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

Energies

Otilia Elena Dragomir²  Florin Dragomir²  Veronica Stefan¹  Eugenia Minca² 

[1] Automation, Computer Science and Electrical Engineering Department, Valahia University of Târgoviște, 2 Carol I Bd., Targoviste 130024, Romania

关键词: forecasting;    neural network;    Adaptive Neuro-Fuzzy Inference Systems;    renewable energy sources;   

DOI  :  10.3390/en81112355

来源: mdpi

【 摘 要 】

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).

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.