UK ENERGY CONSUMPTION

UK Energy Consumption

UK Energy Consumption

Introduction

In past thirty years, consumption of electricity has increased twice as fast as overall energy consumption. It amounts to 424 billion annual kilowatt hours (DECC, 2012). To represent the importance of this figure, it is necessary to imagine that the electrical energy that people consumed in the last twelve months would have sufficed in this time, to operate simultaneously and continuously billion washing machines. Electrical energy is almost half the energy people use. Finally, note that two-thirds of the electricity consumption is devoted to residential, industrial, and transport sector. 

Global oil demand will increase as world economic growth continues to accelerate over the five years to 2016. Emerging economies will grow the quickest as these countries build out essential infrastructure and their citizens increase consumption. China and, to a lesser extent, India will continue to lead demand over the next five years. In turn, their robust growth will push oil prices upward (DECC, 2012). As global demand increases, more firms will locate near such emerging economies. Oil price movements, coupled with changes in the volume of production, play a key role in determining the energy consumption.

This paper presents an analysis of energy consumption in the UK. Annual data for has been collected from year 1970 to year 2010 from Department of Energy and Climate Change.

Methodology

In order to assess the trend of the energy consumption in the UK, multiple approaches can be applied depending upon the future prospects. Autoregressive Integrated Moving Average Model ARIMA considers equalize variances for the data. It also requires stationary series data which makes the expected value of series independent of time (Corbin, 2008). In some situations, non-stationary data can be converted to stationary data by removing the seasonal differences and incorporating moving average trend through ARIMA modelling technique. However, in that case Exponential Smoothing of the data for managing the seasonality component and sequential trend would need to be applied (Balnaves, 2007).

Trend cycle component increases the variation between data if seasonal trend remain unadjusted. This requires normalizing the irregular or random component factors to enhance the prediction of sequential output for the given case (Balnaves, 2007). Increase variation between data figures will require moving regression trend curve to assess the effectiveness of forecasted value and actual values against forecasted ones.

Considering the impact of seasonality and trend component for the given case, Expert Modeler methodology will provide the best fitting model for analysing the future trend. Expert Modeler transforms the data using square root or log transformation in order to have statistically significant relationship with dependent series. It involves both ARIMA and Exponential Smoothing of data, which makes this trend curve fit for forecasting the output of energy consumption. It excludes the trend of seasonality by identifying the errors component associated with the seasonal time (Armstrong, 2001). It provides adjusted seasonal series for the data that increases the probability to predict the seasonal factor component effectively; providing significant value in forecasting even in uncertain situations (Silverman, 2004). Error identified in this modelling technique can be adjusted in forecasted ...