A novel building cooling load prediction based on SVR and SAPSO-Reference-Cited by-同舟云学术

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A novel building cooling load prediction based on SVR and SAPSO

Published:2010-05 Issue: Volume: Page:
ISSN:
Container-title:2010 International Symposium on Computer, Communication, Control and Automation (3CA)
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Author:

Xuemei Li,Lixing Ding,Lanlan Li

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx5/5504583/5533710/05533863.pdf?arnumber=5533863

Cited by 11 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. Energy efficiency in cooling systems: integrating machine learning and meta-heuristic algorithms for precise cooling load prediction;Chemical Product and Process Modeling;2024-07-01

2. Data-driven modelling techniques for earth-air heat exchangers to reduce energy consumption in buildings: a review;Environmental Chemistry Letters;2021-08-30

3. A review of machine learning in building load prediction;Applied Energy;2021-03

4. A Data-Driven Approach to Forecasting Heating and Cooling Energy Demand in an Office Building as an Alternative to Multi-Zone Dynamic Simulation;Applied Sciences;2021-02-03

5. Machine Learning for Building Energy Forecasting;Green Energy and Technology;2021

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