6 research outputs found
Structured Dictionary Learning for Energy Disaggregation
The increased awareness regarding the impact of energy consumption on the
environment has led to an increased focus on reducing energy consumption.
Feedback on the appliance level energy consumption can help in reducing the
energy demands of the consumers. Energy disaggregation techniques are used to
obtain the appliance level energy consumption from the aggregated energy
consumption of a house. These techniques extract the energy consumption of an
individual appliance as features and hence face the challenge of distinguishing
two similar energy consuming devices. To address this challenge we develop
methods that leverage the fact that some devices tend to operate concurrently
at specific operation modes. The aggregated energy consumption patterns of a
subgroup of devices allow us to identify the concurrent operating modes of
devices in the subgroup. Thus, we design hierarchical methods to replace the
task of overall energy disaggregation among the devices with a recursive
disaggregation task involving device subgroups. Experiments on two real-world
datasets show that our methods lead to improved performance as compared to
baseline. One of our approaches, Greedy based Device Decomposition Method
(GDDM) achieved up to 23.8%, 10% and 59.3% improvement in terms of
micro-averaged f score, macro-averaged f score and Normalized Disaggregation
Error (NDE), respectively.Comment: 10 Page
Matrix Factorisation for Scalable Energy Breakdown
Homes constitute more than one-thirds of the total energy consumption. Producing an energy breakdown for a home has been shown to reduce household energy consumption by up to 15%, among other benefits. However, existing approaches to produce an energy breakdown require hardware to be installed in each home and are thus prohibitively expensive. In this paper, we propose a novel application of feature-based matrix factorisation that does not require any additional hard- ware installation. The basic premise of our approach is that common design and construction patterns for homes create a repeating structure in their energy data. Thus, a sparse basis can be used to represent energy data from a broad range of homes. We evaluate our approach on 516 homes from a publicly available data set and find it to be more effective than five baseline approaches that either require sensing in each home, or a very rigorous survey across a large number of homes coupled with complex modelling. We also present a deployment of our system as a live web application that can potentially provide energy breakdown to millions of homes