Smart City Gnosys

Smart city article details

Title	Early Energy Consumption Prediction As A Key Element In Smart City Sustainability
ID_Doc	21565
Authors	Naranjo F.V.; Vivar S.M.; Arias E.J.; Atassi R.
Year	2024
Published	Journal of Intelligent Systems and Internet of Things, 11, 1
DOI	http://dx.doi.org/10.54216/JISIoT.110102
Abstract	In the era of smart cities, the pursuit of sustainability stands as a paramount goal, with energy management playing a central role. This paper is dedicated to the exploration of early energy consumption prediction as a linchpin in the realization of sustainable smart cities. Employing advanced long short-term memory (LSTM) networks, we introduce a potent predictive model tailored to anticipate energy consumption patterns within urban environments. Notably, our model achieves remarkable performance metrics, with a root mean square error of 547.71 and a strikingly low mean absolute percentage error (MAPE) of 1.22. Through meticulous comparisons against baseline models, our LSTM-based approach emerges as a beacon of accuracy, reliability, and sustainability. Beyond predictive analytics, our research offers actionable insights for urban planners and policymakers, fostering the creation of greener, more sustainable, and ecologically responsible smart cities that harmonize technological innovation with environmental stewardship. As smart cities continue to evolve, our work lays the foundation for a future where sustainability is not merely a goal but a reality. © 2024, American Scientific Publishing Group (ASPG). All rights reserved.
Author Keywords	Data-Driven Sustainability; Green urban policies; Predictive Analytics; Renewable Energy Integration; Resource Optimization; Smart City Sustainability; Sustainable Development; Sustainable Urban Planning; Urban Energy Management

Similar Articles

Id	Similarity	Authors	Title	Published
4027	0.915	Carrera B.; Kim K.	A Regression Framework For Energy Consumption In Smart Cities With Encoder-Decoder Recurrent Neural Networks	Energies, 16, 22 (2023)
17962	0.896	Aljohani A.	Deep Learning-Based Optimization Of Energy Utilization In Iot-Enabled Smart Cities: A Pathway To Sustainable Development	Energy Reports, 12 (2024)
6990	0.894	Saeed A.; Asif R.M.; Rehman A.U.; Hassan S.R.; Bharany S.; Hamam H.	Ai-Based Energy Management And Prediction System For Smart Cities	Jordan Journal of Electrical Engineering, 11, 2 (2025)
11151	0.888	Yang F.; Mao Q.	Auto-Evaluation Model For The Prediction Of Building Energy Consumption That Combines Modified Kalman Filtering And Long Short-Term Memory	Sustainability (Switzerland), 15, 22 (2023)
28891	0.883	Akbarzadeh O.; Hamzehei S.; Attar H.; Amer A.; Fasihihour N.; Khosravi M.R.; Solyman A.A.	Heating-Cooling Monitoring And Power Consumption Forecasting Using Lstm For Energy-Efficient Smart Management Of Buildings: A Computational Intelligence Solution For Smart Homes	Tsinghua Science and Technology, 29, 1 (2024)
51483	0.881	Bibri S.E.; Krogstie J.	Smart Sustainable Cities Of The Future: An Extensive Interdisciplinary Literature Review	Sustainable Cities and Society, 31 (2017)
26837	0.878	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)
22572	0.878	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)
17905	0.876	Balasubramaniyan S.; Kumar P.K.; Vaigundamoorthi M.; Rahuman A.K.; Solaimalai G.; Sathish T.; Vidhya R.G.	Deep Learning Method To Analyze The Bi-Lstm Model For Energy Consumption Forecasting In Smart Cities	International Conference on Sustainable Communication Networks and Application, ICSCNA 2023 - Proceedings (2023)
37669	0.876	Mekouar Y.; Saleh I.; Mohammed K.	Modelling And Simulation Of An Ecological Route In Smart Cities Towards A Green Trajectory	2022 9th International Conference on Software Defined Systems, SDS 2022 (2022)