An Effective N-BEATS Network Model for Short Term Load Forecasting
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-53401-0_21
Reference20 articles.
1. Oreshkin, B.N., et al.: N-BEATS neural network for mid-term electricity load forecasting. Appl. Energy 293, 116918 (2021)
2. Hadjout, D., Torres, J.F., Troncoso, A., Sebaa, A., Martínez-Álvarez, F.: Electricity consumption forecasting based on ensemble deep learning with application to the algerian market. Energy 243, 123060 (2022)
3. Santhosh, M., Venkaiah, C., Vinod Kumar, D.M.: Current advances and approaches in wind speed and wind power forecasting for improved renewable energy integration: a review. Eng. Rep. 2(6), e12178 (2020)
4. Donald, I.I., Cios, K.J.: Time series forecasting by combining RBF networks, certainty factors, and the box-Jenkins model. Neurocomputing 10(2), 149–168 (1996)
5. Mbamalu, G., El-Hawary, M.E.: Load forecasting via suboptimal seasonal autoregressive models and iteratively reweighted least squares estimation. IEEE Trans. Power Syst. 8(1), 343–348 (1993)
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