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

Han Lin Shang

Journal of Applied Statistics, 2013, vol. 40, issue 1, 152-168

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.

Date: 2013
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Citations: View citations in EconPapers (17)

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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:40:y:2013:i:1:p:152-168

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DOI: 10.1080/02664763.2012.740619

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