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Title: Fuzzy Integration of Support Vector Regression Models for Anticipatory Control of Complex Energy Systems

Anticipatory control systems are a class of systems whose decisions are based on predictions for the future state of the system under monitoring. Anticipation denotes intelligence and is an inherent property of humans that make decisions by projecting in future. Likewise, artificially intelligent systems equipped with predictive functions may be utilized for anticipating future states of complex systems, and therefore facilitate automated control decisions. Anticipatory control of complex energy systems is paramount to their normal and safe operation. In this paper a new intelligent methodology integrating fuzzy inference with support vector regression is introduced. Our proposed methodology implements an anticipatory system aiming at controlling energy systems in a robust way. Initially a set of support vector regressors is adopted for making predictions over critical system parameters. Furthermore, the predicted values are fed into a two stage fuzzy inference system that makes decisions regarding the state of the energy system. The inference system integrates the individual predictions into a single one at its first stage, and outputs a decision together with a certainty factor computed at its second stage. The certainty factor is an index of the significance of the decision. The proposed anticipatory control system is tested on a realmore » world set of data obtained from a complex energy system, describing the degradation of a turbine. Results exhibit the robustness of the proposed system in controlling complex energy systems.« less
Authors:
 [1] ;  [2]
  1. Purdue Univ., West Lafayette, IN (United States). School of Nuclear Engineering
  2. Idaho National Lab. (INL), Idaho Falls, ID (United States). Dept. of Human Factors, Controls and Statistics
Publication Date:
Report Number(s):
INL/JOU-14-33370
Journal ID: ISSN 2166-7241
Grant/Contract Number:
AC07-05ID14517
Type:
Accepted Manuscript
Journal Name:
International Journal of Monitoring and Surveillance Technologies Research
Additional Journal Information:
Journal Volume: 2; Journal Issue: 2; Journal ID: ISSN 2166-7241
Research Org:
Idaho National Lab. (INL), Idaho Falls, ID (United States)
Sponsoring Org:
USDOE Office of Nuclear Energy (NE)
Country of Publication:
United States
Language:
English
Subject:
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; anticipatory control; energy systems; fuzzy inference; support vector regression
OSTI Identifier:
1367854

Alamaniotis, Miltiadis, and Agarwal, Vivek. Fuzzy Integration of Support Vector Regression Models for Anticipatory Control of Complex Energy Systems. United States: N. p., Web. doi:10.4018/ijmstr.2014040102.
Alamaniotis, Miltiadis, & Agarwal, Vivek. Fuzzy Integration of Support Vector Regression Models for Anticipatory Control of Complex Energy Systems. United States. doi:10.4018/ijmstr.2014040102.
Alamaniotis, Miltiadis, and Agarwal, Vivek. 2014. "Fuzzy Integration of Support Vector Regression Models for Anticipatory Control of Complex Energy Systems". United States. doi:10.4018/ijmstr.2014040102. https://www.osti.gov/servlets/purl/1367854.
@article{osti_1367854,
title = {Fuzzy Integration of Support Vector Regression Models for Anticipatory Control of Complex Energy Systems},
author = {Alamaniotis, Miltiadis and Agarwal, Vivek},
abstractNote = {Anticipatory control systems are a class of systems whose decisions are based on predictions for the future state of the system under monitoring. Anticipation denotes intelligence and is an inherent property of humans that make decisions by projecting in future. Likewise, artificially intelligent systems equipped with predictive functions may be utilized for anticipating future states of complex systems, and therefore facilitate automated control decisions. Anticipatory control of complex energy systems is paramount to their normal and safe operation. In this paper a new intelligent methodology integrating fuzzy inference with support vector regression is introduced. Our proposed methodology implements an anticipatory system aiming at controlling energy systems in a robust way. Initially a set of support vector regressors is adopted for making predictions over critical system parameters. Furthermore, the predicted values are fed into a two stage fuzzy inference system that makes decisions regarding the state of the energy system. The inference system integrates the individual predictions into a single one at its first stage, and outputs a decision together with a certainty factor computed at its second stage. The certainty factor is an index of the significance of the decision. The proposed anticipatory control system is tested on a real world set of data obtained from a complex energy system, describing the degradation of a turbine. Results exhibit the robustness of the proposed system in controlling complex energy systems.},
doi = {10.4018/ijmstr.2014040102},
journal = {International Journal of Monitoring and Surveillance Technologies Research},
number = 2,
volume = 2,
place = {United States},
year = {2014},
month = {4}
}