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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Residential buildings with the ability to monitor and control their net-load(sum of load and generation) can provide valuable flexibility to power gridoperators. We present a novel multiclass nonintrusive load monitoring (NILM)approach that enables effective net-load monitoring capabilities athigh-frequency with minimal additional equipment and cost. The proposed machinelearning based solution provides accurate multiclass state predictions whileoperating at a faster timescale (able to provide a prediction for each 60-Hz accycle used in US power grid) without relying on event-detection techniques. Wealso introduce an innovative hybrid classification-regression method thatallows for the prediction of not only load on/off states via classification butalso individual load operating power levels via regression. A test bed witheight residential appliances is used for validating the NILM approach. Resultsshow that the overall method has high accuracy and, good scaling andgeneralization properties. Furthermore, the method is shown to have sufficientresponse time (within 160ms, corresponding to 10 ac cycles) to support buildinggrid-interactive control at fast timescales relevant to the provision of gridfrequency support services.

Scalable Hybrid Classification-Regression Solution for High-Frequency Nonintrusive Load Monitoring