## 2 Literature Survey  
Over the past decades, many approaches have been proposed for robust and accurate prediction of energy consumption and short-term load forecasting at the distribution-level [4; 5; 6; 7], but there are limited studies for customers-level load forecasting. [PERSON] et al. [8] compared traditional time-series model seasonal autoregressive-integrated moving average with exogenous inputs (SARIMAX) with recurrent neural networks (RNNs) and CNN models in the application of day-ahead building-level load forecasting. The authors reported a decrease in day-ahead multi-step forecasting error by 22.6% on average for cases with strong weather covariance compared to the SARIMAX model.  
[PERSON] et al. [9] proposed a short-term load forecasting method based on a similar day selection and long short-term memory (LSTM) network for buildings. In [10], a network-based short-term load forecasting utilized the LSTM algorithm for residential buildings. The authors applied wavelet decomposition and collaborative representation methods to obtain the feature vector extracted from lagged load variables. In [11], the authors proposed a model based on empirical mode decomposition (EMD) and LSTM network for estimating electricity demand at a specified time interval. Another similar study was conducted in [12] for the residential buildings dataset. The authors concluded that the LSTM-based RNN model performed better compared with simple RNN and gated recurrent unit (GRU)-RNN models for a single user with a one-minute resolution. However, the uncertainty issues involved in this granular scale result in mean absolute percentage error (MAPE) values that range between 24% and 59%. In [13], the proposed BLSTM network has some important advantages as the networks can learn from raw data due to not requiring the feature extraction process and are capable of maintaining the long-term dependencies in time series data. The generalized additive model (GAM) in [14] fills missing data by modeling the input data as a sum of splines. Additionally, another advantage of GAM is to obtain interpretable information about the data and the covariates. Another implementation of deep learning is presented in [15] for the purpose of electricity-theft detection. The proposed method consists of two different components named wide and deep CNN. While the deep CNN component identifies periodic features from the input 2-D electricity consumption data, the wide CNN component obtains useful features from 1-D electricity consumption data. Deep learning-based fault diagnostics/prognostics implementations in the induction machine's bearings are one of the most important issues in the field of fault detection [13; 16].  
Considering hybridizing two DL models in [17], the authors implemented two different sequence-to-sequence (S2S) RNN methods on a building-level energy consumption dataset. For the first RNNs, GRU encodes the information, and the second is the LSTM model that sequentially generates the outputs using this information. The proposed method was tested for three different prediction length scenarios and compared with traditional DL algorithms. The MAPE results varied between 2.441% and 23.698% on average for all scenarios. Another hybrid DL method for gas consumption estimation was introduced in [18]. The proposed method is based on wavelet transform (WT), BLSTM, and genetic algorithm (GA). The proposed methods prediction performance was tested on different horizons such as 1 h, 5 h, and 10 h, and the mean absolute error (MAE) values were 10.27, 11.96, and 55.11, respectively. Lastly, data imputation methods based on transfer learning and deep learning were studied in [19; 20].