Abstract
A data-driven methodology to improve the energy disaggregation accuracy during Non-Intrusive Load Monitoring is proposed. In detail, the method uses a two-stage classification scheme, with the first stage consisting of classification models processing the aggregated signal in parallel and each of them producing a binary device detection score, and the second stage consisting of fusion regression models for estimating the power consumption for each of the electrical appliances. The accuracy of the proposed approach was tested on three datasets—ECO (Electricity Consumption & Occupancy), REDD (Reference Energy Disaggregation Data Set), and iAWE (Indian Dataset for Ambient Water and Energy)—which are available online, using four different classifiers. The presented approach improves the estimation accuracy by up to 4.1% with respect to a basic energy disaggregation architecture, while the improvement on device level was up to 10.1%. Analysis on device level showed significant improvement of power consumption estimation accuracy especially for continuous and nonlinear appliances across all evaluated datasets.
Highlights
Between 25% and 40% of the global energy consumption and the corresponding amount of carbon dioxide emissions comes from residential buildings [1,2,3,4]
The best performing energy disaggregation scores per dataset are indicated in bold for both one- and two-stage results
The most significant improvements in terms of relative performance were observed when using Deep Neural Networks (DNNs) as classifier where performance was improved by 4.1% (REDD-2 dataset)
Summary
Between 25% and 40% of the global energy consumption and the corresponding amount of carbon dioxide emissions comes from residential buildings [1,2,3,4]. Recent studies have shown that households are usually bad at estimating individual power consumption (e.g., overrating small appliances consumption and under-rating the amount of energy for heating) [21] This means that the energy consumption must be either measured on device level, which disadvantageously results in increased cost due to wiring issues and data acquisition [19], or that the aggregated energy (consumed energy measured centrally for each household) must be split to appliance level automatically, which is called energy disaggregation. The term non-intrusive is used to point out the distinction to Intrusive Load Monitoring (ILM) methods utilizing several measurements and smart meters and set the focus on determining the per device consumption.
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