## 1 Introduction
### Prior Works
applied the DBN model with one artificial dataset and three regression datasets to execute time series and regression predictions [21], while [PERSON] presented multilayer perceptron, radial basis, and RNN with a parallel structure ANN where the results outperform the general time series models [22]. [PERSON] et al. proposed an RNN with an internal feedback structure for electricity load prediction with reliable and robust results [23]. A nonlinear auto-regressive RNN model produced smooth forecasted results for hourly predictions of high-resolution wave power [24]. In [25], [PERSON] and [PERSON] used a novel hybrid technique that concatenates a wavelet and Elman network to increase one-day ahead prediction accuracy in all seasons. [PERSON] et al. in [26] used LSTM for power demand forecasting and, performance-wise, their proposed model is proven to be better than gradient boosting tree (GBT) and SVR. Further, [PERSON] et al. used a deep learning LSTM model for electric load forecasting using feature selection and genetic algorithm in which he concluded the characteristics of complex time series [27]. Additionally, [PERSON] et al. [28] proposed a hybrid model which is based on the stacking of RNN fully connected layers and unidirectional LSTM on bi-directional LSTM for energy consumption forecasting accuracy. Experimental results were revealed and compared with other hybrid models such as convolutional (Conv) neural network-LSTM, ConvLSTM, LSTM encoder-decoder model, etc. In the article by [PERSON] et al. [29], they presented an ensemble stacked generalization (ESG) method for better prediction of the energy consumption of electric vehicles (EVs). The ESG meta-regression model was a weighted combination of decision tree (DT), random forest (RF), and K-nearest neighbor (KNN) to improve model prediction and reduce model variability compared to the single regression model. Their forecast results showed that the ESG EV outperformed other models in providing a more stable and acceptable standard for the proposed diagnostic parameters in the energy consumption forecast. The study by [PERSON] et al. [30] provided a statistical model that predicts the short-term energy costs of a multifamily residential building. Their proposed approach developed a common forecast model for predicting the short-term energy demand of residential buildings in South Korea using long-term memory (LSTM) and Kalman filters (KF). Experimental results were compared and analyzed to review and compare their proposed model with the traditional ML model energy for efficient planning and energy management. Considering the operating conditions of the buildings at different times, the study by [PERSON] et al. [31] demonstrated a predictive approach to generating energy consumption based on a combined analysis and classification of energy consumption patterns. In their system, the DT classified the energy consumption patterns of the mines and the energy consumption statistics of their respective sectors. Then, the group analysis method is used to determine the model of the energy cost forecast for each pattern. The proposed method was finally evaluated against the classification of energy consumption patterns and analysis without SVR and ANN, and showed reliability and effectiveness. In the study by [PERSON] et al. [32], the ensemble ML model was proposed for an integrated approach to predicting energy consumption in non-residential buildings. Their proposed ML model used artificial neural networks, support vector replication, and the M5 Rules model as basic models. The analytical results confirmed the effectiveness of the ensemble model against the basic models in predicting the next 24 h of energy consumption in buildings. The article by [PERSON] et al. [33] designed an innovative multi-power charging forecast model for power systems based on in-depth analysis and group methods of multitasking.