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Combining embeddings and fuzzy time series for high-dimensional time series forecasting in internet of energy applications

Author

Listed:
  • Bitencourt, Hugo Vinicius
  • de Souza, Luiz Augusto Facury
  • dos Santos, Matheus Cascalho
  • Silva, Rodrigo
  • de Lima e Silva, Petrônio Cândido
  • Guimarães, Frederico Gadelha

Abstract

High-dimensional time series increasingly arise in the Internet of Energy (IoE), given the use of multi-sensor environments and the two way communication between energy consumers and the smart grid. Therefore, methods that are capable of computing high-dimensional time series are of great value in smart building and IoE applications. Fuzzy Time Series (FTS) models stand out as data-driven non-parametric models of easy implementation and high accuracy. Unfortunately, the existing FTS models can be unfeasible if all variables were used to train the model. We present a new methodology named Embedding Fuzzy Time Series (EFTS), by applying a combination of data embedding transformation and FTS methods. The EFTS is an explainable and data-driven approach, which is flexible and adaptable for many smart building and IoE applications. The experimental results with three public datasets show that our methodology outperforms several machine learning based forecasting methods (LSTM, GRU, TCN, RNN, MLP and GBM), and demonstrates the accuracy and parsimony of the EFTS in comparison to the baseline methods and the results previously published in the literature, showing an enhancement greater than 80%. Therefore, EFTS has a great value in high-dimensional time series forecasting in IoE applications.

Suggested Citation

  • Bitencourt, Hugo Vinicius & de Souza, Luiz Augusto Facury & dos Santos, Matheus Cascalho & Silva, Rodrigo & de Lima e Silva, Petrônio Cândido & Guimarães, Frederico Gadelha, 2023. "Combining embeddings and fuzzy time series for high-dimensional time series forecasting in internet of energy applications," Energy, Elsevier, vol. 271(C).
    • Handle: RePEc:eee:energy:v:271:y:2023:i:c:s0360544223004668
    DOI: 10.1016/j.energy.2023.127072
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    References listed on IDEAS

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    1. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    2. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    3. Reka, S. Sofana & Dragicevic, Tomislav, 2018. "Future effectual role of energy delivery: A comprehensive review of Internet of Things and smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 90-108.
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    1. Nagireddy Venkata Rajasekhar Reddy & Pydimarri Padmaja & Miroslav Mahdal & Selvaraj Seerangan & Vrince Vimal & Vamsidhar Talasila & Lenka Cepova, 2023. "Hybrid Fuzzy Rule Algorithm and Trust Planning Mechanism for Robust Trust Management in IoT-Embedded Systems Integration," Mathematics, MDPI, vol. 11(11), pages 1-18, June.
    2. Orang, Omid & de Lima e Silva, Petrônio Cândido & Guimarães, Frederico Gadelha, 2023. "Multi-output time series forecasting with randomized multivariate Fuzzy Cognitive Maps," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).

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