Energy Use Forecasting with the Use of a Nested Structure Based on Fuzzy Cognitive Maps and Artificial Neural Networks
Katarzyna Poczeta () and
Elpiniki I. Papageorgiou
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Katarzyna Poczeta: Department of Applied Computer Science, Kielce University of Technology, 25314 Kielce, Poland
Elpiniki I. Papageorgiou: Department of Energy Systems, Faculty of Technology, University of Thessaly, Geopolis, 41500 Larisa, Greece
Energies, 2022, vol. 15, issue 20, 1-18
Abstract:
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.
Keywords: nested structure; energy use forecasting; fuzzy cognitive maps; artificial neural networks; long short-term memory networks (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: 2022
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Citations: View citations in EconPapers (3)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:20:p:7542-:d:940998
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