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  • Novel autoregressive basis structure model for short-term forecasting of customer electricity demand

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    88739_1.pdf (515.6Kb)
    Author(s)
    Bennett, Christopher
    Stewart, Rodney
    Lu, Junwei
    Griffith University Author(s)
    Lu, Junwei
    Stewart, Rodney A.
    Year published
    2013
    Metadata
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    Abstract
    This paper describes the method of a prototype forecast component of the energy resource management control algorithm for STATCOMs with battery energy storage. It is desired to be computationally efficient and of minimal complexity due to the desired purposes of forecasting each load in a LV network. The forecast model is comprised of a basis structure selected from observed electricity demand data and an electricity demand difference forecasting component estimated by the autoregressive method. The produced forecasting model had a R2 of 0.65 and a standard error of 368.55 W. During validation of the model, discrepancies ...
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    This paper describes the method of a prototype forecast component of the energy resource management control algorithm for STATCOMs with battery energy storage. It is desired to be computationally efficient and of minimal complexity due to the desired purposes of forecasting each load in a LV network. The forecast model is comprised of a basis structure selected from observed electricity demand data and an electricity demand difference forecasting component estimated by the autoregressive method. The produced forecasting model had a R2 of 0.65 and a standard error of 368.55 W. During validation of the model, discrepancies between the forecasted and observed electricity demand profiles were observed. To overcome forecast model limitations, future work will involve more precise clustering of demand profiles according to additional temporal and environmental variables. This is to enable forecasts under a more diverse range of electricity demand profiles. The final developed forecasting model will be a core component of the firmware controlling STATCOMS with energy storage systems.
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    Conference Title
    2013 IEEE TENCON SPRING CONFERENCE
    DOI
    https://doi.org/10.1109/TENCONSpring.2013.6584418
    Copyright Statement
    © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Numerical analysis
    Electrical energy generation (incl. renewables, excl. photovoltaics)
    Engineering design
    Publication URI
    http://hdl.handle.net/10072/53555
    Collection
    • Conference outputs

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