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

Han Lin Shang*

*Corresponding author for this work

Research output: Contribution to journalArticle

20 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)152-168
Number of pages17
JournalJournal of Applied Statistics
Volume40
Issue number1
DOIs
Publication statusPublished - 2013
Externally publishedYes

Keywords

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

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