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In this paper, the relationship between the Gross Domestic Product (GDP), airtemperature variations and power consumption is evaluated using the linearregression and Wavelet Coherence (WTC) approach on a 1971-2011 time series forthe United Kingdom (UK). The results based on the linear regression approachindicate that some 66% variability of the UK electricity demand can beexplained by the quarterly GDP variations, while only 11% of the quarterlychanges of the UK electricity demand are caused by seasonal air temperaturevariations. WTC however, can detect the period of time when GDP and airtemperature significantly correlate with electricity demand and the results ofthe wavelet correlation at different time scales indicate that a significantcorrelation is to be found on a long-term basis for GDP and on an annual basisfor seasonal air-temperature variations. This approach provides an insight intothe properties of the impact of the main factors on power consumption on thebasis of which the power system development or operation planning andforecasting the power consumption can be improved.

Insight into the Properties of the UK Power Consumption Using a Linear Regression and Wavelet Transform Approach