## 2 Related Works  
In this section, previous works in the area are reviewed in order to justify the contribution of the proposal and the selection of strategies considered for comparison in the experimental section. [PERSON] et al. [16] combined a Bidirectional Long Short-Term Memory (Bi-LSTM) network and a Convolutional Neural Network (CNN) to forecast household EEC. Firstly, the CNN was employed to extract the discriminative feature values from the IHEPC dataset and then the Bi-LSTM network was used to make predictions. [PERSON] et al. [17] introduced a new ensemble-based deep learning model to forecast and predict energy consumption and demands. Initially, data pre-processing was performed using transformation, normalization, and cleaning techniques, and then the pre-processed data were fed into the ensemble model, the CNN and Bi-LSTM network extracting discriminative feature values. In this work, an active learning concept was created on the basis of the moving window to improve and ensure the prediction performance of the presented model. In the resulting phase, the effectiveness of the presented model was tested on a Korean commercial building dataset in light of MAPE, RMSE, MAE, and MSE values. [PERSON] et al. [18] integrated an Extreme Learning Machine (ELM) and Variational Mode Decomposition (VMD) techniques for electrical load forecasting. Firstly, the VMD technique was employed to transform the collected electric load series into components with dissimilar frequencies, which helps to eliminate fluctuation properties and enhances the overall accuracy of prediction. Finally, EEC forecasting was carried out utilizing ELM with a differential evolution algorithm. [PERSON] et al. [19] combined a Deep Belief Network (DBN) and linear regression techniques to predict time series data. In this study, the linear regression technique captures the non-linear and linear behaviors of the time series data. Initially, the linear regression technique was used to obtain the residuals between input and predicted data, and then the residuals were fed into the DBN for the final forecasting. In the time series forecasting, the DBN significantly extracts the features between self-organization properties and layers. [PERSON] et al. [20] applied Support Vector Regression (SVR) to the time series data for electric load forecasting. The SVR technique successfully modelled the nonlinear relation between the target variables and the exogenous covariates. [PERSON] et al. [21] developed a new multivariate temporal convolutional network for time series prediction that has been extensively used in applications such as transportation, finance, aerology, and power/energy. In the time series data forecasting, the presented convolution network superiorly enhanced the results of EECP. Further, this study concentrates on the trade-off between prediction accuracy and implementation complexity. [PERSON] et al. [22] combined a genetic algorithm and a Particle Swarm Optimization (PSO) algorithm to select optimal hyperparameters in LSTM for an effective EECP. [PERSON] et al. [23] introduced an oblique random forest classifier for time series forecasting. In the developed classification technique, every node of the decision tree was replaced by the optimal feature-based orthogonal classifier. Additionally, the least square classification technique was used to perform feature partition. The efficiency of the oblique random forest classifier was investigated using five electricity load time series datasets and eight general time series datasets. Further, [PERSON] and [PERSON] [24] presented a new deep learning network for short term energy load forecasting. The obtained results showed that the deep-energy model was robust and had a strong generalization ability in data series forecasting. Similarly, [PERSON] et al. [25] combined DBN and empirical mode decomposition for electricity load demand forecasting. Initially, the acquired data series were decomposed into several Intrinsic Mode Functions (IMFs).