Energy Use Forecasting with the Use of a Nested Structure Based on Fuzzy Cognitive Maps and Artificial Neural Networks
The aim of this paper is to present a novel approach to energy use forecasting. We propose a nested fuzzy cognitive map in which each concept at a higher level can be decomposed into another fuzzy cognitive map, multilayer perceptron artificial neural network or long short-term memory network. Histo...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/20/7542 |
| _version_ | 1797473605093687296 |
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| author | Katarzyna Poczeta Elpiniki I. Papageorgiou |
| author_facet | Katarzyna Poczeta Elpiniki I. Papageorgiou |
| author_sort | Katarzyna Poczeta |
| collection | DOAJ |
| description | The aim of this paper is to present a novel approach to energy use forecasting. We propose a nested fuzzy cognitive map in which each concept at a higher level can be decomposed into another fuzzy cognitive map, multilayer perceptron artificial neural network or long short-term memory network. Historical data related to energy consumption are used to construct a nested fuzzy cognitive map in order to better understand energy use behavior. Through the experiments, the usefulness of the nested structure in energy demand prediction is demonstrated, by calculating three popular metrics: Mean Square Error, Mean Absolute Error and the correlation coefficient. A comparative analysis is performed, applying classic multilayer perceptron artificial neural networks, long short-term memory networks and fuzzy cognitive maps. The results confirmed that the proposed approach outperforms the classic methods in terms of prediction accuracy. Moreover, the advantage of the proposed approach is the ability to present complex time series in the form of a clear nested structure presenting the main concepts influencing energy consumption on the first level. The second level allows for more detailed problem analysis and lower forecast errors. |
| first_indexed | 2024-03-09T20:17:53Z |
| format | Article |
| id | doaj.art-d31666d7c49c4c608057190f2e059c44 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T20:17:53Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-d31666d7c49c4c608057190f2e059c442023-11-23T23:56:25ZengMDPI AGEnergies1996-10732022-10-011520754210.3390/en15207542Energy Use Forecasting with the Use of a Nested Structure Based on Fuzzy Cognitive Maps and Artificial Neural NetworksKatarzyna Poczeta0Elpiniki I. Papageorgiou1Department of Applied Computer Science, Kielce University of Technology, 25314 Kielce, PolandDepartment of Energy Systems, Faculty of Technology, University of Thessaly, Geopolis, 41500 Larisa, GreeceThe aim of this paper is to present a novel approach to energy use forecasting. We propose a nested fuzzy cognitive map in which each concept at a higher level can be decomposed into another fuzzy cognitive map, multilayer perceptron artificial neural network or long short-term memory network. Historical data related to energy consumption are used to construct a nested fuzzy cognitive map in order to better understand energy use behavior. Through the experiments, the usefulness of the nested structure in energy demand prediction is demonstrated, by calculating three popular metrics: Mean Square Error, Mean Absolute Error and the correlation coefficient. A comparative analysis is performed, applying classic multilayer perceptron artificial neural networks, long short-term memory networks and fuzzy cognitive maps. The results confirmed that the proposed approach outperforms the classic methods in terms of prediction accuracy. Moreover, the advantage of the proposed approach is the ability to present complex time series in the form of a clear nested structure presenting the main concepts influencing energy consumption on the first level. The second level allows for more detailed problem analysis and lower forecast errors.https://www.mdpi.com/1996-1073/15/20/7542nested structureenergy use forecastingfuzzy cognitive mapsartificial neural networkslong short-term memory networks |
| spellingShingle | Katarzyna Poczeta Elpiniki I. Papageorgiou Energy Use Forecasting with the Use of a Nested Structure Based on Fuzzy Cognitive Maps and Artificial Neural Networks Energies nested structure energy use forecasting fuzzy cognitive maps artificial neural networks long short-term memory networks |
| title | Energy Use Forecasting with the Use of a Nested Structure Based on Fuzzy Cognitive Maps and Artificial Neural Networks |
| title_full | Energy Use Forecasting with the Use of a Nested Structure Based on Fuzzy Cognitive Maps and Artificial Neural Networks |
| title_fullStr | Energy Use Forecasting with the Use of a Nested Structure Based on Fuzzy Cognitive Maps and Artificial Neural Networks |
| title_full_unstemmed | Energy Use Forecasting with the Use of a Nested Structure Based on Fuzzy Cognitive Maps and Artificial Neural Networks |
| title_short | Energy Use Forecasting with the Use of a Nested Structure Based on Fuzzy Cognitive Maps and Artificial Neural Networks |
| title_sort | energy use forecasting with the use of a nested structure based on fuzzy cognitive maps and artificial neural networks |
| topic | nested structure energy use forecasting fuzzy cognitive maps artificial neural networks long short-term memory networks |
| url | https://www.mdpi.com/1996-1073/15/20/7542 |
| work_keys_str_mv | AT katarzynapoczeta energyuseforecastingwiththeuseofanestedstructurebasedonfuzzycognitivemapsandartificialneuralnetworks AT elpinikiipapageorgiou energyuseforecastingwiththeuseofanestedstructurebasedonfuzzycognitivemapsandartificialneuralnetworks |