1. A. Xu, M.-W. T., B. Firouzi, K. A. Alattas, A. Mohammadzadeh, and E. Ghaderpour, "A new deep learning Restricted Boltzmann Machine for energy consumption forecasting," Sustainability, vol. 14, no. 16, p. 10081, 2022. [DOI:10.3390/su141610081]

2. Al Mamun, A., Sohel, M., Mohammad, N., Sunny, M. S. H., Dipta, D. R., & Hossain, E. (2020). A comprehensive review of the load forecasting techniques using single and hybrid predictive models. IEEE Access, 8, 134911-134939. [DOI:10.1109/ACCESS.2020.3010702]

3. Amalou, I., Mouhni, N., & Abdali, A. (2022). Multivariate time series prediction by RNN architectures for energy consumption forecasting. Energy Reports, 8, 1084-1091. [DOI:10.1016/j.egyr.2022.07.139]

4. Cui, C., He, M., Di, F., Lu, Y., Dai, Y., & Lv, F. (2020). Research on power load forecasting method based on LSTM model. 2020 IEEE 5th information technology and mechatronics engineering conference (ITOEC), [DOI:10.1109/ITOEC49072.2020.9141684]

5. Farsi, B., Amayri, M., Bouguila, N., & Eicker, U. (2021). On short-term load forecasting using machine learning techniques and a novel parallel deep LSTM-CNN approach. IEEE Access, 9, 31191-31212. [DOI:10.1109/ACCESS.2021.3060290]

6. L'Heureux, A., Grolinger, K., & Capretz, M. A. (2022). Transformer-based model for electrical load forecasting. Energies, 15(14), 4993. [DOI:10.3390/en15144993]

7. Lu, W., Li, J., Wang, J., & Qin, L. (2021). A CNN-BiLSTM-AM method for stock price prediction. Neural Computing and Applications, 33, 4741-4753. [DOI:10.1007/s00521-020-05532-z]

8. Mahjoub, S., Chrifi-Alaoui, L., Marhic, B., & Delahoche, L. (2022). Predicting Energy Consumption Using LSTM, Multi-Layer GRU and Drop-GRU Neural Networks. Sensors, 22(11), 4062. [DOI:10.3390/s22114062]

9. Ozcan, A., Catal, C., & Kasif, A. (2021). Energy load forecasting using a dual-stage attention-based recurrent neural network. Sensors, 21(21), 7115. [DOI:10.3390/s21217115]

10. Shah, I., Iftikhar, H., & Ali, S. (2022). Modeling and forecasting electricity demand and prices: A comparison of alternative approaches. Journal of Mathematics, 2022. [DOI:10.1155/2022/3581037]

11. Song, X., Liu, Y., Xue, L., Wang, J., Zhang, J., Wang, J., Jiang, L., & Cheng, Z. (2020). Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model. Journal of Petroleum Science and Engineering, 186, 106682. [DOI:10.1016/j.petrol.2019.106682]

12. Tarmanini, C., Sarma, N., Gezegin, C., & Ozgonenel, O. (2023). Short term load forecasting based on ARIMA and ANN approaches. Energy Reports, 9, 550-557. [DOI:10.1016/j.egyr.2023.01.060]

13. Wang, C., Wang, Y., Ding, Z., Zheng, T., Hu, J., & Zhang, K. (2022). A transformer-based method of multienergy load forecasting in integrated energy system. IEEE Transactions on Smart Grid, 13(4), 2703-2714. [DOI:10.1109/TSG.2022.3166600]

14. Wang, D., Gan, J., Mao, J., Chen, F., & Yu, L. (2023). Forecasting power demand in China with a CNN-LSTM model including multimodal information. Energy, 263, 126012. [DOI:10.1016/j.energy.2022.126012]

15. Wang, H., Zhang, Y., Liang, J., & Liu, L. (2023). DAFA-BiLSTM: Deep Autoregression Feature Augmented Bidirectional LSTM network for time series prediction. Neural Networks, 157, 240-256. [DOI:10.1016/j.neunet.2022.10.009]

17. A. Xu, M.-W. T., B. Firouzi, K. A. Alattas, A. Mohammadzadeh, and E. Ghaderpour, "A new deep learning Restricted Boltzmann Machine for energy consumption forecasting," Sustainability, vol. 14, no. 16, p. 10081, 2022. [DOI:10.3390/su141610081]

18. Al Mamun, A., Sohel, M., Mohammad, N., Sunny, M. S. H., Dipta, D. R., & Hossain, E. (2020). A comprehensive review of the load forecasting techniques using single and hybrid predictive models. IEEE Access, 8, 134911-134939. [DOI:10.1109/ACCESS.2020.3010702]

19. Amalou, I., Mouhni, N., & Abdali, A. (2022). Multivariate time series prediction by RNN architectures for energy consumption forecasting. Energy Reports, 8, 1084-1091. [DOI:10.1016/j.egyr.2022.07.139]

20. Cui, C., He, M., Di, F., Lu, Y., Dai, Y., & Lv, F. (2020). Research on power load forecasting method based on LSTM model. 2020 IEEE 5th information technology and mechatronics engineering conference (ITOEC), [DOI:10.1109/ITOEC49072.2020.9141684]

21. Farsi, B., Amayri, M., Bouguila, N., & Eicker, U. (2021). On short-term load forecasting using machine learning techniques and a novel parallel deep LSTM-CNN approach. IEEE Access, 9, 31191-31212. [DOI:10.1109/ACCESS.2021.3060290]

22. L'Heureux, A., Grolinger, K., & Capretz, M. A. (2022). Transformer-based model for electrical load forecasting. Energies, 15(14), 4993. [DOI:10.3390/en15144993]

23. Lu, W., Li, J., Wang, J., & Qin, L. (2021). A CNN-BiLSTM-AM method for stock price prediction. Neural Computing and Applications, 33, 4741-4753. [DOI:10.1007/s00521-020-05532-z]

24. Mahjoub, S., Chrifi-Alaoui, L., Marhic, B., & Delahoche, L. (2022). Predicting Energy Consumption Using LSTM, Multi-Layer GRU and Drop-GRU Neural Networks. Sensors, 22(11), 4062. [DOI:10.3390/s22114062]

25. Ozcan, A., Catal, C., & Kasif, A. (2021). Energy load forecasting using a dual-stage attention-based recurrent neural network. Sensors, 21(21), 7115. [DOI:10.3390/s21217115]

26. Shah, I., Iftikhar, H., & Ali, S. (2022). Modeling and forecasting electricity demand and prices: A comparison of alternative approaches. Journal of Mathematics, 2022. [DOI:10.1155/2022/3581037]

27. Song, X., Liu, Y., Xue, L., Wang, J., Zhang, J., Wang, J., Jiang, L., & Cheng, Z. (2020). Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model. Journal of Petroleum Science and Engineering, 186, 106682. [DOI:10.1016/j.petrol.2019.106682]

28. Tarmanini, C., Sarma, N., Gezegin, C., & Ozgonenel, O. (2023). Short term load forecasting based on ARIMA and ANN approaches. Energy Reports, 9, 550-557. [DOI:10.1016/j.egyr.2023.01.060]

29. Wang, C., Wang, Y., Ding, Z., Zheng, T., Hu, J., & Zhang, K. (2022). A transformer-based method of multienergy load forecasting in integrated energy system. IEEE Transactions on Smart Grid, 13(4), 2703-2714. [DOI:10.1109/TSG.2022.3166600]

30. Wang, D., Gan, J., Mao, J., Chen, F., & Yu, L. (2023). Forecasting power demand in China with a CNN-LSTM model including multimodal information. Energy, 263, 126012. [DOI:10.1016/j.energy.2022.126012]

31. Wang, H., Zhang, Y., Liang, J., & Liu, L. (2023). DAFA-BiLSTM: Deep Autoregression Feature Augmented Bidirectional LSTM network for time series prediction. Neural Networks, 157, 240-256. [DOI:10.1016/j.neunet.2022.10.009]