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Energies 2015, 8(11), 13047-13061; doi:10.3390/en81112355

Adaptive Neuro-Fuzzy Inference Systems as a Strategy for Predicting and Controling the Energy Produced from Renewable Sources

Automation, Computer Science and Electrical Engineering Department, Valahia University of Târgoviște, 2 Carol I Bd., Targoviste 130024, Romania
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Academic Editor: Stefan Gößling-Reisemann
Received: 3 July 2015 / Revised: 6 November 2015 / Accepted: 9 November 2015 / Published: 17 November 2015
(This article belongs to the Special Issue Resilience of Energy Systems)
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Abstract

The challenge for our paper consists in controlling the performance of the future state of a microgrid with energy produced from renewable energy sources. The added value of this proposal consists in identifying the most used criteria, related to each modeling step, able to lead us to an optimal neural network forecasting tool. In order to underline the effects of users’ decision making on the forecasting performance, in the second part of the article, two Adaptive Neuro-Fuzzy Inference System (ANFIS) models are tested and evaluated. Several scenarios are built by changing: the prediction time horizon (Scenario 1) and the shape of membership functions (Scenario 2). View Full-Text
Keywords: forecasting; neural network; Adaptive Neuro-Fuzzy Inference Systems; renewable energy sources forecasting; neural network; Adaptive Neuro-Fuzzy Inference Systems; renewable energy sources
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Elena Dragomir, O.; Dragomir, F.; Stefan, V.; Minca, E. Adaptive Neuro-Fuzzy Inference Systems as a Strategy for Predicting and Controling the Energy Produced from Renewable Sources. Energies 2015, 8, 13047-13061.

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