## 2 Related Works
Further, the DBN was applied to model each of the extracted IMFs for accurate prediction. [PERSON] et al. [26] implemented a random forest classifier to forecast household short-term energy consumption. The effectiveness of the random forest classifier was tested on five one-year datasets. The evaluation outcome showed that the presented random forest classifier obtained better predictive accuracy by means of MAE. [PERSON] et al. [27] introduced an ensemble classifier by combining random forest, gradient boosted trees and decision trees to forecast big data time series. The evaluation results showed that the developed ensemble classifier performed well in time series data prediction compared to other models and individual ensemble models. [PERSON] et al. [28] presented a new stacking multi-learning ensemble model for forecasting time series data. The presented model includes three main techniques--SVR, linear regression, and a back-propagation neural network--and the presented ensemble model comprises four major steps: integration, pruning, generation, and ensemble prediction tasks. [PERSON] et al. [29] introduced a hybrid model that integrates a firefly algorithm and an Adaptive Neuro Fuzzy Inference System (ANFIS) classifier for EECP, though the improved search space diversification in the presented model enhances its predictive accuracy. [PERSON] et al. [30] introduced a new LSTM Multi-Seasonal Net (LSTM-MSNet) for time series forecasting with multiple seasonal patterns. The evaluation outcome showed that the presented LSTM-MSNet model achieved better computational time and prediction accuracy compared to existing systems. [PERSON] and [PERSON] [31] combined multi-head attention and LSTM networks to predict the time series data precisely. [PERSON] et al. [32] initially used min-max and standard transformation techniques to eliminate outlier, redundant, and null values from the IHEPC and AEP datasets. Then, EECP was accomplished using CNN with a Gated Recurrent Units (GRUs) model. The experimental evaluation showed that the presented model obtained a significant performance in EECP by means of MAE, RMSE, and MSE. [PERSON] et al. [33] combined a Bi-LSTM network and dilated CNN to predict power consumption in local energy systems. As can be seen in the resulting phase, the presented model effectively predicts multiple step power consumption that includes monthly, weekly, daily, and hourly outputs. [PERSON] et al. [34] has integrated multilayer bi-directional GRU and CNNs for household electricity consumption prediction. The effectiveness of the presented model was evaluated in terms of MAE, RMSE, and MSE on the IHEPC and AEP datasets. Nowadays, artificial intelligence techniques are applied more in the application of EECP because of its reliability and high performance results. The artificial intelligence-based techniques, such as CNN, GRU, multi head attentions, ANFIS, and the ensemble schemes, are extensively applied for energy forecasting and time series issues. The GRU technique obtained a better outcome in EECP related to conventional techniques, but it is ineffective in handling long-term time series data sequences, and it is also historically dependent. In addition, the aforementioned techniques failed in long-term consequence forecasting and includes vanishing gradient issues [35]. To overcome the above stated concerns, a new metaheuristic based on the LSTM network is proposed in this article to predict and handle the short-term and long-term dependencies in energy forecasting.