Physics-Informed LSTM Network for Flexibility Identification in Evaporative Cooling System
Author:
Affiliation:
1. IDLab, Ghent University–imec, Ghent, Belgium
2. Department of Electromechanical, Systems and Metal Engineering, Ghent University, Core lab EEDT-DC, Flanders Make, Ghent, Belgium
Funder
Agentschap Innoveren Ondernemen VLAIO - European Unions Horizon 2020 research and innovation program for the project BIGG
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/9424/9989328/09771395.pdf?arnumber=9771395
Reference24 articles.
1. Real-Time Identification of Power Fluctuations Based on LSTM Recurrent Neural Network: A Case Study on Singapore Power System
2. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
3. Deep lagrangian networks: Using physics as model prior for deep learning;lutter,2019
4. Thermal Performance Evaluation of an Induced Draft Evaporative Cooling System through Adaptive Neuro-Fuzzy Interference System (ANFIS) Model and Mathematical Model
5. Application of demand response programs to heavy industries: A case study on a regional electric company;nejad;Int J Smart Elect Eng,2017
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