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

Otilia Elena Dragomir, Florin Dragomir, Veronica Stefan and Eugenia Minca
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Otilia Elena Dragomir: Automation, Computer Science and Electrical Engineering Department, Valahia University of Târgoviște, 2 Carol I Bd., Targoviste 130024, Romania
Florin Dragomir: Automation, Computer Science and Electrical Engineering Department, Valahia University of Târgoviște, 2 Carol I Bd., Targoviste 130024, Romania
Veronica Stefan: Automation, Computer Science and Electrical Engineering Department, Valahia University of Târgoviște, 2 Carol I Bd., Targoviste 130024, Romania
Eugenia Minca: Automation, Computer Science and Electrical Engineering Department, Valahia University of Târgoviște, 2 Carol I Bd., Targoviste 130024, Romania

Energies, 2015, vol. 8, issue 11, 1-15

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).

Keywords: forecasting; neural network; Adaptive Neuro-Fuzzy Inference Systems; renewable energy sources (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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