Kriging Empirical Mode Decomposition via support vector machine learning technique for autonomous operation diagnosing of CHP in microgrid-Reference-Cited by-同舟云学术

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Kriging Empirical Mode Decomposition via support vector machine learning technique for autonomous operation diagnosing of CHP in microgrid

Published:2018-12 Issue: Volume:145 Page:58-70
ISSN:1359-4311
Container-title:Applied Thermal Engineering
language:en
Short-container-title:Applied Thermal Engineering

Author:

Shao Zehui,Wakil Karzan,Usak Muhammet,Amin Heidari Mohammad,Wang Bo,Simoes Rolando

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Energy Engineering and Power Technology

Reference37 articles.

1. Fuzzy-based heat and power hub models for cost-emission operation of an industrial consumer using compromise programming;Khodaei;Appl. Therm. Eng.,2018

2. A two-stage optimization and control for CCHP microgrid energy management;Luo;Appl. Therm. Eng.,2017

3. Optimum design of a hybrid PV–CSP–LPG microgrid with particle swarm optimization technique;Sigarchian;Appl. Therm. Eng.,2016

4. A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on Mixed Integer Genetic Algorithm;Hamian;Eng. Appl. Artif. Intell.,2018

5. Real-time implementation of Kalman filter for unsteady flow measurement in a pipe;Sanada;Int. J. Hydromechatronics,2018

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