Applications of reinforcement learning for building energy efficiency control: A review

Fu, Qiming; Han, Zhicong; Chen, Jianping; Lu, You; Wu, Hongjie; Wang, Yunzhe

doi:10.1016/j.jobe.2022.104165

Cited by 37 publications

(2 citation statements)

References 45 publications

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“…First, we used the TSRM to assist physicians in making treatment plans. Compared with supervised learning, the proposed method solves the MDP problem by optimizing cumulative rewards, which gives the model foresight (Fu et al 2022, Funai 2022, Keenan et al 2022, Sanzana et al 2022, Singh et al 2022. The model can give comprehensive recommendations considering the patient's prognosis, which avoids the risk that previous classification methods may use extreme treatment strategies to improve patient status rapidly.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence can use physiological parameters to optimize treatment strategies and predict clinical deterioration of sepsis in ICU

et al. 2023

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Objective Sepsis has seriously threatened the human life. Early identification of patient status risk and appropriate treatment can reduce the septic shock risk and mortality. Our purpose is to design and validate an adjuvant therapy system based on deep reinforcement learning (DRL), which can provide treatment recommendations with provident and assess the risks of patient status and treatment options in early stage. Approach The data is from Beth Israel Deaconess Medical Center. The raw data included 53,423 patients from MIMIC-III. 19,620 eligible samples were screened to form the final cohort. First, the patient's physiological parameters are fed into the DRL therapy strategy recommendation module (TSRM), which provides a forward-looking recommendation for treatment strategy. Then, the recommended strategies above are fed into the reinforcement learning risk assessment module (RAM), which predicts risk of patient status and treatment strategy from a long-term perspective. The deep reinforcement learning model designed in this paper assists in formulating treatment plans and evaluating treatment risks and patient status through continuous interaction with patient trajectory. So that this model has the foresight that the supervising-deep- learning model does not have. Main Results The experiment shows that, in the test set, for the TSRM, the mortality is the lowest when actually implemented treatment strategy is the same as the AI-recommended. For the RAM, it can accurately grasp the deterioration trend of the patients, reasonably assess the risk of the patient's status and the treatment plans at an early stage. The assessment results of the model were matched with the actual clinical records. Significance A DRL-based sepsis adjuvant therapy model is proposed. It can prospectively assist physicians in proposing treatment strategies, early assessment the risk of patient status and treatment methods, and detecting deterioration trends in advance.

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence can use physiological parameters to optimize treatment strategies and predict clinical deterioration of sepsis in ICU

et al. 2023

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“…In [12], an event-driven strategy was proposed to improve the optimal control of HVAC systems. Fu et al [13] reviewed in detail the application of reinforcement learning in building energy efficiency. Meanwhile, more specifically, model-free deep reinforcement learning (DRL) combining deep learning and reinforcement learning has received tremendous attention in the HVAC optimal control problem.…”

Section: Introductionmentioning

confidence: 99%

MAQMC: Multi-Agent Deep Q-Network for Multi-Zone Residential HVAC Control

Ding¹,

Fu²,

Chen³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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The optimization of multi-zone residential heating, ventilation, and air conditioning (HVAC) control is not an easy task due to its complex dynamic thermal model and the uncertainty of occupant-driven cooling loads. Deep reinforcement learning (DRL) methods have recently been proposed to address the HVAC control problem. However, the application of single-agent DRL for multi-zone residential HVAC control may lead to non-convergence or slow convergence. In this paper, we propose MAQMC (Multi-Agent deep Q-network for multi-zone residential HVAC Control) to address this challenge with the goal of minimizing energy consumption while maintaining occupants' thermal comfort. MAQMC is divided into MAQMC2 (MAQMC with two agents:one agent controls the temperature of each zone, and the other agent controls the humidity of each zone) and MAQMC3 (MAQMC with three agents:three agents control the temperature and humidity of three zones, respectively). The experimental results show that MAQMC3 can reduce energy consumption by 6.27% and MAQMC2 by 3.73% compared with the xed point; compared with the rule-based, MAQMC3 and MAQMC2 respectively can reduce 61.89% and 59.07% comfort violation. In addition, experiments with di erent regional weather data demonstrate that the well-trained MAQMC RL agents have the robustness and adaptability to unknown environments.

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Machine Learning in Urban Building Energy Modeling

Abbasabadi,

Ashayeri

2024

Artificial Intelligence in Performance‐Driven Design

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Applications of reinforcement learning for building energy efficiency control: A review

Cited by 37 publications

References 45 publications

Artificial intelligence can use physiological parameters to optimize treatment strategies and predict clinical deterioration of sepsis in ICU

Artificial intelligence can use physiological parameters to optimize treatment strategies and predict clinical deterioration of sepsis in ICU

MAQMC: Multi-Agent Deep Q-Network for Multi-Zone Residential HVAC Control

Machine Learning in Urban Building Energy Modeling

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