Federated learning-based short-term building energy consumption prediction method for solving the data silos problem-Reference-Cited by-同舟云学术

Federated learning-based short-term building energy consumption prediction method for solving the data silos problem

Published:2021-12-10 Issue: Volume: Page:
ISSN:1996-3599
Container-title:Building Simulation
language:en
Short-container-title:Build. Simul.

Author:

Li Junyang,Zhang Chaobo,Zhao Yang,Qiu Weikang,Chen Qi,Zhang Xuejun

Publisher

Springer Science and Business Media LLC

Subject

Energy (miscellaneous),Building and Construction

Link

https://link.springer.com/content/pdf/10.1007/s12273-021-0871-y.pdf

Reference68 articles.

1. Ahmad MW, Mourshed M, Rezgui Y (2017). Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption. Energy and Buildings, 147: 77–89.

2. Amasyali K, El-Gohary N (2021). Machine learning for occupant-behavior-sensitive cooling energy consumption prediction in office buildings. Renewable and Sustainable Energy Reviews, 142: 110714.

3. Arjunan P, Poolla K, Miller C (2020). EnergyStar++: Towards more accurate and explanatory building energy benchmarking. Applied Energy, 276: 115413.

4. ASHRAE (2013). ASHRAE Standard 169-2013. Climatic Data for Building Design Standards. GA, Atlanta, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.

5. ASHRAE (2014). ASHRAE Guideline 14-2014. Measurement of Energy, Demand, and Water Savings. GA, Atlanta, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.

Cited by 45 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Federated learning framework for prediction of net energy demand in transactive energy communities;Sustainable Energy, Grids and Networks;2024-12

2. Credit risk prediction for small and micro enterprises based on federated transfer learning frozen network parameters;Journal of Network and Computer Applications;2024-12

3. Residential building energy consumption estimation: A novel ensemble and hybrid machine learning approach;Expert Systems with Applications;2024-10

4. A prior-knowledge-based time series model for heat demand prediction of district heating systems;Applied Thermal Engineering;2024-09

5. A review of federated learning in renewable energy applications: Potential, challenges, and future directions;Energy and AI;2024-09