Forecasting United Kingdom's energy consumption using machine learning and hybrid approaches

Abstract

Investigating the current and future dynamics of energy consumption in modern economies such as the UK is crucial. This paper predicts the UK's energy consumption using data spanning January 1995 to March 2022 by comparing and evaluating the forecast performance of machine learning, dynamic regression, time series and combination modelling techniques. The analysis reveals that the seasonal ARIMA and TBATS hybrid models yield the lowest forecast errors in predicting the UK's electricity and gas consumption. Although the combination forecasts performed poorly relative to other models, machine learning techniques such as neural network and support vector regression produced better results compared to the dynamic regression models, whereas the seasonal hybrid model performed better than the machine learning and time series models. The results indicate that the UK's electricity consumption would either stabilise or decline over the forecast horizon, suggesting that it will take some years for electricity consumption to attain pre-2019 levels. For gas consumption, the results indicate that consumption would either maintain current levels or increase over the forecast period. We also show that combination forecasts do not often generate the best predictions, and therefore, choice of methodology matters in energy consumption forecasting. Overall, changing seasonal patterns, energy efficiency improvements, shift to renewable sources and uncertainties due to the COVID-19 pandemic, Brexit, and the Russia–Ukraine crisis appear to be significant drivers of energy consumption in the UK in recent times. These findings are expected to help in designing more effective energy policies as well as guide investor decisions in the energy sector.