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Combination Forecasting of Energy Demand in the UK

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  • Marco Barassi and Yuqian Zhao

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.

Suggested Citation

  • Marco Barassi and Yuqian Zhao, 2018. "Combination Forecasting of Energy Demand in the UK," The Energy Journal, International Association for Energy Economics, vol. 0(Special I).
    • Handle: RePEc:aen:journl:ej39-si1-barassi
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    Cited by:

    1. Dahiru A Bala & Mohammed Shuaibu, 2024. "Forecasting United Kingdom's energy consumption using machine learning and hybrid approaches," Energy & Environment, , vol. 35(3), pages 1493-1531, May.
    2. Chaturvedi, Shobhit & Rajasekar, Elangovan & Natarajan, Sukumar & McCullen, Nick, 2022. "A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India," Energy Policy, Elsevier, vol. 168(C).
    3. Kexin Peng & Chao Kang & Xiwen Ru & Ligang Zhou, 2024. "The optimal interval combination prediction model based on vectorial angle cosine and a new aggregation operator for social security level prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 490-505, March.

    More about this item

    JEL classification:

    • F0 - International Economics - - General

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