## 1 Introduction
Deep structure methods have the ability to address the aforementioned concerns of shallow-based methods using multi-layer processing and hierarchical feature learning from electricity historical data. Recently, recurrent neural networks (RNNs) and convolutional neural networks (CNNs) are two powerful architecture proposed in the literature for the analysis of time series data. For instance, [PERSON] et al. [28] developed a CNN-based methodology for electricity load forecasting and compared their results with a factored restricted Boltzmann machine, sequence-to-sequence long short term memory (LSTM), support vector regressor (SVR), and ANN. Another study presented in [29] proposed a deep CNN network for day-ahead load forecasting and compared the results with an extreme learning machine, ARIMA, CNN, and RNN. Several studies also used RNN models for electricity load prediction, whereas [PERSON] et al. [30] used RNN, gated recurrent unit (GRU), and LSTM models for electricity load prediction in Turkey and extensively decreased the error. Furthermore, the authors of [31] developed an LSTM-based model for periodic energy prediction and compared their results with other models. Another study presented in [32] developed an RNN-based model for medium- and long-term electricity load prediction. The electricity consumption data is time-series data, which comprises spatial and temporal information. The CNN models perform well for spatial information extraction, but insufficient for temporal information, whereas the RNN models are insufficient for spatial information and can learn temporal information. Therefore, to develop an optimal model for electricity load prediction, hybrid models are introduced in the recent literature. For instance, [PERSON] et al. [33] developed a hybrid model combining CNN with LSTM for short-term load prediction and compared their results with GRU, attention LSTM, LSTM, and bidirectional LSTM. [PERSON] et al. [34] also developed a hybrid model with a combination of CNN and multi-layer bidirectional LSTM and compared their results with bidirectional LSTM. LSTM, LSTM, and CNN-LSTM. Similarly, another study presented in [17] integrated a CNN with an LSTM auto-encoder and compared the final results with LSTM, LSTM autoencoder, and CNN-LSTM. Moreover, [PERSON] et al. [35] and [PERSON] et al. [36] presented the performance of a CNN-GRU based model for electricity forecasting. The performance of hybrid models is quite promising and has achieved state-of-the-art accuracy; however, further improvements are needed for optimal electricity load prediction. Therefore, in the current study, we established a two-step framework for predicting the electricity load that includes data preprocessing and proposed a hybrid model. In the first step, the historical data of electricity is refined to remove abnormalities that are then passed to the next step CNN along with MB-GRU model for learning. To extract the spatial information, we used CNN layers where MB-GRU is used to learn the temporal information. The contributions of the proposed research are summarized below:  
* The electricity consumption data are gathered from smart meter sensors, which include missing values, redundant values, outlier values, etc., due to several reasons, such as faults in meter sensors, variable weather conditions, abnormal customer consumption patterns, etc., which need to be refined before training the model. Therefore, in this work, the input raw datasets are refined before training to fill the missing values and remove the outlier values from the dataset. Similarly, the electricity consumption patterns are of very diverse nature where the neural networks are sensitive to it, so a data normalization technique is applied to bring the dataset into a standard range. * The mainstream methods use solo models for electricity consumption prediction, which are unable to precisely extract spatiotemporal patterns and have high error rates.