K. Amasyali, N. M. El-Gohary, 2018, A review of data-driven building energy consumption prediction studies, Renewable and Sustainable Energy Reviews, Vol. 81, pp. 1192-1205

M. M. Alam, M. F. Ahmed, I. Jahan, Y. M. Jang, 2021, Optimal Energy Management Strategy for ESS with Day Ahead Energy Prediction, In 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 492-496

B. Wolff, J. Kühnert, E. Lorenz, O. Kramer, D. Heinemann, Oct 2016, Comparing support vector regression for PV power forecasting to a physical modeling approach using measurement numerical weather prediction and cloud motion data, Sol. Energy, Vol. 135, pp. 197-208

P.-H. Kuo, C.-J. Huang, 2018, A high precision artificial neural networks model for short-term energy load forecasting, Energies, Vol. 11, No. 1, pp. 213

L. Li, K. Ota, M. Dong, 2017, Everything is image: CNN-based short-term electrical load forecasting for smart grid, Proc. 14th Int. Symp. Pervas. Syst. Algorithms Network 11th International Conf. Frontier Comput. Sci. Technol. 3rd Int. Symp. Creative Comput. (ISPAN-FCST -ISCC), pp. 344-351

H. Shi, M. Xu, R. Li, 2018, Deep learning for household load forecasting—A novel pooling deep RNN, IEEE Trans. Smart Grid, Vol. 9, No. 5, pp. 5271-5280

J. Jiang, W. Zhu, C. Zhang, W. Xingang, 2021, Electrical Load Forecasting Based on Multi-model Combination by Stacking Ensemble Learning Algorithm, 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), pp. 739-743

K. Yan, X. Wang, Y. Du, N. Jin, H. Huang, H. Zhou, 2018, Multi-step short-term power consumption forecasting with a hybrid deep learning strategy, Energies, Vol. 11, No. 11, pp. 1-15

K. Gyu-Hyun, H. Chang Woo, J. Sung-Hoon, H. Kyeon, 2021, Short-term load forecasting of commercial building considering seasonal characteristics based on CNN-LSTM algorithm, The 52th KIEE Summer Conference Proceedings, pp. 686-687

S Gupta., A Gupta., 2019, Dealing with Noise Problem in Machine Learning Data-sets: A Systematic Review, Procedia Computer Science 161, pp. 466-474

M. Xia, H. Shao, X. Ma, Silva C. W. de, 2021, A stacked GRU-RNN-based approach for predicting renewable energy and electricity load for smart grid operation, IEEE Transactions on Industrial Informatics, Vol. 17, No. 10, pp. 7050-7059

Z. Yifan, Q. Fengchen, X. Fei, 2020, GS-RNN: A Novel RNN Optimization Method based on Vanishing Gradient Mitigation for HRRP Sequence Estimation and Recognition, In 2020 IEEE 3rd International Conference on Electronics Technology (ICET), pp. 840-844

I. Junghyun, S. Yeong Rak, O. Ha-Ryoung, 2021, Improving the accuracy of predicting electricity usage in distribution and logistics facilities, The Korean Institute of Electromagnetic Engineering and Science summer conference 2021, Vol. 9, No. 1, pp. 238

P. Dhal, C. Azad, 2021, A comprehensive survey on feature selection in the various fields of machine learning, Applied Intelligence, pp. 1-39