An application of reinforcement learning to residential energy storage under real-time pricing-Reference-Cited by-同舟云学术

An application of reinforcement learning to residential energy storage under real-time pricing

Published:2021-12-05 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia)
language:
Short-container-title:

Author:

Brock Eli¹,Bruckstein Lauren²,Connor Patrick¹,Nguyen Sabrina¹,Kerestes Robert¹,Abdelhakim Mai¹

Affiliation:

1. Electrical and Computer Engineering University of Pittsburgh,Pittsburgh,USA

2. Computer Science University of Pittsburgh,Pittsburgh,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9715538/9715267/09715712.pdf?arnumber=9715712

Reference23 articles.

1. Real-time electricity pricing for industrial customers: Survey and case studies in the United States

2. Lessons learned from smart grid enabled pricing programs

3. Real Time Pricing and Electricity Markets;allcott;Group,2009

4. Home energy management system for the residential load control based on the price prediction;ren;2011 IEEE Online Conference on Green Communications GreenCom,0

5. Price Prediction in Real-Time Electricity;leon-garcia;IEEE Transactions on Smart Grid,2010

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An overview of reinforcement learning-based approaches for smart home energy management systems with energy storages;Renewable and Sustainable Energy Reviews;2024-09

2. Optimal Scheduled Control Operation of Battery Energy Storage System using Model-Free Reinforcement Learning;2022 IEEE Sustainable Power and Energy Conference (iSPEC);2022-12-04