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

Increasing energy efficiency in buildings can reduce costs and emissionssubstantially. Historically, this has been treated as a local, or single-agent,optimization problem. However, many buildings utilize the same types of thermalequipment e.g. electric heaters and hot water vessels. During operation,occupants in these buildings interact with the equipment differently therebydriving them to diverse regions in the state-space. Reinforcement learningagents can learn from these interactions, recorded as sensor data, to optimizethe overall energy efficiency. However, if these agents operate individually ata household level, they can not exploit the replicated structure in theproblem. In this paper, we demonstrate that this problem can indeed benefitfrom multi-agent collaboration by making use of targeted exploration of thestate-space allowing for better generalization. We also investigate trade-offsbetween integrating human knowledge and additional sensors. Results show thatsavings of over 40% are possible with collaborative multi-agent systems makinguse of either expert knowledge or additional sensors with no loss of occupantcomfort. We find that such multi-agent systems comfortably outperformcomparable single agent systems.

Valuing knowledge, information and agency in Multi-agent Reinforcement Learning: a case study in smart buildings