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Hybrid Long Short-Term Memory Wavelet Transform Models for Short-Term Electricity Load Forecasting

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  • Agbassou Guenoukpati

    (Centre d’Excellence Régional pour la Maîtrise de l’Electricité (CERME), Université de Lomé, Lome P.O. Box 1515, Togo
    Laboratoire de Recherche en Sciences de l’Ingénieur (LARSI), Department of Electrical Engineering, Ecole Polytechnique de Lomé (EPL), Université de Lomé, Lome P.O. Box 1515, Togo)

  • Akuété Pierre Agbessi

    (Centre d’Excellence Régional pour la Maîtrise de l’Electricité (CERME), Université de Lomé, Lome P.O. Box 1515, Togo
    Laboratoire de Recherche en Sciences de l’Ingénieur (LARSI), Department of Electrical Engineering, Ecole Polytechnique de Lomé (EPL), Université de Lomé, Lome P.O. Box 1515, Togo)

  • Adekunlé Akim Salami

    (Centre d’Excellence Régional pour la Maîtrise de l’Electricité (CERME), Université de Lomé, Lome P.O. Box 1515, Togo
    Laboratoire de Recherche en Sciences de l’Ingénieur (LARSI), Department of Electrical Engineering, Ecole Polytechnique de Lomé (EPL), Université de Lomé, Lome P.O. Box 1515, Togo)

  • Yawo Amen Bakpo

    (Centre d’Excellence Régional pour la Maîtrise de l’Electricité (CERME), Université de Lomé, Lome P.O. Box 1515, Togo)

Abstract

To ensure the constant availability of electrical energy, power companies must consistently maintain a balance between supply and demand. However, electrical load is influenced by a variety of factors, necessitating the development of robust forecasting models. This study seeks to enhance electricity load forecasting by proposing a hybrid model that combines Sorted Coefficient Wavelet Decomposition with Long Short-Term Memory (LSTM) networks. This approach offers significant advantages in reducing algorithmic complexity and effectively processing patterns within the same class of data. Various models, including Stacked LSTM, Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network—Long Short-Term Memory (CNN-LSTM), and Convolutional Long Short-Term Memory (ConvLSTM), were compared and optimized using grid search with cross-validation on consumption data from Lome, a city in Togo. The results indicate that the ConvLSTM model outperforms its counterparts based on Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and correlation coefficient (R 2 ) metrics. The ConvLSTM model was further refined using wavelet decomposition with coefficient sorting, resulting in the WT+ConvLSTM model. This proposed approach significantly narrows the gap between actual and predicted loads, reducing discrepancies from 10–50 MW to 0.5–3 MW. In comparison, the WT+ConvLSTM model surpasses Autoregressive Integrated Moving Average (ARIMA) models and Multilayer Perceptron (MLP) type artificial neural networks, achieving a MAPE of 0.485%, an RMSE of 0.61 MW, and an R 2 of 0.99. This approach demonstrates substantial robustness in electricity load forecasting, aiding stakeholders in the energy sector to make more informed decisions.

Suggested Citation

  • Agbassou Guenoukpati & Akuété Pierre Agbessi & Adekunlé Akim Salami & Yawo Amen Bakpo, 2024. "Hybrid Long Short-Term Memory Wavelet Transform Models for Short-Term Electricity Load Forecasting," Energies, MDPI, vol. 17(19), pages 1-21, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:19:p:4914-:d:1490057
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    References listed on IDEAS

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    1. Vahid Nourani & Mehdi Komasi & Akira Mano, 2009. "A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(14), pages 2877-2894, November.
    2. Zhang, Jinliang & Wei, Yi-Ming & Li, Dezhi & Tan, Zhongfu & Zhou, Jianhua, 2018. "Short term electricity load forecasting using a hybrid model," Energy, Elsevier, vol. 158(C), pages 774-781.
    3. Soares, Lacir J. & Medeiros, Marcelo C., 2008. "Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data," International Journal of Forecasting, Elsevier, vol. 24(4), pages 630-644.
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