Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks-Reference-Cited by-同舟云学术

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Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks

Published:2019-04 Issue: Volume:240 Page:276-294
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Wei Yixuan^ORCID,Xia Liang,Pan Song,Wu Jinshun,Zhang Xingxing^ORCID,Han Mengjie,Zhang Weiya,Xie Jingchao,Li Qingping

Funder

13th Five-Year National Science and Technology Major Project of China

Beijing Natural Science Foundation

National Natural Science Foundation of China

Engineering Research Center of Digital Community

Ministry of Education

Beijing Laboratory for Urban Mass Transit

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference93 articles.

1. Methods for benchmarking building energy consumption against its past or intended performance: an overview;Li;Appl Energy,2014

2. Comparison of typical year and multiyear building simulations using a 55-year actual weather data set from China;Cui;Appl Energy,2017

3. EKF based self-adaptive thermal model for a passive house;Fux;Energy Build,2014

4. A hybrid model approach for forecasting future residential electricity consumption;Dong;Energy Build,2016

5. A review on applications of ANN and SVM for building electrical energy consumption forecasting;Ahmad;Renew Sustain Energy Rev,2014

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