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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper presents a novel Sequence-to-Sequence (Seq2Seq) model based on atransformer-based attention mechanism and temporal pooling for Non-IntrusiveLoad Monitoring (NILM) of smart buildings. The paper aims to improve theaccuracy of NILM by using a deep learning-based method. The proposed methoduses a Seq2Seq model with a transformer-based attention mechanism to capturethe long-term dependencies of NILM data. Additionally, temporal pooling is usedto improve the model's accuracy by capturing both the steady-state andtransient behavior of appliances. The paper evaluates the proposed method on apublicly available dataset and compares the results with other state-of-the-artNILM techniques. The results demonstrate that the proposed method outperformsthe existing methods in terms of both accuracy and computational efficiency.

Sequence-to-Sequence Model with Transformer-based Attention Mechanism and Temporal Pooling for Non-Intrusive Load Monitoring