Template-Type: ReDIF-Article 1.0
Author-Name:  Marco Barassi and Yuqian Zhao
Title:  Combination Forecasting of Energy Demand in the UK
Classification-JEL: F0
Volume:  Volume 39
Issue:  Special Issue 1
Year:  2018
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. 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). Our results show that model averaging gives always a lower Mean Square Forecast Error (MSFE) than the best/optimal models within each class however selected. For example, for Coal, Wind and Hydro generated Electricity forecasts generated with model averaging, we report a MSFE about 12% lower than that obtained using the best selected individual models. Among these, the best individual forecasting models are the Non-Linear Artificial Neural Networks and the Vector Autoregression and that models selected by the Jackknife have often superior performance. However, MMA averaged forecasts almost always beat the predictions obtained from any of the individual models however selected, and those generated by other model averaging techniques.
Handle: RePEc:aen:journl:ej39-si1-Barassi
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