Adaptive Neuro-Fuzzy Inference Systems as a Strategy for Predicting and Controling the Energy Produced from Renewable Sources
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 neu...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-11-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1996-1073/8/11/12355 |
| _version_ | 1798039839605522432 |
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| author | Otilia Elena Dragomir Florin Dragomir Veronica Stefan Eugenia Minca |
| author_facet | Otilia Elena Dragomir Florin Dragomir Veronica Stefan Eugenia Minca |
| author_sort | Otilia Elena Dragomir |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-04-11T21:59:13Z |
| format | Article |
| id | doaj.art-205f84e956e748b69b5f7724c47e7ef3 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T21:59:13Z |
| publishDate | 2015-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-205f84e956e748b69b5f7724c47e7ef32022-12-22T04:01:00ZengMDPI AGEnergies1996-10732015-11-01811130471306110.3390/en81112355en81112355Adaptive Neuro-Fuzzy Inference Systems as a Strategy for Predicting and Controling the Energy Produced from Renewable SourcesOtilia Elena Dragomir0Florin Dragomir1Veronica Stefan2Eugenia Minca3Automation, Computer Science and Electrical Engineering Department, Valahia University of Târgoviște, 2 Carol I Bd., Targoviste 130024, RomaniaAutomation, Computer Science and Electrical Engineering Department, Valahia University of Târgoviște, 2 Carol I Bd., Targoviste 130024, RomaniaAutomation, Computer Science and Electrical Engineering Department, Valahia University of Târgoviște, 2 Carol I Bd., Targoviste 130024, RomaniaAutomation, Computer Science and Electrical Engineering Department, Valahia University of Târgoviște, 2 Carol I Bd., Targoviste 130024, RomaniaThe 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).http://www.mdpi.com/1996-1073/8/11/12355forecastingneural networkAdaptive Neuro-Fuzzy Inference Systemsrenewable energy sources |
| spellingShingle | Otilia Elena Dragomir Florin Dragomir Veronica Stefan Eugenia Minca Adaptive Neuro-Fuzzy Inference Systems as a Strategy for Predicting and Controling the Energy Produced from Renewable Sources Energies forecasting neural network Adaptive Neuro-Fuzzy Inference Systems renewable energy sources |
| title | Adaptive Neuro-Fuzzy Inference Systems as a Strategy for Predicting and Controling the Energy Produced from Renewable Sources |
| title_full | Adaptive Neuro-Fuzzy Inference Systems as a Strategy for Predicting and Controling the Energy Produced from Renewable Sources |
| title_fullStr | Adaptive Neuro-Fuzzy Inference Systems as a Strategy for Predicting and Controling the Energy Produced from Renewable Sources |
| title_full_unstemmed | Adaptive Neuro-Fuzzy Inference Systems as a Strategy for Predicting and Controling the Energy Produced from Renewable Sources |
| title_short | Adaptive Neuro-Fuzzy Inference Systems as a Strategy for Predicting and Controling the Energy Produced from Renewable Sources |
| title_sort | adaptive neuro fuzzy inference systems as a strategy for predicting and controling the energy produced from renewable sources |
| topic | forecasting neural network Adaptive Neuro-Fuzzy Inference Systems renewable energy sources |
| url | http://www.mdpi.com/1996-1073/8/11/12355 |
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