Combination Forecasting of Energy Demand in the UK

38 Pages Posted: 30 Jan 2017

See all articles by Marco R. Barassi

Marco R. Barassi

University of Birmingham

Yuqian Zhao

University of Essex

Date Written: January 28, 2017

Abstract

In more deregulated markets such as the UK, demand forecasting is vital for the electric industry as it is used to set electricity generation and purchasing, establishing electricity prices, load switching and demand response. In this paper we produce improved short-term forecasts of the demand for energy produced from five different sources in the UK averaging from a set of 6 univariate and multivariate models comprising ARMA, Holt Winters, Non Linear Autoregressive Neural Networks (NLANN), Vector Autoregressions (VAR), Bayesian VAR and Factor Augmented VAR (FAVAR). The forecasts are averaged using six different weighting functions including Simple Model Averaging (SMA), Granger-Ramanathan Model Averaging (GRMA), Bayesian Model Averaging (BMA), Smoothing Akaike (SAIC), Mallows Weights (MMA) and Jackknife (JMA). We find that the best individual forecasting models are the NLANN and VAR and optimal forecasting models selected by the Jackknife have often superior performance compared to others. However, BMA and JMA almost always beat the predictions obtained from any of the individual models however selected.

Keywords: Demand for Energy; Forecasting; Model Averaging

JEL Classification: C53, C55, Q47

Suggested Citation

Barassi, Marco R. and Zhao, Yuqian, Combination Forecasting of Energy Demand in the UK (January 28, 2017). Available at SSRN: https://ssrn.com/abstract=2907296 or http://dx.doi.org/10.2139/ssrn.2907296

Marco R. Barassi (Contact Author)

University of Birmingham ( email )

Birmingham B15 2TT, Birmingham B15 2TT
United Kingdom

Yuqian Zhao

University of Essex ( email )

Essex Business School
Wivenhoe Park
Colchester, CO4 3SQ
United Kingdom
CO4 3SQ (Fax)

HOME PAGE: http://https://www.essex.ac.uk/people/zhaoy37900/yuqian-zhao

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