A new method to improve energy consumption in a smart city using deep learning

Nowadays, due to the increase in energy demand, the optimal management of energy consumption has become one of the main challenges in smart cities. This research is an important step towards making energy management smarter in smart cities. In this research, a hybrid model based on deep learning, convolutional neural networks (CNN) and long-short-term memory (LSTM) neural network is proposed to accurately predict energy consumption. By combining these two models, it is possible to have a more accurate prediction of future energy consumption and to make better decisions for energy management by identifying complex patterns of energy consumption and providing solutions to optimize energy consumption. First, energy consumption data is collected from various sources such as sensors and smart meters, then data preprocessing is done to prepare them for model training, and CNN is used to extract spatial features from energy consumption data and LSTM is used to understand the temporal patterns of these data and predict energy consumption. . The proposed method has advantages such as high accuracy of energy consumption prediction, identification of complex patterns in energy consumption, the possibility of making better decisions for energy management, helping to reduce energy consumption and improving environmental sustainability.