Research on Load Forecasting Method Considering Data Feature Analysis Based on Bi-LSTM Network
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Publisher
Springer Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-0852-1_20
Reference11 articles.
1. Khatoon, S., Ibraheem, A., Singh, K., et al.: Analysis and comparison of various methods available for load forecasting: an overview. In: 2014 Innovative Applications of Computational Intelligence on Power Energy and Controls with their impact on Humanity (CIPECH), pp. 243–247 (2014).https://doi.org/10.1109/CIPECH.2014.7019112
2. Mustapha, M., Mustafa, M.W., Khalid, S.N., et al.: Classification of electricity load forecasting based on the factors influencing the load consumption and methods used: an-overview. IEEE Conf. Energy Convers. (CENCON) 2015, 442–447 (2015). https://doi.org/10.1109/CENCON.2015.7409585
3. Zhang, L., Xu, L.: Forecasting of fluctuations and turning points of power demand in China based on the maximum entropy method and ARMA model. In: 2010 5th International Conference on Critical Infrastructure (CRIS), pp. 1–6 (2010). https://doi.org/10.1109/CRIS.2010.5617508
4. He, Y., Xu, Q.: Short-term power load forecasting based on self-adapting PSO-BP neural network model. In: Fourth International Conference on Computational and Information Sciences, pp. 1096–1099 (2012).https://doi.org/10.1109/ICCIS.2012.279
5. Kong, W., Dong, Z.Y., Jia, Y., et al.: Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Trans. Smart Grid 10(1), 841–851 (2019). https://doi.org/10.1109/TSG.2017.2753802
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