Real building implementation of a deep reinforcement learning controller to enhance energy efficiency and indoor temperature control-Reference-Cited by-同舟云学术

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Real building implementation of a deep reinforcement learning controller to enhance energy efficiency and indoor temperature control

Published:2024-08 Issue: Volume:368 Page:123447
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Silvestri Alberto^ORCID,Coraci Davide^ORCID,Brandi Silvio,Capozzoli Alfonso,Borkowski Esther^ORCID,Köhler Johannes^ORCID,Wu Duan^ORCID,Zeilinger Melanie N.,Schlueter Arno^ORCID

Publisher

Elsevier BV

Reference62 articles.

1. Real-world challenges for multi-agent reinforcement learning in grid-interactive buildings;Nweye;Energy AI,2022

2. A data analytics-based tool for the detection and diagnosis of anomalous daily energy patterns in buildings;Piscitelli;Build Simul,2021

3. Online implementation of a soft actor-critic agent to enhance indoor temperature control and energy efficiency in buildings;Coraci;Energies,2021

4. A comparative review of heating systems in EU countries, based on efficiency and fuel cost;Martinopoulos;Renew Sustain Energy Rev,2018

5. Rule-based scheduling of air conditioning using occupancy forecasting;Dorokhova;Energy AI,2020

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Integration of LSTM networks with gradient boosting machines (GBM) for assessing heating and cooling load requirements in building energy efficiency;Energy Exploration & Exploitation;2024-08-02

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