# Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM
## 1 Introduction
The authors implemented two approaches including the Neural Network based Genetic Algorithm denoted by NNGA and the Neural Network based Particle Swarm Optimization namely NNPSO for optimizing the weight of neural network to improve the performance. The proposed approach yields better results compared with the results of the CNN model in predicting the future energy consumption. The proposed approach was verified by the real time dataset collected from pecan street. Meanwhile, [PERSON] and [PERSON] [22] proposed an efficient model for electronic energy consumption prediction in residential buildings using deep extreme learning machine. The dataset used on this study for weekly and monthly energy prediction was collected from four residential buildings from January to December in 2010. In addition, [PERSON] et al. [23] utilized feature selection and genetic algorithm to optimize a LSTM model for electric load prediction. The proposed approach obtained higher accuracy than other machine learning models for France metropolitan's electricity consumption data. Unfortunately, most of the data from above studies are not available. In 2018, [PERSON] et al. [24] introduced a comprehensive survey regarding to the approaches based on data driven and large scale for predicting the energy demand in a building to provide better overview of this issue. Next, [PERSON] et al. [25] proposed a hybrid electric energy consumption forecasting model for week-ahead total daily electric energy consumption forecasting. They classified electric energy consumption data by pattern similarity using the decision tree technique. Then, they used Random Forest and Multilayer Perceptron to select models with a better prediction performance in similar time series. They compared their hybrid model with other machine learning techniques using three university building clusters. They verified their hybrid model yielded better prediction performance than other machine learning techniques. In 2019, [PERSON] et al. [26] utilized several regression approaches for predicting the urban area electrical energy demand in an urban area located in Sydney from 2006 to 2010. The experimental results show that Random Forest Regressor model archived the best performance for short-term electrical energy demand prediction (30 min) while kNN model obtained the relatively better results for predicting the daily timespan of electrical energy demand. Meanwhile, [PERSON] et al. [27] introduced a comparative empirical evaluation of different time series forecasting strategies in both statistical and machine learning based approaches for Short Term Electric Energy Consumption Prediction on a dataset regarding the electricity consumption registered by thirteen buildings located at the Pablo de Olavide (UPO) University campus in Seville, Spain, collected over five and a half years. The results found that Machine Learning approaches including bagging and boosting ensemble schemes could achieve better results. [PERSON] and [PERSON] [28] developed a framework using Petri Nets for smart temperature control to reduce energy consumption for smart building. The proposed system reduces the electric energy consumption by automatically controller electrical devices. The experiments show that this system reduces a significant amount of electric energy consumption at ratio 46.79%. [PERSON] et al. [29] proposed a recurrent inception convolution neural network (RICNN) to predict the electric energy consumption (48-time steps with an interval of 30 min). They combined RNN and 1-D convolution inception module to help calibrate the prediction time and the hidden state vector values calculated from the nearby time steps. They used three industrial distribution complexes in South Korea. They verified their proposed RICNN outperformed better prediction performance than Multilayer Perceptron, Convolutional Neural Networks, and Recurrent Neural Networks.