Logistics & Supply Chain Management: Faculty Presentations (1998-2020)
Automatic Forecasting of Hourly Electricity Demand with A Computationally Efficient Semi-Parametric Time Series Model
Document Type
Presentation
Presentation Date
6-25-2017
Copyright
This work is archived and distributed under the repository's Standard Copyright and Reuse License (opens in new tab). End users may copy, store, and distribute this work without restriction. For all other uses, permission must be obtained from the copyright owners or their authorized agents.
Abstract or Description
In this paper we develop a semi-parametric approach to model nonlinear relationship in time series data. The usefulness of this approach is illustrated on a hourly electricity demand data set. Polynomial splines are used to model the effect of temperature on hourly electricity demand for different times of the day and types of the day. An ARIMA model is used to model the serial correlation in the data. An algorithm is developed to automatically select the models, and the models are estimated through backfitting. Forecasting performance is evaluated using post-sample forecasting and comparative results are presented.
Sponsorship/Conference/Institution
International Chinese Statistical Association Applied Statistical Symposium (ICSA)
Location
Chicago, IL
Recommended Citation
Liu, Jun.
2017.
"Automatic Forecasting of Hourly Electricity Demand with A Computationally Efficient Semi-Parametric Time Series Model."
Logistics & Supply Chain Management: Faculty Presentations (1998-2020).
Presentation 165.
https://digitalcommons.georgiasouthern.edu/logistics-supply-facpres/165
