Smart City Gnosys

Smart city article details

Title	A Transfer Learning Approach To Create Energy Forecasting Models For Building Fleets
ID_Doc	5628
Authors	Torres M.V.; Shahid Z.; Mitra K.; Saguna S.; Ahlund C.
Year	2024
Published	2024 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2024
DOI	http://dx.doi.org/10.1109/SmartGridComm60555.2024.10738094
Abstract	The development of accurate energy prediction models plays a significant role in achieving sustainability in smart cities. However, stakeholders such as municipalities face the problem of creating individual energy forecasting models for multiple building fleets which leads to an increased amount of computational resources and time spent to prepare each model. This research proposes a method using Hierarchical clustering with dynamic time warping to group similar buildings according to their consumption values and the integration of transfer Learning (TL) to share the model weights from a source building to other target buildings. Different TL models using only 20%, 40%, and 60% of the target data were tested against a standard workflow without TL for predicting electricity and district heating for several school buildings using a Multivariate LSTM model. The results show a small variation between the TL and the standard models; when trained on only 40% of the data, the models achieved an average of 0.24% RMSE improvement for district heating and a 1.23% for electricity, indicating a potential for reduced data requirements without sacrificing predictive accuracy and demonstrating TL's efficiency to streamline the energy forecasting process for building fleets. © 2024 IEEE.
Author Keywords	Building fleet; DTW; Energy consumption; Hierarchical clustering; LSTM; Time series forecasting; Transfer learning

Similar Articles

Id	Similarity	Authors	Title	Published
5629	0.918	Gonzalez-Vidal A.; Mendoza-Bernal J.; Niu S.; Skarmeta A.F.; Song H.	A Transfer Learning Framework For Predictive Energy-Related Scenarios In Smart Buildings	IEEE Transactions on Industry Applications, 59, 1 (2023)
39232	0.901	Himeur Y.; Elnour M.; Fadli F.; Meskin N.; Petri I.; Rezgui Y.; Bensaali F.; Amira A.	Next-Generation Energy Systems For Sustainable Smart Cities: Roles Of Transfer Learning	Sustainable Cities and Society, 85 (2022)
23280	0.885	Huang J.; Algahtani M.; Kaewunruen S.	Energy Forecasting In A Public Building: A Benchmarking Analysis On Long Short-Term Memory (Lstm), Support Vector Regression (Svr), And Extreme Gradient Boosting (Xgboost) Networks	Applied Sciences (Switzerland), 12, 19 (2022)
52403	0.877	Roth J.; Bailey A.; Choudhary S.; Jain R.K.	Spatial And Temporal Modeling Of Urban Building Energy Consumption Using Machine Learning And Open Data	Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019 (2019)
58759	0.876	Dridi J.; Amayri M.; Bouguila N.	Transfer Learning For Estimating Occupancy And Recognizing Activities In Smart Buildings	Building and Environment, 217 (2022)
57240	0.872	Shafiq M.; Bhavani N.P.G.; Venkata Naga Ramesh J.; Veeresha R.K.; Talasila V.; Sulaiman Alfurhood B.	Thermal Modeling And Machine Learning For Optimizing Heat Transfer In Smart City Infrastructure Balancing Energy Efficiency And Climate Impact	Thermal Science and Engineering Progress, 54 (2024)
32444	0.872	Nijim M.; Kanumuri V.; Albetaineh H.; Goyal A.	Intelligent Monitoring And Management Of Smart Buildings Using Machine Learning: Optimizing User Behavior And Energy Efficiency	Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023 (2023)
22572	0.868	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)
17892	0.867	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)
36066	0.864	Ajagunsegun T.; Li J.; Bamisile O.; Ohakwe C.	Machine Learning-Based System For Managing Energy Efficiency Of Public Buildings: An Approach Towards Smart Cities	2022 4th Asia Energy and Electrical Engineering Symposium, AEEES 2022 (2022)