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    Forecasting Gas Usage for Big Buildings using Generalized Additive Models and Deep Learning

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    smartcomp18_nilavra.pdf (710.5Kb)
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    http://mpsc.umbc.edu/wp-content/uploads/2018/05/smartcomp18_nilavra.pdf
    Permanent Link
    http://hdl.handle.net/11603/11040
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    Author/Creator
    Pathak, Nilavra
    Ba, Amadou
    Ploennings, Joern
    Roy, Nirmalya
    Date
    2018
    Type of Work
    8 pages
    Text
    conference papers and proceedings preprints
    Rights
    This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.
    Subjects
    gas forecasting
    deep learning
    long short term memory
    generalized additive model
    forecasting
    Time series behavior of gas consumption is highly irregular, non-stationary, and volatile due to its dependency on the weather, users’ habits and lifestyle. This complicates the modeling and forecasting of gas consumption with most of the existing time series modeling techniques, specifically when missing values and outliers are present. To demonstrate and overcome these problems, we investigate two approaches to model the gas consumption, namely Generalized Additive Models (GAM) and Long Short-Term Memory (LSTM). We perform our evaluations on two building datasets from two different conti-nents. We present each selected feature’s influence, the tuning parameters, and the characteristics of the gas consumption on their forecasting abilities. We compare the performances of GAM and LSTM with other state-of-the-art forecasting approaches. We show that LSTM outperforms GAM and other existing approaches, however, GAM provides better interpretable results for building management systems (BMS).
    Abstract
    Time series behavior of gas consumption is highly irregular, non-stationary, and volatile due to its dependency on the weather, users’ habits and lifestyle. This complicates the modeling and forecasting of gas consumption with most of the existing time series modeling techniques, specifically when missing values and outliers are present. To demonstrate and overcome these problems, we investigate two approaches to model the gas consumption, namely Generalized Additive Models (GAM) and Long Short-Term Memory (LSTM). We perform our evaluations on two building datasets from two different conti-nents. We present each selected feature’s influence, the tuning parameters, and the characteristics of the gas consumption on their forecasting abilities. We compare the performances of GAM and LSTM with other state-of-the-art forecasting approaches. We show that LSTM outperforms GAM and other existing approaches, however, GAM provides better interpretable results for building management systems (BMS).

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    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3544


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.