Smart City Gnosys

Smart city article details

Title	Smart Meter-Based Energy Consumption Forecasting For Smart Cities Using Adaptive Federated Learning
ID_Doc	51169
Authors	Abdulla N.; Demirci M.; Ozdemir S.
Year	2024
Published	Sustainable Energy, Grids and Networks, 38
DOI	http://dx.doi.org/10.1016/j.segan.2024.101342
Abstract	Forecasting short-term residential energy consumption is critical in modern decentralized power systems. Deep learning-based prediction methods that can handle the high variability of residential electrical loads have made models more accurate. On the other hand, these methods need a lot of sensitive information about how much people use something gathered centrally to train a forecasting model. This is not good for privacy and scalability. Moreover, models may become less accurate over time due to changing conditions. In this work, we propose a framework for energy consumption forecasting that exploits adaptive learning, federated learning, and edge computing concepts. A central server aggregates numerous long short-term memory (LSTM) models that users at various locations train with their energy consumption data to create a generalized model that uses adaptive learning to detect data drifts and enhance forecasting at the edge layer. Our findings show that adaptive federated learning performs better than centralized learning while preserving privacy, reducing communication overhead, lowering the forecast error rate by 8%, and decreasing the training time by approximately 80%. © 2024 Elsevier Ltd
Author Keywords	Adaptive learning; Energy consumption forecasting; Federated learning; IoT; LSTM; Smart grid

Similar Articles

Id	Similarity	Authors	Title	Published
26399	0.913	Al-Quraan M.; Khan A.; Centeno A.; Zoha A.; Imran M.A.; Mohjazi L.	Fedratrees: A Novel Computation-Communication Efficient Federated Learning Framework Investigated In Smart Grids	Engineering Applications of Artificial Intelligence, 124 (2023)
38614	0.897	Yang F.; Yan K.; Jin N.; Du Y.	Multiple Households Energy Consumption Forecasting Using Consistent Modeling With Privacy Preservation	Advanced Engineering Informatics, 55 (2023)
26314	0.897	Hassna A.-A.; Fatima M.; Abdellatif K.; Mohammed E.K.	Federate Learning For Solar Power Forecasting In Smart Cities	Proceedings - IEEE Global Communications Conference, GLOBECOM (2024)
26320	0.894	Alzamil I.	Federated Deep Learning For Scalable And Explainable Load Forecasting In Privacy-Conscious Smart Cities	IEEE Access (2025)
26355	0.892	El Hanjri M.; Kabbaj H.; Kobbane A.; Abouaomar A.	Federated Learning For Water Consumption Forecasting In Smart Cities	IEEE International Conference on Communications, 2023-May (2023)
6496	0.89	Rojek I.; Mikołajewski D.; Galas K.; Piszcz A.	Advanced Deep Learning Algorithms For Energy Optimization Of Smart Cities	Energies, 18, 2 (2025)
1342	0.887	Xin Q.; Alazab M.; Díaz V.G.; Montenegro-Marin C.E.; Crespo R.G.	A Deep Learning Architecture For Power Management In Smart Cities	Energy Reports, 8 (2022)
10561	0.884	Sulaiman A.; Nagu B.; Kaur G.; Karuppaiah P.; Alshahrani H.; Reshan M.S.A.; AlYami S.; Shaikh A.	Artificial Intelligence-Based Secured Power Grid Protocol For Smart City	Sensors, 23, 19 (2023)
43181	0.883	Zhang X.-Y.; Cordoba-Pachon J.-R.; Guo P.; Watkins C.; Kuenzel S.	Privacy-Preserving Federated Learning For Value-Added Service Model In Advanced Metering Infrastructure	IEEE Transactions on Computational Social Systems, 11, 1 (2024)
23181	0.88	Lee S.H.; Lee T.; Kim S.; Park S.	Energy Consumption Prediction System Based On Deep Learning With Edge Computing	2019 2nd International Conference on Electronics Technology, ICET 2019 (2019)