English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Energy disaggregation estimates appliance-by-appliance electricityconsumption from a single meter that measures the whole home's electricitydemand. Recently, deep neural networks have driven remarkable improvements inclassification performance in neighbouring machine learning fields such asimage classification and automatic speech recognition. In this paper, we adaptthree deep neural network architectures to energy disaggregation: 1) a form ofrecurrent neural network called `long short-term memory' (LSTM); 2) denoisingautoencoders; and 3) a network which regresses the start time, end time andaverage power demand of each appliance activation. We use seven metrics to testthe performance of these algorithms on real aggregate power data from fiveappliances. Tests are performed against a house not seen during training andagainst houses seen during training. We find that all three neural nets achievebetter F1 scores (averaged over all five appliances) than either combinatorialoptimisation or factorial hidden Markov models and that our neural netalgorithms generalise well to an unseen house.

Neural NILM: Deep Neural Networks Applied to Energy Disaggregation