Yiming Zhang

Nanjing Tech University

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Ruping Shao

Nanjing Tech University

H. Sun

Southwest Petroleum University

Sen Zhou

Chongqing Metrology Quality Inspection and Research Institute

Peng Jiang

Hohai University

Pengchun Li

Nanjing Tech University

Shawn Dawley

University of Alabama

Jing Yang

Nanjing Tech University

Jing Yang

Zhengzhou University

Xinhe Liu

Macau University of Science and Technology

Cooperative Institutions

Nanjing Tech University

Southwest Petroleum University

Chongqing Metrology Quality Inspection and Research Institute

Yanshan University

LinkedIn (United States)

Huazhong University of Science and Technology

China Tobacco

NetEase (China)

National University of Defense Technology

University of California, Irvine

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Research on Building Energy Consumption Prediction Based on Extreme Time Series Prediction

Sen Zhou Ruping Shao Jing Yang Yiming Zhang Pengchun Li

In the study of building energy consumption prediction, short-term prediction is more extensive. Based on short-term prediction results, the future development and operation mode of buildings can be adjusted, thereby achieving better and faster energy allocation and building fault analysis. This is of great significance for achieving future green and smart buildings. In order to better utilize the information in feature data and improve prediction accuracy, this article adds an Auto Encoding layer to the LSTM model, expanding a single feature to multiple dimensions when there are fewer features. Experiments have shown that compared to traditional models, the LSTM+Auto Encoder model based on deep learning for extreme time series prediction has better prediction performance.

Predictive modelling

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Extreme Learning Machine

Autoencoder

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10.1145/3650215.3650323

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Statistical and probability analysis of surface water and groundwater time series in theLake Tuscaloosa Watershed, Alabama

AGU Fall Meeting Abstracts (2018)

Shawn Dawley Yiming Zhang Xinhe Liu Peng Jiang H. Sun

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