there is no robust evidence that current forms of
disaggregated energy feedback are more effective than aggregate energy
feedback. The effectiveness of disaggregated feedback may increase if the
general population become more energy-conscious (e.g. if energy prices rise or
concern about climate change deepens); or if users' trust in fine-grained
disaggregation improves; or if innovative new approaches or alternative
disaggregation strategies (e.g. disaggregating by behaviour rather than by
appliance) out-perform existing feedback. We also discuss opportunities for new
research into the effectiveness of disaggregated feedback.Comment: Accepted for oral presentation at the 3rd International NILM
Workshop, Vancouver, 14-15 May 201
Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation
In this paper we evaluate several well-known and widely used machine learning algorithms for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load Monitoring approach was considered and the K-Nearest-Neighbours, Support Vector Machines, Deep Neural Networks and Random Forest algorithms were evaluated across five datasets using seven different sets of statistical and electrical features. The experimental results demonstrated the importance of selecting both appropriate features and regression algorithms. Analysis on device level showed that linear devices can be disaggregated using statistical features, while for non-linear devices the use of electrical features significantly improves the disaggregation accuracy, as non-linear appliances have non-sinusoidal current draw and thus cannot be well parametrized only by their active power consumption. The best performance in terms of energy disaggregation accuracy was achieved by the Random Forest regression algorithm.Peer reviewedFinal Published versio
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