Evolutive ANFIS training for energy load profile forecast for an IEMS in an automated factory

In this paper an evolutive algorithm is used to train an adaptative-network-based fuzzy inference system (ANFIS), particularly a genetic algorithm (GA). The GA is able to train the antecedent and consequent parameters of an ANFIS, which is used for energy load profile forecasting in an automated fac...

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Bibliographic Details
Main Authors: Cardenas, J. J., Garcia, A., Romeral, J. L., Kampouropoulos, K.
Format: Conference Proceeding
Language:English
Subjects:
Adaptative-network-based fuzzy inference system
Algorismes genètics
ANFIS
Automatització i control de l'energia elèctrica
Biological cells
Energia elèctrica
Energies
Fuzzy systems
Genetic algorithms
Polynomials
Production
Signal processing algorithms
Training
Àrees temàtiques de la UPC
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Summary:In this paper an evolutive algorithm is used to train an adaptative-network-based fuzzy inference system (ANFIS), particularly a genetic algorithm (GA). The GA is able to train the antecedent and consequent parameters of an ANFIS, which is used for energy load profile forecasting in an automated factory. This load forecasting is useful to support an intelligent energy management system (IEMS), which enables the user to optimize the energy consumptions by means of getting the optimal work points, scheduling the production according to these points, etc. The proposed training algorithm showed excellent results with complex plants like industrial energy consumers in the user-side, where the randomness of the loads is higher than in utility loads. Real data from an automated car factory were used to test the presented algorithms. Appropriated results were obtained.
ISSN:1946-0740
1946-0759
DOI:10.1109/ETFA.2011.6059079