Fuzzy Integration of Support Vector Regression Models for Anticipatory Control of Complex Energy Systems
Abstract
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 »
- Authors:
-
- Purdue Univ., West Lafayette, IN (United States). School of Nuclear Engineering
- Idaho National Lab. (INL), Idaho Falls, ID (United States). Dept. of Human Factors, Controls and Statistics
- Publication Date:
- Research Org.:
- Idaho National Laboratory (INL), Idaho Falls, ID (United States)
- Sponsoring Org.:
- USDOE Office of Nuclear Energy (NE)
- OSTI Identifier:
- 1367854
- Report Number(s):
- INL/JOU-14-33370
Journal ID: ISSN 2166-7241
- Grant/Contract Number:
- AC07-05ID14517
- Resource 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
- Publisher:
- IGI Publishing
- 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
Citation Formats
Alamaniotis, Miltiadis, and Agarwal, Vivek. Fuzzy Integration of Support Vector Regression Models for Anticipatory Control of Complex Energy Systems. United States: N. p., 2014.
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. https://doi.org/10.4018/ijmstr.2014040102
Alamaniotis, Miltiadis, and Agarwal, Vivek. Tue .
"Fuzzy Integration of Support Vector Regression Models for Anticipatory Control of Complex Energy Systems". United States. https://doi.org/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 = {Tue Apr 01 00:00:00 EDT 2014},
month = {Tue Apr 01 00:00:00 EDT 2014}
}
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Works referenced in this record:
Anticipatory monitoring and control of complex energy systems using a fuzzy based fusion of support vector regressors
conference, July 2014
- Alamaniotis, Miltiadis; Agarwal, Vivek; Jevremovic, Tatjana
- 2014 5th International Conference on Information, Intelligence, Systems and Applications (IISA), IISA 2014, The 5th International Conference on Information, Intelligence, Systems and Applications
Expert System for Decision Making and Instructing Nuclear Resonance Fluorescence Cargo Interrogation
conference, November 2009
- Alamaniotis, M.; Gao, R.; Tsoukalas, L. H.
- 2009 21st IEEE International Conference on Tools with Artificial Intelligence (ICTAI)
Regression to fuzziness method for estimation of remaining useful life in power plant components
journal, October 2014
- Alamaniotis, Miltiadis; Grelle, Austin; Tsoukalas, Lefteri H.
- Mechanical Systems and Signal Processing, Vol. 48, Issue 1-2
Optimal Assembly of Support Vector Regressors with Application to System Monitoring
journal, December 2012
- Alamaniotis, Miltiadis; Ikonomopoulos, Andreas; Tsoukalas, Lefteri H.
- International Journal on Artificial Intelligence Tools, Vol. 21, Issue 06
Fuzzy logic for control of roll and moment for a flexible wing aircraft
journal, June 1991
- Chiu, S.; Chand, S.; Moore, D.
- IEEE Control Systems, Vol. 11, Issue 4, p. 42-48
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
book, January 2013
- Cristianini, Nello; Shawe-Taylor, John
Intelligent Control
book, March 1993
- Harris, C. J.; Moore, C. G.; Brown, M.
- World Scientific Series in Robotics and Intelligent Systems
Integration of Neural Networks with Fuzzy Reasoning for Measuring Operational Parameters in a Nuclear Reactor
journal, October 1993
- Ikonomopoulos, Andreas; Tsoukalas, Lefteri H.; Uhrig, Robert E.
- Nuclear Technology, Vol. 104, Issue 1
Anticipatory Control of Wind Turbines With Data-Driven Predictive Models
journal, September 2009
- Kusiak, Andrew; Song, Zhe; Zheng, Haiyang
- IEEE Transactions on Energy Conversion, Vol. 24, Issue 3
Parameter simulation in performance monitoring system of steam turbine unit for a fossil-fuel power plant
conference, January 2002
- Guang-Lin Liu,
- 2002 International Conference on Machine Learning and Cybernetics, Proceedings. International Conference on Machine Learning and Cybernetics
A hybrid expert system-neural networks methodology for anticipatory control in a process environment
conference, January 1990
- Tsoukalas, L.; Reyes-Jimenez, J.
- Proceedings of the third international conference on Industrial and engineering applications of artificial intelligence and expert systems - IEA/AIE '90
Neurofuzzy approaches to anticipation: a new paradigm for intelligent systems
journal, January 1998
- Tsoukalas, L. H.
- IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), Vol. 28, Issue 4
Multi-agent-based anticipatory control for enhancing the safety and performance of Generation-IV nuclear power plants during long-term semi-autonomous operation
journal, January 2003
- Uhrig, Robert E.; Tsoukalas, Lefteri H.
- Progress in Nuclear Energy, Vol. 43, Issue 1-4
Application of neural networks and fuzzy systems to power plants
conference, January 1994
- Uhrig, R. E.; Tsoukalas, L. H.; Ikonomopoulos, A.
- Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)
Intelligent infrastructures: distributed intelligence in transport system control - an illustrative example
conference, January 2004
- van Dam, K. H.; Ottjes, J. A.; Lodewijks, G.
- 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583)
A neurofuzzy approach for the anticipatory control of complex systems
conference, January 1996
- Xinqing, L.; Tsoukalas, L. H.; Uhrig, R. E.
- Proceedings of IEEE 5th International Fuzzy Systems
A hybrid expert system-neural networks methodology for anticipatory control in a process environment
conference, January 1990
- Tsoukalas, L.; Reyes-Jimenez, J.
- Proceedings of the third international conference on Industrial and engineering applications of artificial intelligence and expert systems - IEA/AIE '90
Works referencing / citing this record:
Integration of Gaussian Processes and Particle Swarm Optimization for Very-Short Term Wind Speed Forecasting in Smart Power
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- Alamaniotis, Miltiadis; Karagiannis, Georgios
- International Journal of Monitoring and Surveillance Technologies Research, Vol. 5, Issue 3