Smart City Gnosys

Smart city article details

Title	A Regression Framework For Energy Consumption In Smart Cities With Encoder-Decoder Recurrent Neural Networks
ID_Doc	4027
Authors	Carrera B.; Kim K.
Year	2023
Published	Energies, 16, 22
DOI	http://dx.doi.org/10.3390/en16227508
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.
Author Keywords	data mining; deep learning; energy consumption; energy management; machine learning; smart buildings; smart city

Similar Articles

Id	Similarity	Authors	Title	Published
21565	0.915	Naranjo F.V.; Vivar S.M.; Arias E.J.; Atassi R.	Early Energy Consumption Prediction As A Key Element In Smart City Sustainability	Journal of Intelligent Systems and Internet of Things, 11, 1 (2024)
22572	0.911	Peteleaza D.; Matei A.; Sorostinean R.; Gellert A.; Fiore U.; Zamfirescu B.-C.; Palmieri F.	Electricity Consumption Forecasting For Sustainable Smart Cities Using Machine Learning Methods	Internet of Things (Netherlands), 27 (2024)
17962	0.91	Aljohani A.	Deep Learning-Based Optimization Of Energy Utilization In Iot-Enabled Smart Cities: A Pathway To Sustainable Development	Energy Reports, 12 (2024)
26837	0.9	Helli S.S.; Tanberk S.; Demir O.	Forecasting Energy Consumption Using Deep Learning In Smart Cities	Proceedings - 2022 International Conference on Artificial Intelligence of Things, ICAIoT 2022 (2022)
7417	0.895	Sunder R.; R S.; Paul V.; Punia S.K.; Konduri B.; Nabilal K.V.; Lilhore U.K.; Lohani T.K.; Ghith E.; Tlija M.	An Advanced Hybrid Deep Learning Model For Accurate Energy Load Prediction In Smart Building	Energy Exploration and Exploitation, 42, 6 (2024)
17892	0.893	Moveh S.; Merchán-Cruz E.A.; Abuhussain M.; Dodo Y.A.; Alhumaid S.; Alhamami A.H.	Deep Learning Framework Using Transformer Networks For Multi Building Energy Consumption Prediction In Smart Cities	Energies, 18, 6 (2025)
23309	0.891	Laroui M.; Dridi A.; Afifi H.; Moungla H.; Marot M.; Cherif M.A.	Energy Management For Electric Vehicles In Smart Cities: A Deep Learning Approach	2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019 (2019)
38700	0.888	Amalou I.; Mouhni N.; Abdali A.	Multivariate Time Series Prediction By Rnn Architectures For Energy Consumption Forecasting	Energy Reports, 8 (2022)
23190	0.884	Hrnjica B.; Mehr A.D.	Energy Demand Forecasting Using Deep Learning	EAI/Springer Innovations in Communication and Computing (2020)
23401	0.882	Puangpontip S.; Hewett R.	Energy Usage Of Deep Learning In Smart Cities	Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020 (2020)