The electricity demand is significantly dependent on the weather information. Such weather information is comprised of different climatic variables such as temperature, humidity, wind speed, evaporation, rain fall and solar exposure which constantly change. Therefore, analysing the impacts of these variables on demand is necessary for predicting the future change in demand. In this paper, the cooling and heating degree days are utilised to capture the relationship between the per capita demand to temperature, which is one of the key climatic variables. In addition, Pearson correlation analysis has been employed to investigate the interdependency between different climatic variables and electricity demand. Finally, back-ward elimination based multiple regression is used to exclude non-significant climatic variables and evaluate the sensitivity of significant variables to the electricity demand. A case study has been reported in this paper by acquiring the data from the state of New South Wales, Australia. The results reveal that the climatic variables such as heating degree days, humidity, evaporation, and wind speed predominantly affect the electricity demand of the state of New South Wales.