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 language | English |
---|---|
Pages (from-to) | 152-168 |
Number of pages | 17 |
Journal | Journal of Applied Statistics |
Volume | 40 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2013 |
Externally published | Yes |
Keywords
- functional principal component analysis
- multivariate time series
- ordinary least-squares regression
- penalised least-squares regression
- roughness penalty
- seasonal time series