Abstract: In this paper, the results of correlations between air temperature and electricity demand by linear regression and Wavelet Coherence (WTC) approach for three different European countries are presented. The results show a very close relationship between air temperature and electricity demand for the selected power systems, however, the WTC approach presents interesting dynamics of correlations between air temperature and electricity demand at different time-frequency space and provide useful information for a more complete understanding of the related consumption. Key words: power system, electricity demand, air temperature, linear regression, wave-let coherence 1. Introduction The electric power system is a very complex system. It is composed of a large number of different elements such as generators, lines, transformers, etc., but it also has a lot of different consumption categories such as household, industrial, transportation, and others. Short, me-dium or long-term planning requires thorough understanding of consumption characteristics which are defined by their load curves. The load curves represent characteristics of the power system’s load variability as a function of time, and their analysis can be performed for some customer groups, some geographic areas or the power system as a whole, where it is very important to identify the main factors affecting the consumption of electricity, such as growth and structure of Gross Domestic Product (GDP), demographic change, housing standard, the

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