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    Computationally efficient forecasting procedures for Kuhn-Tucker consumer demand model systems: Application to residential energy consumption analysis

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    Date
    2011-07
    Author
    Pinjari, Abdul Rawoof
    Bhat, Chandra R.
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    Abstract
    This paper proposes simple and computationally efficient forecasting algorithms for a Kuhn- Tucker (KT) consumer demand model system called the Multiple Discrete-Continuous Extreme Value (MDCEV) model. The algorithms build on simple, yet insightful, analytical explorations with the Kuhn-Tucker conditions of optimality that shed new light on the properties of the model. Although developed for the MDCEV model, the proposed algorithm can be easily modified to be used for other KT demand model systems in the literature with additively separable utility functions. The MDCEV model and the forecasting algorithms proposed in this paper are applied to a household-level energy consumption dataset to analyze residential energy consumption patterns in the United States. Further, simulation experiments are undertaken to assess the computational performance of the propose d (and existing) KT demand forecasting algorithms for a range of choice situations with small and large choice sets.
    Department
    Civil, Architectural, and Environmental Engineering
    Description
    At the time of publication, A.R. Pinjari was at the University of South Florida, and C. Bhat was at the University of Texas at Austin.
    Subject
    discrete-continuous models
    Kuhn-Tucker consumer demand systems
    MDCEV model
    forecasting procedure
    residential energy consumption
    climate change impacts
    welfare analysis
    URI
    http://hdl.handle.net/2152/23880
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    • facebook
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    • youtube
    • CONTACT US
    • MAPS & DIRECTIONS
    • JOB OPPORTUNITIES
    • UT Austin Home
    • Emergency Information
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    • Web Accessibility Policy
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    • Adobe Reader
    Subscribe to our NewsletterGive to the Libraries

    © The University of Texas at Austin