JournalTOCs

IEEE Transactions on Neural Networks and Learning Systems
Journal Prestige (SJR): 3.406

Citation Impact (citeScore): 9
Number of Followers: 57

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2162-237X
Published by IEEE

[228 journals]

IEEE Computational Intelligence Society Information
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  Pages: C3 - C3
  Abstract: null
  PubDate: MON, 05 AUG 2024 09:16:50 -04
  Issue No: Vol. 35, No. 8 (2024)
IEEE Transactions on Neural Networks and Learning Systems Publication
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  Pages: C2 - C2
  Abstract: null
  PubDate: MON, 05 AUG 2024 09:16:49 -04
  Issue No: Vol. 35, No. 8 (2024)
IEEE Transactions on Neural Networks and Learning Systems Information for
Authors
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  Pages: C4 - C4
  Abstract: null
  PubDate: MON, 05 AUG 2024 09:16:48 -04
  Issue No: Vol. 35, No. 8 (2024)
ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural
Network Model for Short-Term Load Forecasting
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  Authors: Slawek Smyl;Grzegorz Dudek;Paweł Pełka;
  Pages: 11346 - 11358
  Abstract: Short-term load forecasting (STLF) is challenging due to complex time series (TS) which express three seasonal patterns and a nonlinear trend. This article proposes a novel hybrid hierarchical deep-learning (DL) model that deals with multiple seasonality and produces both point forecasts and predictive intervals (PIs). It combines exponential smoothing (ES) and a recurrent neural network (RNN). ES extracts dynamically the main components of each individual TS and enables on-the-fly deseasonalization, which is particularly useful when operating on a relatively small dataset. A multilayer RNN is equipped with a new type of dilated recurrent cell designed to efficiently model both short and long-term dependencies in TS. To improve the internal TS representation and thus the model’s performance, RNN learns simultaneously both the ES parameters and the main mapping function transforming inputs into forecasts. We compare our approach against several baseline methods, including classical statistical methods and machine learning (ML) approaches, on STLF problems for 35 European countries. The empirical study clearly shows that the proposed model has high expressive power to solve nonlinear stochastic forecasting problems with TS including multiple seasonality and significant random fluctuations. In fact, it outperforms both statistical and state-of-the-art ML models in terms of accuracy.
  PubDate: THU, 31 AUG 2023 10:03:16 -04
  Issue No: Vol. 35, No. 8 (2023)

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