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

Demand Response (DR) programs have become a crucial component of smartelectricity grids as they shift the flexibility of electricity consumption fromsupply to demand in response to the ever-growing demand for electricity. Inparticular, in times of crisis, an emergency DR program is required to manageunexpected spikes in energy demand. In this paper, we propose theIncentive-Driven Load Balancer (ILB), a program designed to efficiently managedemand and response during crisis situations. By offering incentives toflexible households likely to reduce demand, the ILB facilitates effectivedemand reduction and prepares them for unexpected events. To enable ILB, weintroduce a two-step machine learning-based framework for participantselection, which employs a graph-based approach to identify households capableof easily adjusting their electricity consumption. This framework utilizes twoGraph Neural Networks (GNNs): one for pattern recognition and another forhousehold selection. Through extensive experiments on household-levelelectricity consumption in California, Michigan, and Texas, we demonstrate theILB program's significant effectiveness in supporting communities duringemergencies.

ILB: Graph Neural Network Enabled Emergency Demand Response Program For Electricity