Open Access
| Issue |
E3S Web Conf.
Volume 689, 2026
14th International Symposium on Heating, Ventilation, and Air Conditioning (ISHVAC 2025)
|
|
|---|---|---|
| Article Number | 10003 | |
| Number of page(s) | 11 | |
| Section | Building Automation and Energy Management | |
| DOI | https://doi.org/10.1051/e3sconf/202668910003 | |
| Published online | 21 January 2026 | |
- JING W, ZHEN M, GUAN H, et al. A prediction model for building energy consumption in a shopping mall based on Chaos theory [J]. Energy Reports, 2022, 8: 5305-12. [Google Scholar]
- ZHANG W, WU J, LIU J. A novel building flexibility potential assessment method based on hybrid CNN-GRU-CALDA framework considering consumer psychology [J]. Sustainable Cities and Society, 2024, 104: 105298. [Google Scholar]
- LIU H, LIANG J, LIU Y, et al. A Review of Data-Driven Building Energy Prediction [J]. Buildings, 2023, 13(2): 532. [Google Scholar]
- AL-SHARGABI A A, ALMHAFDY A, IBRAHIM D M, et al. Buildings’ energy consumption prediction models based on buildings’ characteristics: Research trends, taxonomy, and performance measures [J]. Journal of Building Engineering, 2022, 54: 104577. [Google Scholar]
- KABILAN R, CHANDRAN V, YOGAPRIYA J, et al. Short‐Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning Algorithms [J]. International Journal of Photoenergy, 2021, 2021(1): 5582418. [Google Scholar]
- PORSE E, FOURNIER E, CHENG D, et al. Net solar generation potential from urban rooftops in Los Angeles [J]. Energy Policy, 2020, 142: 111461. [Google Scholar]
- FAN C, SUN Y, XIAO F, et al. Statistical investigations of transfer learning-based methodology for short-term building energy predictions [J]. Applied Energy, 2020, 262: 114499. [Google Scholar]
- LEI L, CHEN W, WU B, et al. A building energy consumption prediction model based on rough set theory and deep learning algorithms [J]. Energy and Buildings, 2021, 240: 110886. [Google Scholar]
- CAO W, YU J, CHAO M, et al. Short-term energy consumption prediction method for educational buildings based on model integration [J]. Energy, 2023, 283: 128580. [Google Scholar]
- SOMU N, RAMAN M R G, RAMAMRITHAM K. A deep learning framework for building energy consumption forecast [J]. Renewable and Sustainable Energy Reviews, 2021, 137: 110591. [CrossRef] [Google Scholar]
- LI G, WU Y, YAN C, et al. An improved transfer learning strategy for short-term cross-building energy prediction using data incremental [J]. Building Simulation, 2024, 17(1): 165-83. [Google Scholar]
- SINGH G, BEDI J. A federated and transfer learning based approach for households load forecasting [J]. Knowledge-Based Systems, 2024, 299: 111967. [Google Scholar]
- BAI D, MA S, YANG X, et al. A recommendation model for optimizing transfer learning hyper-parameter settings in building heat load prediction with limited data samples [J]. Energy and Buildings, 2024, 325: 115021. [Google Scholar]
- LABIADH M, OBRECHT C, FERREIRA DA SILVA C, et al. A microservice-based framework for exploring data selection in cross-building knowledge transfer [J]. Service Oriented Computing and Applications, 2021, 15: 97-107. [Google Scholar]
- YAN R, ZHAO T, REZGUI Y, et al. Transferability and robustness of a data-driven model built on a large number of buildings [J]. Journal of Building Engineering, 2023, 80: 108127. [Google Scholar]
- CHAUDHARY G, JOHRA H, GEORGES L, et al. Transfer learning in building dynamics prediction [J]. Energy and Buildings, 2025, 330: 115384. [Google Scholar]
- LU H, WU J, RUAN Y, et al. A multi-source transfer learning model based on LSTM and domain adaptation for building energy prediction [J]. International Journal of Electrical Power & Energy Systems, 2023, 149: 109024. [Google Scholar]
- PINTO G, WANG Z, ROY A, et al. Transfer learning for smart buildings: A critical review of algorithms, applications, and future perspectives [J]. Advances in Applied Energy, 2022, 5: 100084. [Google Scholar]
- LI G, WANG Z, GAO J, et al. Performance assessment of cross office building energy prediction in the same region using the domain adversarial transfer learning strategy [J]. Applied Thermal Engineering, 2024, 241: 122357. [Google Scholar]
- LU Y, TIAN Z, ZHOU R, et al. A general transfer learning-based framework for thermal load prediction in regional energy system [J]. Energy, 2021, 217: 119322. [Google Scholar]
- JIANG B, LI Y, REZGUI Y, et al. Multi-source domain generalization deep neural network model for predicting energy consumption in multiple office buildings [J]. Energy, 2024, 299: 131467. [Google Scholar]
- ZHOU M, YU J, SUN F, et al. Forecasting of short term electric power consumption for different types buildings using improved transfer learning: A case study of primary school in China [J]. Journal of Building Engineering, 2023, 78: 107618. [Google Scholar]
- WEI B, LI K, ZHOU S, et al. An instance based multi-source transfer learning strategy for building’s short-term electricity loads prediction under sparse data scenarios [J]. Journal of Building Engineering, 2024, 85: 108713. [Google Scholar]
- LI X, YU J, ZHAO A, et al. Time series prediction method based on sub-metering in building energy performance evaluation [J]. Journal of Building Engineering, 2023, 72: 106638. [Google Scholar]
- BLANCO-MALLO E, MORáN-FERNáNDEZ L, REMESEIRO B, et al. Do all roads lead to Rome? Studying distance measures in the context of machine learning [J]. Pattern Recognition, 2023, 141: 109646. [Google Scholar]
- RAN X, XI Y, LU Y, et al. Comprehensive survey on hierarchical clustering algorithms and the recent developments [J]. Artificial Intelligence Review, 2023, 56(8): 8219-64. [Google Scholar]
- XIE J-L, LI Y-S, CAI G-L, et al. An improved Mahalanobis distance-based colour segmentation method for rural building recognition [J]. Journal of Mountain Science, 2018, 15(7): 1460-70. [Google Scholar]
- GOPALAKRISHNAN S, CHEN V Z, DOU W, et al. On the relation between K–L divergence and transfer learning performance on causality extraction tasks [J]. Natural Language Processing Journal, 2024, 6: 100055. [Google Scholar]
- GANIN Y, USTINOVA E, AJAKAN H, et al. Domain-adversarial training of neural networks [J]. Journal of machine learning research, 2016, 17(59): 1-35. [Google Scholar]
- CHA S-H. Comprehensive survey on distance/similarity measures between probability density functions [J]. City, 2007, 1(2): 1. [Google Scholar]
- ZHOU D, CAI T, LU J. Multi-source learning via completion of block-wise overlapping noisy matrices [J]. Journal of Machine Learning Research, 2023, 24(221): 1-43. [Google Scholar]
- FANG X, GONG G, LI G, et al. A general multi-source ensemble transfer learning framework integrate of LSTM-DANN and similarity metric for building energy prediction [J]. Energy and buildings, 2021, 252: 111435. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

