118 research outputs found
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
Metadata for Energy Disaggregation
Energy disaggregation is the process of estimating the energy consumed by
individual electrical appliances given only a time series of the whole-home
power demand. Energy disaggregation researchers require datasets of the power
demand from individual appliances and the whole-home power demand. Multiple
such datasets have been released over the last few years but provide metadata
in a disparate array of formats including CSV files and plain-text README
files. At best, the lack of a standard metadata schema makes it unnecessarily
time-consuming to write software to process multiple datasets and, at worse,
the lack of a standard means that crucial information is simply absent from
some datasets. We propose a metadata schema for representing appliances,
meters, buildings, datasets, prior knowledge about appliances and appliance
models. The schema is relational and provides a simple but powerful inheritance
mechanism.Comment: To appear in The 2nd IEEE International Workshop on Consumer Devices
and Systems (CDS 2014) in V\"aster{\aa}s, Swede
Stochastic Modelling and Optimisation of Internet Auction Processes
AbstractInternet auctions are an attractive mechanism for the exchange of goods at a non-fixed price point. The operation of these auctions can be run under a variety of parameters. In this paper, we provide a theoretical analysis of fixed time forward auctions in cases where a single bid or multiple bids are accepted in a single auction. A comparison of the economic benefits and the corresponding buyer and seller surpluses between the auctions where a single bid is accepted and the auctions where multiple bids are accepted is made. These models are verified through systematic simulation experiments, based on a series of operational assumptions, which characterise the arrival rate of bids, as well as the distribution from which the private values of buyers are sampled. Decision rules for optimising surplus under different auction fee structures are also given
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
zkFL: Zero-Knowledge Proof-based Gradient Aggregation for Federated Learning
Federated Learning (FL) is a machine learning paradigm, which enables
multiple and decentralized clients to collaboratively train a model under the
orchestration of a central aggregator. Traditional FL solutions rely on the
trust assumption of the centralized aggregator, which forms cohorts of clients
in a fair and honest manner. However, a malicious aggregator, in reality, could
abandon and replace the client's training models, or launch Sybil attacks to
insert fake clients. Such malicious behaviors give the aggregator more power to
control clients in the FL setting and determine the final training results. In
this work, we introduce zkFL, which leverages zero-knowledge proofs (ZKPs) to
tackle the issue of a malicious aggregator during the training model
aggregation process. To guarantee the correct aggregation results, the
aggregator needs to provide a proof per round. The proof can demonstrate to the
clients that the aggregator executes the intended behavior faithfully. To
further reduce the verification cost of clients, we employ a blockchain to
handle the proof in a zero-knowledge way, where miners (i.e., the nodes
validating and maintaining the blockchain data) can verify the proof without
knowing the clients' local and aggregated models. The theoretical analysis and
empirical results show that zkFL can achieve better security and privacy than
traditional FL, without modifying the underlying FL network structure or
heavily compromising the training speed
Demo Abstract: NILMTK v0.2: A Non-intrusive Load Monitoring Toolkit for Large Scale Data Sets
In this demonstration, we present an open source toolkit for evaluating
non-intrusive load monitoring research; a field which aims to disaggregate a
household's total electricity consumption into individual appliances. The
toolkit contains: a number of importers for existing public data sets, a set of
preprocessing and statistics functions, a benchmark disaggregation algorithm
and a set of metrics to evaluate the performance of such algorithms.
Specifically, this release of the toolkit has been designed to enable the use
of large data sets by only loading individual chunks of the whole data set into
memory at once for processing, before combining the results of each chunk.Comment: 1st ACM International Conference on Embedded Systems For
Energy-Efficient Buildings, 201
- …