Data Driven Energy Management of Residential PV-Battery System Using Q-Learning-Reference-Cited by-同舟云学术

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Data Driven Energy Management of Residential PV-Battery System Using Q-Learning

Published:2023-11-08 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE)
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Author:

Baberwal Krishna¹,Yadav Anshul Kumar²,Saini Vikash Kumar³,Lamba Ravita¹,Kumar Rajesh¹

Affiliation:

1. Malaviya National Institute of Technology,Department of Electrical Engineering,Jaipur,India

2. Central Electronics Engineering Research Institute, Council of Scientific & Industrial Research,Pilani,India

3. Center for Energy and Environment, Malaviya National Institute of Technology,Jaipur,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10363431/10363424/10363461.pdf?arnumber=10363461

Reference22 articles.

1. Uncertainty aware optimal battery sizing for cloud energy storage in community microgrid

2. A multi-agent deep reinforcement learning approach enabled distributed energy management schedule for the coordinate control of multi-energy hub with gas, electricity, and freshwater

3. Hybrid energy storage approach for renewable energy applications

4. Cloud Energy Storage Based Embedded Battery Technology Architecture for Residential Users Cost Minimization

5. Energy Consumption Cost Benefits through Smart Home Energy Management in Residential Buildings: An Indian Case Study

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1. Optimal Electric Vehicle Battery Management Using Q-learning for Sustainability;Sustainability;2024-08-21

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