# Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM
## 1 Introduction  
With the development of data, internet as well as computing power of computers, machine learning and deep learning [1; 2] have been used in many areas such as construction [3; 4; 5], cybernetic [6; 7], economic [8; 9; 10; 11] and medical [12; 13] to help professionals save time and effort. Utilizing machine learning in economic, [PERSON] et al. [14] introduced a full-fledged geo-demographic segmentation model for identifying and gaining insights of the most probable cause of churn for a bank dataset. Meanwhile, [PERSON] et al. [8; 9; 10] developed several machine learning models for dealing with imbalance data problem to forecast the bankruptcy in South Korea. These studies can help the investors, managers and government devise appropriate strategies to optimize the profits in their business. In addition, medical diagnosis is another practical application of machine learning and deep learning with many studies in this decade. Recently, [PERSON] et al. [12] proposed a deep CNN model to classify brainimages to diagnose abnormal brain tumors. In addition, [PERSON] and Baik Le and Baik (2019) proposes FSX framework that utilizes an oversampling technique and extreme gradient boosting (XGB) classifier to improve the prediction ability in self-care problem identification for children with disability. Through the above studies, machine learning and deep learning are growing dramatically and are being applied to most important areas. Long Short-Term Memory (LSTM) is a popular specialized model of artificial recurrent neural network (RNN) which is capable of modeling sequential or temporal aspects of data. Unlike original RNN models which often get problem of vanishing or exploding gradients when training sequential data, LSTM introduces three new gates in each cell including the input gate, output gate and forget gate which are capable of capturing the temporal changes in extreme long sequential data. Therefore, it has been used widely for text, videos, and time-series analysis. [PERSON] et al. [PERSON] et al. (2018) proposed a lightweight as well as real-time system utilizing LSTM, a model of recurrent neural networks, for fault detection in smart factories. Next, [PERSON] and [PERSON] (2018) combined Convolutional Neural Network (CNN) and LSTM to particulate matter forecasting in smart cities. Monitoring particulate matter concentration will reduce several diseases such as asthma, lung cancer, or cardiovascular diseases. Meanwhile, [PERSON] et al. [PERSON] et al. (2018) propose a method based on LSTM with attention mechanism for travel time prediction to improve the effectiveness of intelligent transportation systems. In addition, [PERSON] et al. [PERSON] et al. (2018) developed a neural-encoded mention-hypergraph (NEMH) approach to recognize the nested structure mention entities which has ability to extract features for dealing with both overlapping and nested structures mentions automatically. Electric energy consumption prediction (EECP), a multivariate time series forecasting issue, is an interesting issue that needs to be addressed for stable power supply. In recent years, there are many approaches proposed to predict the electric energy consumption [PERSON] et al. (2019); [PERSON] and [PERSON] (2019); [PERSON] et al. (2020); [PERSON] et al. (2020); [PERSON] et al. (2020); [PERSON] et al. in short-term timespan. This issue can recognize the peak demand pattern for both commercial and industrial customers. This approach would provide to the customers several direct and indirect benefits. In addition, the utility providers also benefit from demand reduction, cost control, and system stability. This approach was successfully applied to three different manufactories to estimate and manage the electrical maximum demand in short-term timespan. Next, [PERSON], [PERSON] and [PERSON] [21] developed a novel neural network-based optimization approach for electronic energy consumption prediction.