Whole building energy model for HVAC optimal control: A practical framework based on deep reinforcement learning

Zhang, Zhiang; Chong, Adrian; Pan, Yaokun; Zhang, Chenlu; Lam, Khee Poh

doi:10.1016/j.enbuild.2019.07.029

Cited by 233 publications

(98 citation statements)

References 39 publications

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“…Since there are many DRL methods in the [47]. For most of existing works on DRL for building energy systems, model-free methods have been used and can be further classified into several types as in [48] [63], Advantage Actor-Critic (A2C) [64], Asynchronous Advantage Actor-Critic (A3C) [65]), and maximum entropy methods (e.g., Multi-Actor Attention-Critic (MAAC) [17], Entropy-Based Collective Advantage Actor-Critic (EB-C-A2C) [27], Entropy-Based Collective Deep Q-Network (EB-C-DQN) [27]). In above-mentioned methods, Q-learning methods do not support continuous actions.…”

Section: Drl Classificationmentioning

confidence: 99%

“…In model-based methods, DRL agents need to learn building environment models based on historical data Long Short-Term Memory-Deep Deterministic Policy Gradients (LSTM-DDPG) [46], differentiable mode policy-Proximal Policy Optimization (differentiable MPC policy-PPO) [47]. [63], Advantage [64], Asynchronous Advantage Actor-Critic (A3C) [65]), and maximum entropy methods (e.g., Multi-Ac (MAAC) [17], Entropy-Based Collective Advantage Actor-Critic (EB-C-A2C) [27], Entropy-Based Collecti (EB-C-DQN) [27]). In above-mentioned methods, Q-learning methods do not support continuous actions.…”

Section: Applications Of Drl In a Single Buildingmentioning

confidence: 99%

“…Secondly, it may take a long time for the DRL agent to learn an optimal policy if trained in a real-world environment [46] [47]. To reduce the dependency on a real building environment, many modelbased DRL control methods have been developed [46] [65]. For example, Zhang et al [65] proposed and implemented a Building Energy Model (BEM)-based DRL control framework for a novel radiant heating system in an existing office building.…”

Section: Applications Of Drl In a Single Buildingmentioning

confidence: 99%

“…To reduce the dependency on a real building environment, many modelbased DRL control methods have been developed [46] [65]. For example, Zhang et al [65] proposed and implemented a Building Energy Model (BEM)-based DRL control framework for a novel radiant heating system in an existing office building. The proposed control framework consists of four steps as shown in Fig.…”

Section: Applications Of Drl In a Single Buildingmentioning

confidence: 99%

“…Experimental results show that the obtained DRL-based control strategy can reduce 16.7% heating demand with more than 95% probability compared with the rule-based control strategy. Since building energy models are calibrated based on the observed data in a short period of time (e.g., three months), the DRL agent's exposure to real-world HVAC operational data is limited [65]. To overcome this drawback, Zou et al [46] proposed a DRL-based HVAC control framework to minimize energy consumption while maintaining thermal comfort levels for occupants based on operational data within two years.…”

Section: Applications Of Drl In a Single Buildingmentioning

confidence: 99%

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Deep Reinforcement Learning for Smart Home Energy Management

Xie

et al. 2020

IEEE Internet Things J.

283

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In this paper, we investigate an energy cost minimization problem for a smart home in the absence of a building thermal dynamics model with the consideration of a comfortable temperature range. Due to the existence of model uncertainty, parameter uncertainty (e.g., renewable generation output, nonshiftable power demand, outdoor temperature, and electricity price) and temporally-coupled operational constraints, it is very challenging to determine the optimal energy management strategy for scheduling Heating, Ventilation, and Air Conditioning (HVAC) systems and energy storage systems in the smart home. To address the challenge, we first formulate the above problem as a Markov decision process, and then propose an energy management strategy based on Deep Deterministic Policy Gradients (DDPG). It is worth mentioning that the proposed strategy does not require the prior knowledge of uncertain parameters and building thermal dynamics model. Simulation results based on real-world traces demonstrate the effectiveness and robustness of the proposed strategy.

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Section: Drl Classificationmentioning

confidence: 99%

Section: Applications Of Drl In a Single Buildingmentioning

confidence: 99%

Section: Applications Of Drl In a Single Buildingmentioning

confidence: 99%

Section: Applications Of Drl In a Single Buildingmentioning

confidence: 99%

Section: Applications Of Drl In a Single Buildingmentioning

confidence: 99%

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Deep Reinforcement Learning for Smart Home Energy Management

Xie

et al. 2020

IEEE Internet Things J.

283

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Deep learning and reinforcement learning approach on microgrid

Kumar¹,

Kandasamy

Ramanathan

2020

Int Trans Electr Energ Syst

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Microgrid is a new era in the power system and it has more scope of investigation on research. Due to an increase in demand and future expansion of the power system, analyzing the complexities of the network becomes a challenging task. Artificial intelligence plays a vital role in resolving such issues in a microgrid in various aspects. Owing to the rapid growth of periodical update in computational cost reduction, enhanced data analysis-based algorithm artificial intelligence enters into new epoch Artificial Intelligence AI 2.0. Based on such approach, machine learning has been evolved as AI 2.0 initially. Now, it develops branches like deep learning, reinforcement learning, and a combination of both deep reinforcement learning algorithms. These algorithms are precise to attain higher priority in decision-making under a complex network. This paper deals with numerous challenges of the above algorithm to state the significance of AI 2.0 and summarization of their application toward microgrid is useful to analyze the power system. K E Y W O R D S deep learning, deep reinforcement learning, microgrid, reinforcement learning 1 | INTRODUCTION Microgrid is a cluster of distributed generators (DG), stored energy system, local loads along with protecting and monitoring devices. Microgrid has its own specialty in operating under two modes of operation: autonomous and grid-connected mode, mode selection attained by the use of static transfer switch that bridges operation selection in microgrid. The presence of multiple DGs creates a distribution access problem in multipoint is transformed into a concentrated

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Reinforcement Learning for Intelligent Building Energy Management System Control *

Kotevska

Andelfinger

2022

Intelligent Data Mining and Analysis in Power and Energy Systems

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Whole building energy model for HVAC optimal control: A practical framework based on deep reinforcement learning

Cited by 233 publications

References 39 publications

Deep Reinforcement Learning for Smart Home Energy Management

Deep Reinforcement Learning for Smart Home Energy Management

Deep learning and reinforcement learning approach on microgrid

Reinforcement Learning for Intelligent Building Energy Management System Control *

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