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Authors: Stefano Leonori ; Alessio Martino ; Antonello Rizzi and Fabio Massimo Frattale Mascioli

Affiliation: University of Rome "La Sapienza", Italy

ISBN: 978-989-758-274-5

Keyword(s): Smart Grids, Microgrids, Energy Management System, ANFIS, Data Clustering, Decision Making System.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Fuzzy Systems ; Learning and Adaptive Fuzzy Systems ; Soft Computing

Abstract: Microgrids (MGs) play a crucial role for the development of Smart Grids. They are conceived to intelligently integrate the generation from Distributed Energy Resources, to improve Demand Response (DR) services, to reduce pollutant emissions and curtail power losses, assuring the continuity of services to the loads as well. In this work it is proposed a novel synthesis procedure for modelling an Adaptive Neuro-Fuzzy Inference System (ANFIS) featured by multivariate Gaussian Membership Functions (MFs) and first order Takagi-Sugeno rules. The Fuzzy Rule Base is the core inference engine of an Energy Management System (EMS) for a gridconnected MG equipped with a photovoltaic power plant, an aggregated load and an Energy Storage System (ESS). The EMS is designed to operate in real time by defining the ESS energy flow in order to maximize the revenues generated by the energy trade with the distribution grid. The ANFIS EMS is synthesized through a data driven approach that relies on a cluste ring algorithm which defines the MFs and the rule consequent hyperplanes. Moreover, three clustering algorithms are investigated. Results show that the adoption of kmedoids based on Mahalanobis (dis)similarity measure is more efficient with respect to the k-means, although affected by some variety in clusters composition. (More)

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Paper citation in several formats:
Leonori, S.; Martino, A.; Rizzi, A. and Frattale Mascioli, F. (2017). ANFIS Synthesis by Clustering for Microgrids EMS Design.In Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI, ISBN 978-989-758-274-5, pages 328-337. DOI: 10.5220/0006514903280337

@conference{ijcci17,
author={Stefano Leonori. and Alessio Martino. and Antonello Rizzi. and Fabio Massimo Frattale Mascioli.},
title={ANFIS Synthesis by Clustering for Microgrids EMS Design},
booktitle={Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,},
year={2017},
pages={328-337},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006514903280337},
isbn={978-989-758-274-5},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,
TI - ANFIS Synthesis by Clustering for Microgrids EMS Design
SN - 978-989-758-274-5
AU - Leonori, S.
AU - Martino, A.
AU - Rizzi, A.
AU - Frattale Mascioli, F.
PY - 2017
SP - 328
EP - 337
DO - 10.5220/0006514903280337

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