The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Personalized federated learning for buildings energy consumption forecasting
Open accessAbstract
Buildings' energy consumption forecasting is critical for energy saving and building maintenance. However, most studies only focus on centralized learning of one dataset, which ignores the data privacy and data shortage issue. Meanwhile, the difference in energy data distributions from many buildings causes difficulties in training a good machine learning model. Although these two challenges of data privacy and data heterogeneity could be resolved through personalized federated learning algorithms to some degree, there is still a lack of investigation into applying these algorithms to building energy data analytics. Besides using existing personalized federated learning algorithms, we design a new deep learning model through a mixture of experts to support personalization for heterogeneous data distribution. This new design is the first trial to tackle the data heterogeneity through ensemble architecture in federated load forecasting. Extensive experiments are conducted to evaluate the effectiveness of our proposed model with different training algorithms. The results show that our proposed method outperforms other state-of-the-art models in energy forecasting accuracies by 10% to 40% across the buildings' energy data from university campuses. • Create a new personalized federated learning algorithm through deep learning architecture model design. • Develop a new transformer-based deep learning model called Metaformer, which reduces memory usage by 50%. • Improve buildings' energy forecasting accuracy by 10% to 40% compared to state-of-the-art models across 40 buildings.
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“Optimization-based personalization avoids some of these shortcomings by adjusting objective functions or applying regularization at each client, enabling alignment with the global model while respecting local deviations [65].”
From Explainable AI and Multi-Agent Systems for Energy Management in IoT-Edge Environments: A State of the Art Review (2026) · cites this paper as [65]
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Cites
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Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
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Federated Optimization in Heterogeneous Networks
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Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series ForecastingPreprint
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SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
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Towards Personalized Federated Learning
Tan, A. Z., Yu, H., Cui, L., & Yang, Q. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3160699 · Cited by 1,474
MetaFormer is Actually What You Need for Vision
Yu, W., Luo, M., Zhou, P., Si, C., Zhou, Y., Wang, X., Feng, J., & Yan, S. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01055 · Cited by 1,449
Personalized Federated Learning with Moreau EnvelopesPreprint
Dinh, C. T., Tran, N. H. K., & Nguyen, T. D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.08848 · Cited by 697
Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach
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A review of the-state-of-the-art in data-driven approaches for building energy prediction
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Cited by
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Lu, S., Zhou, S., Ding, Y. F., Kim, M. K., Yang, B., Tian, Z., & Liu, J. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.103765 · Cited by 68
Antonesi, G., Cioara, T., Anghel, I. M., Papias, I., Michalakopoulos, V., & Sarmas, E. (2025) · Energy and AI · doi:10.1016/j.egyai.2025.100489 · Cited by 44
Álvarez-López, C., González-Briones, A., & Li, T. (2026) · Electronics · doi:10.3390/electronics15020385 · Cited by 23
Federated Learning for Smart Cities: A Thematic Review of Challenges and Approaches
Alterkawi, L., & Dib, F. K. (2025) · Future Internet · doi:10.3390/fi17120545 · Cited by 16
Woldegiyorgis, T. A., Li, H., Asmare, E., Assamnew, A. D., Chekol, F., Desalegn, G. A., Asefa, S. K., & Mossie, S. Y. (2025) · Energy Informatics · doi:10.1186/s42162-025-00589-3 · Cited by 15
Metadata
- Authors
- Wang, R., Bai, L., Rayhana, R., & Liu, Z.
- Affiliations
- University of British Columbia (CA)
- Corresponding author
- Liu, Zheng
- Venue
- Energy and Buildings
- Publisher
- Elsevier BV
- Published
- 2024-11
- Volume
- 323
- Pages
- 114762
- ISSN
- 0378-7788 (ISSN-L)
- OpenAlex
- W4402567580
- Cited by
- 38
- References
- 46
- Citations by year
- 2026: 22 · 2025: 14 · 2024: 2
- FWCI
- 7.05
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Energy and Buildings (publishedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- personalized federated learning, building energy consumption prediction, data heterogeneity, data privacy, mixture of experts, ensemble architecture, MetaFormer, transformer models, memory efficiency, university buildings, load forecasting
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- Wang, R., Bai, L., Rayhana, R., & Liu, Z. (2024). Personalized federated learning for buildings energy consumption forecasting. Energy and Buildings. https://doi.org/10.1016/j.enbuild.2024.114762
