Enhancing Electric Vehicle Charging with Dynamic Adaptation: A Machine Learning Approach to Improving Grid Alignment Precision

Rajani, D; Manjunatha,; Gopal, J Vijay; Saxena, Abhishek; Salman, Zahraa N.; Jain, Alok

doi:10.1109/ic3se62002.2024.10593543

2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE) 2024

DOI: 10.1109/ic3se62002.2024.10593543

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Enhancing Electric Vehicle Charging with Dynamic Adaptation: A Machine Learning Approach to Improving Grid Alignment Precision

D Rajani,

Manjunatha,

J Vijay Gopal

et al.

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2024

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Review and Evaluation of Multi-Agent Control Applications for Energy Management in Buildings

Michailidis,

Kosmatopoulos

2024

Energies

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The current paper presents a comprehensive review analysis of Multi-agent control methodologies for Integrated Building Energy Management Systems (IBEMSs), considering combinations of multi-diverse equipment such as Heating, Ventilation, and Air conditioning (HVAC), domestic hot water (DHW), lighting systems (LS), renewable energy sources (RES), energy storage systems (ESS) as well as electric vehicles (EVs), integrated at the building level. Grounded in the evaluation of key control methodologies—such as Model Predictive Control (MPC) and reinforcement learning (RL) along with their synergistic hybrid integration—the current study integrates a large number of impactful applications of the last decade and evaluates their contribution to the field of energy management in buildings. To this end, over seventy key scholarly papers from the 2014–2024 period have been integrated and analyzed to provide a holistic evaluation on different areas of interest, including the utilized algorithms, agent interactions, energy system types, building typologies, application types and simulation tools. Moreover, by analyzing the latest advancements in the field, a fruitful trend identification is conducted in the realm of multi-agent control for IBEMS frameworks, highlighting the most prominent solutions to achieve sustainability and energy efficiency.

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Review and Evaluation of Multi-Agent Control Applications for Energy Management in Buildings

Michailidis,

Kosmatopoulos

2024

Energies

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scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Enhancing Electric Vehicle Charging with Dynamic Adaptation: A Machine Learning Approach to Improving Grid Alignment Precision

Cited by 1 publication

References 44 publications

Review and Evaluation of Multi-Agent Control Applications for Energy Management in Buildings

Review and Evaluation of Multi-Agent Control Applications for Energy Management in Buildings

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