A novel energy management system (EMS) synthesis procedure based on adaptive neurofuzzy inference systems (ANFISs) by hyperplane clustering is investigated in this paper. In particular, since it is known that clustering input–output samples in hyperplane space does not consider clusters’ separability in the input space, a Min–Max classifier is applied to properly cut and update those hyperplanes defined on scattered clusters in order to refine the ANFIS membership functions. Furthermore, three different clustering techniques have been compared for the ANFIS rule synthesis as well, both with and without considering the classifier support. The procedure under analysis has been applied for designing a microgrid EMS equipped with a photovoltaic generator and an energy storage system (ESS). The EMS is in charge of intelligently defining how to redistribute the prosumer energy balance between the ESS and the connected grid in order to maximize the profit generated by the energy exchange with the grid, assuming a time of use energy price policy. Results on real-world data show very interesting performances, close to optimal values evaluated with a mixed integer linear programming problem formulation by approximately 12%. Moreover, the contribution of the Min–Max classifier improves the EMS performance by approximately 50% with respect to the same algorithm without refining fuzzy rules by the classification step.

ANFIS microgrid energy management system synthesis by hyperplane clustering supported by neurofuzzy min–max classifier / Leonori, Stefano; Martino, Alessio; Mascioli, Fabio Massimo Frattale; Rizzi, Antonello. - In: IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE. - ISSN 2471-285X. - 3:3(2019), pp. 193-204. [10.1109/TETCI.2018.2880815]

ANFIS microgrid energy management system synthesis by hyperplane clustering supported by neurofuzzy min–max classifier

Martino, Alessio;
2019

Abstract

A novel energy management system (EMS) synthesis procedure based on adaptive neurofuzzy inference systems (ANFISs) by hyperplane clustering is investigated in this paper. In particular, since it is known that clustering input–output samples in hyperplane space does not consider clusters’ separability in the input space, a Min–Max classifier is applied to properly cut and update those hyperplanes defined on scattered clusters in order to refine the ANFIS membership functions. Furthermore, three different clustering techniques have been compared for the ANFIS rule synthesis as well, both with and without considering the classifier support. The procedure under analysis has been applied for designing a microgrid EMS equipped with a photovoltaic generator and an energy storage system (ESS). The EMS is in charge of intelligently defining how to redistribute the prosumer energy balance between the ESS and the connected grid in order to maximize the profit generated by the energy exchange with the grid, assuming a time of use energy price policy. Results on real-world data show very interesting performances, close to optimal values evaluated with a mixed integer linear programming problem formulation by approximately 12%. Moreover, the contribution of the Min–Max classifier improves the EMS performance by approximately 50% with respect to the same algorithm without refining fuzzy rules by the classification step.
2019
optimal scheduling
microgrids
renewable energy sources
smart grids
real-time systems
job shop scheduling
adaptive neurofuzzy inference systems
hyperplane clustering
hierarchical clustering
min–max neurofuzzy classifiers
microgrids
energy management
renewable energy sources
energy storage systems
ANFIS microgrid energy management system synthesis by hyperplane clustering supported by neurofuzzy min–max classifier / Leonori, Stefano; Martino, Alessio; Mascioli, Fabio Massimo Frattale; Rizzi, Antonello. - In: IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE. - ISSN 2471-285X. - 3:3(2019), pp. 193-204. [10.1109/TETCI.2018.2880815]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11385/214557
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