Demand Charge Control for Energy-intensive Enterprises based on Deep Reinforcement Learning
Author:
Affiliation:
1. Xi’an Jiaotong University,State Key Laboratory of Mechanical Manufacturing System Engineering,Xi’an,China
2. Xi’an Jiaotong University,The Moe Klinns Laboratory,Xi’an,China
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9727224/9727236/09728428.pdf?arnumber=9728428
Reference11 articles.
1. Analysis and Research on Power Demand Control of Iron and Steel Enterprises Based on Process Network;hao;Proceedings of the 2013 National Power Consumption and Power Saving Technology Seminar,2013
2. Research on the two-part electricity price mechanism under the low-carbon transition of the power industry [J];han;China Power Enterprise Management,2021
3. Stochastic demand charge management for commercial and industrial buildings
4. A Comprehensive Review of Deep Reinforcement Learning for Object Detection
5. Real-Time Smart Charging of Electric Vehicles for Demand Charge Reduction at Non-Residential Sites
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