Prediction of Electricity Consumption Demand Based on Long-Short Term Memory Network
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-9833-3_12
Reference15 articles.
1. Jacob, M., Neves, C., Vukadinović Greetham, D.: Forecasting and assessing risk of individual electricity peaks. Springer Nature (2020).
2. Torres, J.F., Martínez-Álvarez, F., Troncoso, A.: A deep LSTM network for the Spanish electricity consumption forecasting. Neural Comput. Appl. 34(13), 10533–10545 (2022)
3. Mahjoub, S., Chrifi-Alaoui, L., Marhic, B., Delahoche, L.: Energy consumption using LSTM multi-layer GRU and drop-GRU neural networks. Sensors 22(11), 4062 (2022)
4. Wang, T., Chen, X., Wang, Y., Chen, Y., Chen, J., Sun, S.: Short-term load forecasting for industrial enterprises based on long short-term memory network. In: 2019 IEEE 3rd Conference on Energy Internet and Energy System Integration (EI2), pp. 1759–1764. IEEE (2019)
5. Wang, L., Wang, Z., Qu, H., Liu, S.: Optimal forecast combination based on neural networks for time series forecasting. Appl. Soft Comput. 66, 1–17 (2018)
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