Research on Short Term Power Load Forecasting Based on Wavelet and BiLSTM
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-53401-0_7
Reference12 articles.
1. Nti, I.K., Teimeh, M., Nyarko-Boateng, O., et al.: Electricity load forecasting: a systematic review. J. Electr. Syst. Inf. Technol. 7(1), 1–19 (2020)
2. Ghoushchi, S.J., Manjili, S., Mardani, A., et al.: An extended new approach for forecasting short-term wind power using modified fuzzy wavelet neural network: a case study in wind power plant. Energy 223, 120052 (2021)
3. Gong, M., Wang, J., Bai, Y., et al.: Heat load prediction of residential buildings based on discrete wavelet transform and tree-based ensemble learning. J. Build. Eng. 32, 101455 (2020)
4. Zhang, L., Alahmad, M., Wen, J.: Comparison of time-frequency-analysis techniques applied in building energy data noise cancellation for building load forecasting: a real-building case study. Energy Build. 231, 110592 (2021)
5. Bouktif, S., Fiaz, A., Ouni, A., et al.: Multi-sequence LSTM-RNN deep learning and metaheuristics for electric load forecasting. Energies 13(2), 391 (2020)
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