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    Daily average load forecasting using dynamic linear regression

    Access Status
    Fulltext not available
    Authors
    Azad, S.
    Ali, A.
    Wolfs, Peter
    Date
    2014
    Type
    Conference Paper
    
    Metadata
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    Citation
    Azad, S. and Ali, A. and Wolfs, P. 2014. Daily average load forecasting using dynamic linear regression.
    Source Title
    Asia-Pacific World Congress on Computer Science and Engineering, APWC on CSE 2014
    DOI
    10.1109/APWCCSE.2014.7053851
    ISBN
    9781479919550
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/55212
    Collection
    • Curtin Research Publications
    Abstract

    © 2014 IEEE. Load forecasting plays a vital role in demand management. The primary goal of demand management strategy is to shave the peak load in order to reduce the dependency on the peaking plants and to avoid the overloading of the transmission and distribution equipment. Battery storage can also be utilized for peak shaving by storing excess energy during the off-peak and consuming battery energy during peak hours. For effective battery use, the battery management system must have the accurate forecast of the load demand. This paper proposes a dynamic regression scheme to predict the average daily load of a feeder so that the battery management system can decide the amount of charging and discharging required at each instant. Forecasting of average daily load rather than point forecast of load demand at every hour avoids the complexity of battery scheduling and reduces the computational effort. This paper uses Perth solar city data to showcase the effectiveness of dynamic regression for forecasting future loads.

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