Optimal demand response strategy of commercial building‐based virtual power plant using reinforcement learning-Reference-Cited by-同舟云学术

Optimal demand response strategy of commercial building‐based virtual power plant using reinforcement learning

Author:

Chen Tao¹^ORCID,Cui Qiushi²,Gao Ciwei¹,Hu Qinran¹^ORCID,Lai Kexing³,Yang Jianlin⁴,Lyu Ran⁵,Zhang Hao⁶,Zhang Jinyuan⁷

Affiliation:

1. School of Electrical Engineering Southeast University Nanjing China

2. Department of Electrical and Computer Engineering Arizona State University Tempe Arizona USA

3. Optimization Division Hitachi ABB Power Grids San Jose California USA

4. Wind Power Industry Innovation Center State Power Investment Corporation Limited's (SPIC) Shanghai China

5. Economic Technology Research Institute State Grid Corporation of China‐Shanghai Company Shanghai China

6. Department of Technology and Management Tengtian Energy Saving Technology Corporation Limited's Shanghai China

7. Administration Office Shanghai Huangpu District Development and Reform Commission Shanghai China

Funder

Science and Technology Commission of Shanghai Municipality

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology,Control and Systems Engineering

Link

Reference31 articles.

1. Day‐ahead building‐level load forecasts using deep learning vs;Cai M.;traditional time‐series techniques. Appl. Energy,2019

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