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

Berny Carrera and Kwanho Kim ()
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Berny Carrera: Department of Industrial and Management Engineering, Incheon National University, Incheon 22012, Republic of Korea
Kwanho Kim: Department of Industrial and Management Engineering, Incheon National University, Incheon 22012, Republic of Korea

Energies, 2023, vol. 16, issue 22, 1-24

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 R 2 , MAE, and RMSE, according to the study’s findings.

Keywords: smart buildings; smart city; energy consumption; energy management; deep learning; machine learning; data mining (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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