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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Reinforcement learning is a promising model-free and adaptive controller fordemand side management, as part of the future smart grid, at the districtlevel. This paper presents the results of the algorithm that was submitted forthe CityLearn Challenge, which was hosted in early 2020 with the aim ofdesigning and tuning a reinforcement learning agent to flatten and smooth theaggregated curve of electrical demand of a district of diverse buildings. Theproposed solution secured second place in the challenge using a centralised'Soft Actor Critic' deep reinforcement learning agent that was able to handlecontinuous action spaces. The controller was able to achieve an averaged scoreof 0.967 on the challenge dataset comprising of different buildings andclimates. This highlights the potential application of deep reinforcementlearning as a plug-and-play style controller, that is capable of handlingdifferent climates and a heterogenous building stock, for district demand sidemanagement of buildings.

A Centralised Soft Actor Critic Deep Reinforcement Learning Approach to District Demand Side Management through CityLearn