|1/2014 - 2|
A Combined Methodology of Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm for Short-term Energy ForecastingKAMPOUROPOULOS, K. , ANDRADE, F. , GARCIA, A. , ROMERAL, L.
|Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (682 KB) | Citation | Downloads: 772 | Views: 3,269|
adaptive neuro-fuzzy inference system, energy forecast, genetic algorithm, intelligent energy management systems
energy(13), systems(9), load(7), neural(6), fuzzy(6), applications(6), term(5), short(5), optimization(5), network(5)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2014-02-28
Volume 14, Issue 1, Year 2014, On page(s): 9 - 14
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.01002
Web of Science Accession Number: 000332062300002
SCOPUS ID: 84894611007
This document presents an energy forecast methodology using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithms (GA). The GA has been used for the selection of the training inputs of the ANFIS in order to minimize the training result error. The presented algorithm has been installed and it is being operating in an automotive manufacturing plant. It periodically communicates with the plant to obtain new information and update the database in order to improve its training results. Finally the obtained results of the algorithm are used in order to provide a short-term load forecasting for the different modeled consumption processes.
Web of Science® Times Cited: 10 [View]
View record in Web of Science® [View]
View Related Records® [View]
SCOPUS® Times Cited: 15
View record in SCOPUS® [Free preview]
 Integrated Forecasting Using the Discrete Wavelet Theory and Artificial Intelligence Techniques to Reduce the Bullwhip Effect in a Supply Chain, Singh, Lakhwinder Pal, Challa, Ravi Teja, Global Journal of Flexible Systems Management, ISSN 0972-2696, Issue 2, Volume 17, 2016.
Digital Object Identifier: 10.1007/s40171-015-0115-z [CrossRef]
 Industrial Time Series Modelling by Means of the Neo-Fuzzy Neuron, Zurita, Daniel, Delgado, Miguel, Carino, Jesus A., Ortega, Juan A., Clerc, Guy, IEEE Access, ISSN 2169-3536, Issue , 2016.
Digital Object Identifier: 10.1109/ACCESS.2016.2611649 [CrossRef]
 New modified CHB multilevel inverter topology with elimination of lower and higher order harmonics, Chabni, Fayçal, Taleb, Rachid, Lakhedar, Abdelhak, Bounadja, Mohammed, Automatika, ISSN 0005-1144, Issue 1, Volume 59, 2018.
Digital Object Identifier: 10.1080/00051144.2018.1484549 [CrossRef]
 A Multi-objective PMU Placement Method Considering Observability and Measurement Redundancy using ABC Algorithm, KULANTHAISAMY, A., VAIRAMANI, R., KARUNAMURTHI, N. K., KOODALSAMY, C., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 2, Volume 14, 2014.
Digital Object Identifier: 10.4316/AECE.2014.02020 [CrossRef] [Full text]
 Estimation of DSI log parameters from conventional well log data using a hybrid particle swarm optimization–adaptive neuro-fuzzy inference system, Zahmatkesh, Iman, Soleimani, Bahman, Kadkhodaie, Ali, Golalzadeh, Alireza, Abdollahi, AliAkbar- Moussavi, Journal of Petroleum Science and Engineering, ISSN 0920-4105, Issue , 2017.
Digital Object Identifier: 10.1016/j.petrol.2017.08.002 [CrossRef]
 A Novel Evolutionary Genetic Optimization-Based Adaptive Neuro-Fuzzy Inference System and Geographical Information Systems Predict and Map Soil Organic Carbon Stocks Across an Afromontane Landscape, WERE, Kennedy O., TIEN BUI, Dieu, DICK, Øystein Bjarne, SINGH, Bal Ram, Pedosphere, ISSN 1002-0160, Issue 5, Volume 27, 2017.
Digital Object Identifier: 10.1016/S1002-0160(17)60461-2 [CrossRef]
 Prediction of lung nicotine concentration based on novel GA-ANFIS system approach, Begic Fazlic, Lejla, Avdagic, Aja, 2015 XXV International Conference on Information, Communication and Automation Technologies (ICAT), ISBN 978-1-4673-8146-8, 2015.
Digital Object Identifier: 10.1109/ICAT.2015.7340520 [CrossRef]
 Optimization of a combined cool, heat and power plant based on genetic algorithms and specialized software, Hopulele, Eugen, Gavrilas, Mihai, 2014 International Conference and Exposition on Electrical and Power Engineering (EPE), ISBN 978-1-4799-5849-8, 2014.
Digital Object Identifier: 10.1109/ICEPE.2014.6970065 [CrossRef]
 Smart multi-model approach based on adaptive Neuro-Fuzzy Inference Systems and Genetic Algorithms, Sala, Enric, Kampouropoulos, Konstantinos, Giacometto, Francisco, Romeral, Luis, IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society, ISBN 978-1-4799-4032-5, 2014.
Digital Object Identifier: 10.1109/IECON.2014.7048513 [CrossRef]
 Multi-carrier optimal power flow of energy hubs by means of ANFIS and SQP, Kampouropoulos, Konstantinos, Andrade, Fabio, Sala, Enric, Espinosa, Antonio Garcia, Romeral, Luis, IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, ISBN 978-1-5090-3474-1, 2016.
Digital Object Identifier: 10.1109/IECON.2016.7793570 [CrossRef]
 GA-ANFIS expert system prototype for detection of tar content in the manufacturing process, Fazlic, Lejla Begic, Avdagic, Zikrija, Besic, Ingmar, 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), ISBN 978-9-5323-3082-3, 2015.
Digital Object Identifier: 10.1109/MIPRO.2015.7160457 [CrossRef]
 Optimal control of energy hub systems by use of SQP algorithm and energy prediction, Kampouropoulos, Konstantinos, Andrade, Fabio, Sala, Enric, Romeral, Luis, IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society, ISBN 978-1-4799-4032-5, 2014.
Digital Object Identifier: 10.1109/IECON.2014.7048503 [CrossRef]
Disclaimer: All information displayed above was retrieved by using remote connections to respective databases. For the best user experience, we update all data by using background processes, and use caches in order to reduce the load on the servers we retrieve the information from. As we have no control on the availability of the database servers and sometimes the Internet connectivity may be affected, we do not guarantee the information is correct or complete. For the most accurate data, please always consult the database sites directly. Some external links require authentication or an institutional subscription.
Web of Science® is a registered trademark of Clarivate Analytics, Scopus® is a registered trademark of Elsevier B.V., other product names, company names, brand names, trademarks and logos are the property of their respective owners.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.