A data-driven interval forecasting model for building energy prediction using attention-based LSTM and fuzzy information granulation-Reference-Cited by-同舟云学术

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A data-driven interval forecasting model for building energy prediction using attention-based LSTM and fuzzy information granulation

Published:2022-01 Issue: Volume:76 Page:103481
ISSN:2210-6707
Container-title:Sustainable Cities and Society
language:en
Short-container-title:Sustainable Cities and Society

Author:

Li Yue,Tong Zheming,Tong Shuiguang,Westerdahl Dane

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Transportation,Renewable Energy, Sustainability and the Environment,Civil and Structural Engineering,Geography, Planning and Development

Reference61 articles.

1. Algorithms for hyper-parameter optimization;Bergstra;Advances in Neural Information Processing Systems,2011

2. Modeling and forecasting building energy consumption: A review of data-driven techniques;Bourdeau;Sustainable Cities and Society,2019

3. Performance degradation prediction of mechanical equipment based on optimized multi-kernel relevant vector machine and fuzzy information granulation;Chen;Measurement,2020

4. Achieving natural ventilation potential in practice: Control schemes and levels of automation;Chen;Applied energy,2019

5. Modeling heating and cooling loads by artificial intelligence for energy-efficient building design;Chou;Energy and Buildings,2014

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