Online Implementation of a Soft Actor-Critic Agent to Enhance Indoor Temperature Control and Energy Efficiency in Buildings

Coraci, Davide; Brandi, Silvio; Piscitelli, Marco Savino; Capozzoli, Alfonso

doi:10.3390/en14040997

Cited by 32 publications

(17 citation statements)

References 46 publications

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“…Application Objective Building Type Algorithm [141], [142] Other/Mixed Cost Residential DQN [143] Cost & Load Balance [94] EV, ES, and RG Cost [144] Other [145] Cost & Comfort [146] HVAC, Fans, WH Cost [147] Other/Mixed Commercial [148] Cost & Comfort [149], [150] HVAC, Fans, WH Mixed/NA [151], [152] Other/Mixed Cost [153], [154] P2P Trading Other Mixed/NA [163] EV, ES, and RG [164] Other/Mixed Cost [165] Cost & Comfort Residential TRPO [51], [168], [169], [170] Other/Mixed [171], [172] Cost & Load Balance [173] Cost [174] EV, ES, and RG [175] Other/Mixed Cost & Comfort Academic [176] Other [177], [178] EV, ES, and RG Commercial [179], [180], [181] HVAC, Fans, WH Cost & Comfort Mixed/NA [182], [183], [184] EV, ES, and RG Other [185], [186] Other/Mixed Cost & Load Balance Residential SAC [187], [188] HVAC, Fans, WH Cost Commercial [189], [190],…”

Section: Referencementioning

confidence: 99%

Reinforcement Learning: Theory and Applications in HEMS

Alani¹,

Das²

2022

Preprint

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The twin capabilities of learning from experience and learning at higher levels of abstraction, set reinforcement learning apart from other areas of machine learning and (within the broader context) all of artificial intelligence. It allows algorithmic agents to replace human beings in the real world, including in homes and buildings, in application domains that had hitherto been considered to be beyond today’s capabilities. This goal, specifically aimed at home energy automation that forms the backdrop of this article, which surveys the use of deep reinforcement learning in various HEMS applications. The article provides an overview of generic reinforcement learning. This is followed with discussions on the state-of-the-art methods for value based, policy gradient, and actor-critic methods in deep reinforcement learning. In order to make published literature in reinforcement learning more accessible to HEMS researchers, verbal descriptions are accompanied with explanatory figures as well as mathematical expressions using the same terminology as the machine learning community. Next, a detailed survey of how reinforcement learning is used in different HEMS domains is described. The survey also considers what kind of reinforcement learning algorithms are used in each HEMS application. The survey suggests that this research is still in its infancy.

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Section: Referencementioning

confidence: 99%

Reinforcement Learning: Theory and Applications in HEMS

Alani¹,

Das²

2022

Preprint

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“…Building Type Algorithm [152,153] Other/Mixed Cost Residential DQN [154] Cost and Load Balance [105] EV, ES, and RG Cost [155] Other [156] Cost and Comfort [157] HVAC, Fans, WH Cost [158] Other/Mixed Commercial [159] Cost and Comfort [160,161] EV, ES, and RG [175] Other/Mixed Cost [176] Cost and Comfort Residential TRPO Other/Mixed [182,183] Cost and Load Balance [184] Cost [185] EV, ES, and RG [186] Other/Mixed Cost and Comfort Academic [187] Other [188,189] EV, ES, and RG Commercial [190][191][192] HVAC, Fans, WH Cost and Comfort Mixed/NA [193][194][195] EV, ES, and RG Other [196,197] Other/Mixed Cost and Load Balance Residential SAC [198,199] HVAC, Fans, WH Cost Commercial [103,[200][201][202] Cost and Comfort [203] Other/Mixed [204] Academic [205][206][207] HVAC, Fans, WH Cost and Load Balance Mixed/NA [208][209][210]<...…”

Section: Reference Application Objectivementioning

confidence: 99%

Reinforcement Learning: Theory and Applications in HEMS

Alani¹,

Das²

2022

Preprint

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The steep rise in reinforcement learning (RL) in various applications in energy as well as the penetration of home automation in recent years are the motivation for this article. It surveys the use of RL in various home energy management system (HEMS) applications. There is a focus on deep neural network (DNN) models in RL. The article provides an overview of reinforcement learning. This is followed with discussions on state-of-the-art methods for value, policy, and actor–critic methods in deep reinforcement learning (DRL). In order to make the published literature in reinforcement learning more accessible to the HEMS community, verbal descriptions are accompanied with explanatory figures as well as mathematical expressions using standard machine learning terminology. Next, a detailed survey of how reinforcement learning is used in different HEMS domains is described. The survey also considers what kind of reinforcement learning algorithms are used in each HEMS application. It suggests that research in this direction is still in its infancy. Lastly, the article proposes four performance metrics to evaluate RL methods.

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“…SAC uses a stochastic policy which enhances the stability with more stochastic exploration and faster convergence, yielding robust policies. Recently, SAC algorithms have been shown to have applications in building energy management [30], energy management of Electric vehicles with a hybrid energy storage systems [31]. Zhang et al have proposed a SAC based control algorithm for minimizing the operational costs and ensuring power reliability in an integrated power, heat and natural-gas system [32].…”

Section: Introductionmentioning

confidence: 99%

TASAC: a twin-actor reinforcement learning framework with stochastic policy for batch process control

Joshi¹,

Kodamanaa²,

Kandath³

et al. 2022

Preprint

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Due to their complex nonlinear dynamics and batch-to-batch variability, batch processes pose a challenge for process control. Due to the absence of accurate models and resulting plant-model mismatch, these problems become harder to address for advanced model-based control strategies. Reinforcement Learning (RL), wherein an agent learns the policy by directly interacting with the environment, offers a potential alternative in this context. RL frameworks with actor-critic architecture have recently become popular for controlling systems where state and action spaces are continuous. It has been shown that an ensemble of actor and critic networks further helps the agent learn better policies due to the enhanced exploration due to simultaneous policy learning. To this end, the current study proposes a stochastic actor-critic RL algorithm, termed Twin Actor Soft Actor-Critic (TASAC), by incorporating an ensemble of actors for learning, in a maximum entropy framework, for batch process control.

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Online Implementation of a Soft Actor-Critic Agent to Enhance Indoor Temperature Control and Energy Efficiency in Buildings

Cited by 32 publications

References 46 publications

Reinforcement Learning: Theory and Applications in HEMS

Reinforcement Learning: Theory and Applications in HEMS

Reinforcement Learning: Theory and Applications in HEMS

TASAC: a twin-actor reinforcement learning framework with stochastic policy for batch process control

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