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/

Zendy Pro

Presents the zaia usage as premium feature

ZAIA

Presents AI Discovery search usage as a premium feature

AI Discovery

Presents AI Discovery explain and summary usage as a premium feature

Content Analysis

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Presents the pdf analysis as premium feature

PDF Analysis

1500 Top-up

3000 Top-up

Zendy Basic

2500 Top-up

Zendy Unlimited

Presents the access of premium content as premium feature

Premium Content

Flexibility from demand-side resources is increasingly required in modern power systems to maintain the dynamic balance between demand and supply. This flexibility comes from elastic users managing controllable loads. In this context, controlling Electric Space Heaters (ESHs) is of particular interest because it can leverage building inner thermal storage capacity to shift consumption while maintaining comfort conditions. Some economic Demand Response (DR) programs have considered exploiting EHSs flexibility potentials in recent years. However, these programs still struggle to engage customers due to the complexity of processing price signals for inexpert users. Therefore, it is necessary to develop automated tools for helping users to operate their loads. Accordingly, this paper presents a recommender system based on Gaussian processes to discover users&#x2019; valuations of thermal comfort and perform the predictive control of their ESHs. The proposed method enables customers to participate in DR programs and impose their preferences through straightforward queries instead of directly changing control parameters. Validation results demonstrate that users maximize their utility by supplying noiseless and consistent data to the recommender system. Additionally, the suggested approach achieves a higher acceptance rate than other methods from the literature, such as persistency and support vector machines.

A Recommender System for Predictive Control of Heating Systems in Economic Demand Response Programs