Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review

Antonopoulos, Ioannis; Robu, Valentin; Couraud, Benoit; Kirli, Desen; Norbu, Sonam; Kiprakis, Aristides; Flynn, David; Elizondo-Gonzalez, Sergio; Wattam, Steve

doi:10.1016/j.rser.2020.109899

Cited by 313 publications

(145 citation statements)

References 137 publications

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“…Moreover, they have proven to be effective methods to capture hidden and nonlinear pattern among data [49,50]. RL algorithms which employ ANNs, and in particular Deep Neural Networks (DNN), are identified as Deep Reinforcement Learning (DRL) [51].…”

Section: Deep Reinforcement Learningmentioning

confidence: 99%

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

et al. 2021

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Recently, a growing interest has been observed in HVAC control systems based on Artificial Intelligence, to improve comfort conditions while avoiding unnecessary energy consumption. In this work, a model-free algorithm belonging to the Deep Reinforcement Learning (DRL) class, Soft Actor-Critic, was implemented to control the supply water temperature to radiant terminal units of a heating system serving an office building. The controller was trained online, and a preliminary sensitivity analysis on hyperparameters was performed to assess their influence on the agent performance. The DRL agent with the best performance was compared to a rule-based controller assumed as a baseline during a three-month heating season. The DRL controller outperformed the baseline after two weeks of deployment, with an overall performance improvement related to control of indoor temperature conditions. Moreover, the adaptability of the DRL agent was tested for various control scenarios, simulating changes of external weather conditions, indoor temperature setpoint, building envelope features and occupancy patterns. The agent dynamically deployed, despite a slight increase in energy consumption, led to an improvement of indoor temperature control, reducing the cumulative sum of temperature violations on average for all scenarios by 75% and 48% compared to the baseline and statically deployed agent respectively.

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Section: Deep Reinforcement Learningmentioning

confidence: 99%

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

et al. 2021

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“…The growing penetration of renewables has created new challenges in the operation and management of power systems, also considering the pervasiveness of information and communication technologies (ICT), real-time monitoring and control devices, advanced metering infrastructures, etc. This digitalization trend is expected to generate massive amounts of data which require sophisticated data handling techniques [26]. Also, from a modeling point of view, new challenges are created due to the increasing number of actors and the dynamics of their interactions in decentralized energy systems, as LEC, marked by a noticeable socio-technical dimension [27].…”

Section: Distributed Artificial Intelligence In Energy Modelingmentioning

confidence: 99%

“…Also, from a modeling point of view, new challenges are created due to the increasing number of actors and the dynamics of their interactions in decentralized energy systems, as LEC, marked by a noticeable socio-technical dimension [27]. In this setting, Artificial Intelligence (AI) has been identified as key to deal with modeling and decision support [26,28]. Distributed Artificial Intelligence (DAI) is a subfield of AI which is based on the interactions of intelligent agents capable of making decisions to achieve goals while co-habiting in an environment populated by other agents [29].…”

Section: Distributed Artificial Intelligence In Energy Modelingmentioning

confidence: 99%

Assessing the Influence of Different Goals in Energy Communities’ Self-Sufficiency—An Optimized Multiagent Approach

et al. 2021

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Understanding to what extent the emergence of prosumers and prosumagers organized in energy communities can impact the organization and operation of power grids has been one of the major recent research avenues at the European level. In renewable-based communities aiming to reach some level of energy self-sufficiency, a key issue to be addressed is assessing how the presence of end-users playing different roles in the system (self-consuming, producing and trading, performing demand management, etc.) can influence the overall system performance. In this setting, this paper combines Distributed Artificial Intelligence and optimization approaches to assess how prosumagers and consumers pursuing different goals can influence the energy self-sufficiency of a local energy community. The residential demand is accurately modeled, and the agents’ preferences are considered in the modeling to represent a smart community. The results show that although energy community members may have conflicting individual goals, the overall system self-sufficiency can be maximized with economic benefits for all stakeholders, thus illustrating the advantages of energy communities.

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“…An extensive work on RL and DRL applications in power systems have been presented in [99][100][101]. [101] focuses an overview of RL methods with emphasis on demand response applications. RL methods have been used for the control of standalone MGs [102][103][104][105].…”

Section: B Reinforcement Learningmentioning

confidence: 99%

The Evolution of Research in Microgrids Control

Vasilakis

Zafeiratou

Lagos

et al. 2020

IEEE Open J. Power Energy

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Microgrids (MGs), as novel paradigms of active Distribution Networks, have been gaining increasing interest by the research community in the last 20 years. Currently, they are considered as key components in power system decentralization, providing viable solutions for rural electrification, enhancing resilience and supporting local energy communities. Their main characteristic is the coordinated control of the interconnected distributed energy resources (DER), which can be realized by various methods, ranging from decentralized communicationfree approaches to centralized ones, where decisions are taken at a central point. This paper provides an overview of this development focusing on the technical control solutions proposed by reseachers for the various levels of MG organization hierarchy. A critical assessment of selected, popular technologies is provided and open research questions regarding the trend to more decentralized power systems are discussed.

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Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review

Cited by 313 publications

References 137 publications

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

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

Assessing the Influence of Different Goals in Energy Communities’ Self-Sufficiency—An Optimized Multiagent Approach

The Evolution of Research in Microgrids Control

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