Personalized federated learning for buildings energy consumption forecasting

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404

Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., & Zhang, W. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i12.17325 · Cited by 7,752

Federated Optimization in Heterogeneous Networks

Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2018) · arXiv · doi:10.48550/arxiv.1812.06127 · Cited by 4,023

Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series ForecastingPreprint

Wu, H., Xu, J., Wang, J., & Long, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.13008 · Cited by 2,208

SCAFFOLD: Stochastic Controlled Averaging for Federated Learning

Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S. J., Stich, S. U., & Suresh, A. T. (2019) · arXiv · doi:10.48550/arxiv.1910.06378 · Cited by 1,734

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

Fallah, A., Mokhtari, A., & Ozdaglar, A. (2020) · neural information processing systems · Cited by 596

A review of the-state-of-the-art in data-driven approaches for building energy prediction

Sun, Y., Haghighat, F., & Fung, B. C. M. (2020) · Energy and Buildings · doi:10.1016/j.enbuild.2020.110022 · Cited by 511

A review on renewable energy and electricity requirement forecasting models for smart grid and buildings

Ahmad, T., Zhang, H., & Yan, B. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102052 · Cited by 504

A review of strategies for building energy management system: Model predictive control, demand side management, optimization, and fault detect & diagnosis

Mariano-Hernández, D., Hernández-Callejo, L., Zorita-Lamadrid, Á., Duque-Pérez, Ó., & García, F. S. (2020) · Journal of Building Engineering · doi:10.1016/j.jobe.2020.101692 · Cited by 486

Electrical Load Forecasting Using Edge Computing and Federated Learning

Taik, A., & Cherkaoui, S. (2020) · ICC 2020 - 2020 IEEE International Conference on Communications (ICC) · doi:10.1109/icc40277.2020.9148937 · Cited by 252

Statistical investigations of transfer learning-based methodology for short-term building energy predictions

Fan, C., Sun, Y., Xiao, F., Ma, J., Lee, D., Wang, J., & Tseng, Y.-C. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.114499 · Cited by 246

Distributed load forecasting using smart meter data: Federated learning with Recurrent Neural Networks

Fekri, M. N., Grolinger, K., & Mir, S. H. (2021) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2021.107669 · Cited by 238

A Federated Learning Approach to Anomaly Detection in Smart Buildings

Abdel-Sater, R., & Hamza, A. B. (2021) · ACM Transactions on Internet of Things · doi:10.1145/3467981 · Cited by 220

Privacy-preserving federated learning for residential short-term load forecasting

Fernández, J. D., Menci, S. P., Lee, C. M., Rieger, A., & Fridgen, G. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119915 · Cited by 157

Smart building energy management and monitoring system based on artificial intelligence in smart city

Selvaraj, R., Kuthadi, V. M., & Baskar, S. (2023) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2023.103090 · Cited by 151

Data-driven prediction and optimization toward net-zero and positive-energy buildings: A systematic review

Mousavi, S., Villarreal-Marroquín, M. G., Hajiaghaei–Keshteli, M., & Smith, N. R. (2023) · Building and Environment · doi:10.1016/j.buildenv.2023.110578 · Cited by 115

A multi-source transfer learning model based on LSTM and domain adaptation for building energy prediction

Lu, H., Wu, J., Ruan, Y., Qian, F., Meng, H., Gao, Y., & Xu, T. (2023) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2023.109024 · Cited by 110

Federated learning with hyperparameter-based clustering for electrical load forecasting

Gholizadeh, N., & Musı́lek, P. (2021) · Internet of Things · doi:10.1016/j.iot.2021.100470 · Cited by 107

Green building standards and the United Nations’ Sustainable Development Goals

Goubran, S., Walker, T. J., Cucuzzella, C., & Schwartz, T. (2022) · Journal of Environmental Management · doi:10.1016/j.jenvman.2022.116552 · Cited by 104

Artificial intelligence enabled energy-efficient heating, ventilation and air conditioning system: Design, analysis and necessary hardware upgrades

Lee, D., & Lee, S.-T. (2023) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2023.121253 · Cited by 93

Probabilistic forecasting method for mid-term hourly load time series based on an improved temporal fusion transformer model

Li, D., Tan, Y., Zhang, Y., Miao, S., & He, S. (2022) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2022.108743 · Cited by 92

Privacy-preserving knowledge sharing for few-shot building energy prediction: A federated learning approach

Tang, L., Xie, H., Wang, X., & Bie, Z. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.120860 · Cited by 89

TransformGraph: A novel short-term electricity net load forecasting model

Zhang, Q., Chen, J., Xiao, G., He, S., & Deng, K. (2023) · Energy Reports · doi:10.1016/j.egyr.2023.01.050 · Cited by 70

Personalized Federated DARTS for Electricity Load Forecasting of Individual Buildings

Qin, D., Wang, C., Wen, Q., Chen, W., Sun, L., & Wang, Y. (2023) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2023.3253855 · Cited by 67

Universal workflow of artificial intelligence for energy saving

Lee, D., Chen, Y.-T., & Chao, S.-L. (2022) · Energy Reports · doi:10.1016/j.egyr.2021.12.066 · Cited by 66

Artificial Intelligence-Assisted Heating Ventilation and Air Conditioning Control and the Unmet Demand for Sensors: Part 1. Problem Formulation and the Hypothesis

Cheng, C.-C., & Lee, D. (2019) · Sensors · doi:10.3390/s19051131 · Cited by 57

Federated Learning-Based Multi-Energy Load Forecasting Method Using CNN-Attention-LSTM Model

Zhang, G., Zhu, S., & Bai, X. (2022) · Sustainability · doi:10.3390/su141912843 · Cited by 56

An adaptive federated learning system for community building energy load forecasting and anomaly prediction

Wang, R., Yun, H., Rayhana, R., Bin, J., Zhang, C., Herrera, O. E., Liu, Z., & Mérida, W. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.113215 · Cited by 55

Deep Federated Adaptation: An Adaptative Residential Load Forecasting Approach with Federated Learning

Shi, Y., & Xu, X. (2022) · Sensors · doi:10.3390/s22093264 · Cited by 51

A multi-task learning model for building electrical load prediction

Liu, C., Tseng, C.-J., Huang, T., Yang, J.-S., & Huang, K.-B. (2022) · Energy and Buildings · doi:10.1016/j.enbuild.2022.112601 · Cited by 51

An integrated federated learning algorithm for short-term load forecasting

Yang, Y., Wang, Z., Zhao, S., & Wu, J. (2022) · Electric Power Systems Research · doi:10.1016/j.epsr.2022.108830 · Cited by 48

Artificial intelligence implementation framework development for building energy saving

Lee, D., Huang, H., Lee, W., & Liu, Y. (2020) · International Journal of Energy Research · doi:10.1002/er.5839 · Cited by 37

Consumers profiling based federated learning approach for energy load forecasting

Dogra, A., Anand, A., & Bedi, J. (2023) · Sustainable Cities and Society · doi:10.1016/j.scs.2023.104815 · Cited by 34

Data-Augmentation-Based Federated Learning

Zhang, H., Hou, Q., Wu, T., Cheng, S., & Liu, J. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3303889 · Cited by 31

Forecasting energy power consumption using federated learning in edge computing devices

Sarmento, E. M. M., Ribeiro, I. F., Marciano, P. R. N., Neris, Y. G., Rocha, H. R. O., Mota, V. F. S., & Villaça, R. D. S. (2023) · Internet of Things · doi:10.1016/j.iot.2023.101050 · Cited by 31

Federated deep contrastive learning for mid-term natural gas demand forecasting

Qin, D., Liu, G., Li, Z., Guan, W., Zhao, S., & Wang, Y. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121503 · Cited by 31

A review of preserving privacy in data collected from buildings with differential privacy

Janghyun, K., Barry, H., Tianzhen, H., & P, M. A. (2022) · Journal of Building Engineering · doi:10.1016/j.jobe.2022.104724 · Cited by 27

Multi-task deep learning for large-scale buildings energy management

Wang, R., Rayhana, R., Gholami, M., Herrera, O. E., Liu, Z., & Mérida, W. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.113964 · Cited by 25

Privacy-Preserving federated learning: An application for big data load forecast in buildings

Khalil, M., Esseghir, M., & Merghem, L. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103211 · Cited by 20

A short- and medium-term forecasting model for roof PV systems with data pre-processing

Lee, D., Lai, C.-W., & Fu, S.-K. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e27752 · Cited by 17

Applicability of energy consumption prediction models in a department store: A case study

Chen, L., Chen, Y.-T., Chen, Y., & Lee, D. (2023) · Case Studies in Thermal Engineering · doi:10.1016/j.csite.2023.103380 · Cited by 11

Efficient community electricity load forecasting with transformer and federated learning

Wang, R., & Liu, Z. (2023) · doi:10.1117/12.2657701 · Cited by 3

Efficient community building energy load forecasting through federated hypernetwork

Wang, R., Rayhana, R., Bai, L., & Liu, Z. (2024) · doi:10.1117/12.3012011 · Cited by 2

Exploring the comprehensive integration of artificial intelligence in optimizing HVAC system operations: A review and future outlook

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

Hybrid transformer model with liquid neural networks and learnable encodings for buildings’ energy forecasting

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

Explainable AI and Multi-Agent Systems for Energy Management in IoT-Edge Environments: A State of the Art Review

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

Harnessing Artificial Intelligence to improve building performance and energy use: innovations, challenges, and future perspectives

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

Efficient community building energy load forecasting through federated hypernetwork

Wang, R., Rayhana, R., Bai, L., & Liu, Z. (2024) · doi:10.1117/12.3012011 · Cited by 2

Personalized federated learning for cross-building energy knowledge sharing: Cost-effective strategies and model architectures

Fan, C., Chen, R., Mo, J., & Liao, L. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123016 · Cited by 37

Personalized Federated Transfer Learning for Building Energy Forecasting via Model Ensemble with Multi-Level Masking in Heterogeneous Sensing Environment

Kim, H., Dorjgochoo, S., Park, H., & Lee, S.-J. (2025) · Electronics · doi:10.3390/electronics14091790 · Cited by 13

Comparative Analysis of Data-Driven Algorithms for Building Energy Planning via Federated Learning

Ali, M., Singh, A. K., Kumar, A., Ali, S. S., & Choi, B. J. (2023) · Energies · doi:10.3390/en16186517 · Cited by 13

DBE-Fed: Decentralized Federated Fine-Tuning ofTime-Series Foundation Models for Privacy-Preserving Building Energy ManagemenPreprint

Wang, R. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7239483