A Regression Framework for Energy Consumption in Smart Cities with Encoder-Decoder Recurrent Neural Networks

Abstract

Currently, a smart city should ideally be environmentally friendly and sustainable, and energy management is one method to monitor sustainable use. This research project investigates the potential for a “smart city” to improve energy management by enabling the adoption of various types of intelligent technology to improve the energy sustainability of a city’s infrastructure and operational efficiency. In addition, the South Korean smart city region of Songdo serves as the inspiration for this case study. In the first module of the proposed framework, we place a strong emphasis on the data capabilities necessary to generate energy statistics for each of the numerous structures. In the second phase of the procedure, we employ the collected data to conduct a data analysis of the energy behavior within the microcities, from which we derive characteristics. In the third module, we construct baseline regressors to assess the proposed model’s varying degrees of efficacy. Finally, we present a method for building an energy prediction model using a deep learning regression model to solve the problem of 48-hour-ahead energy consumption forecasting. The recommended model is preferable to other models in terms of R2, MAE, and RMSE, according to the study’s findings.

Keywords

• smart buildings
• smart city
• energy consumption
• energy management
• deep learning
• machine learning