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Functional time series approach for forecasting very short-term electricity demand

Date

2013

Authors

Shang, Hanlin

Journal Title

Journal ISSN

Volume Title

Publisher

Taylor & Francis

Abstract

This empirical paper presents a number of functional modelling and forecasting methods for predicting very short-term (such as minute-by-minute) electricity demand. The proposed functional methods slice a seasonal univariate time series (TS) into a TS of curves; reduce the dimensionality of curves by applying functional principal component analysis before using a univariate TS forecasting method and regression techniques. As data points in the daily electricity demand are sequentially observed, a forecast updating method can greatly improve the accuracy of point forecasts. Moreover, we present a non-parametric bootstrap approach to construct and update prediction intervals, and compare the point and interval forecast accuracy with some naive benchmark methods. The proposed methods are illustrated by the half-hourly electricity demand from Monday to Sunday in South Australia.

Description

Keywords

functional principal component analysis, multivariate time series, ordinary least-squares regression, penalised least-squares regression, roughness penalty, seasonal time series

Citation

Source

Journal of Applied Statistics

Type

Journal article

Book Title

Entity type

Access Statement

License Rights

DOI

10.1080/02664763.2012.740619

Restricted until

2037-12-31