An iterative approach for weather-correcting energy consumption data

Energy consumption data are influenced by the weather conditions. The use of weather unadjusted energy data in analysis and forecasting could give misleading and erroneous results. In this paper a new iterative econometric technique is developed to correct for abnormal weather conditions in published energy consumption data. The technique is applied to sectoral natural gas sales data for the province of Ontario, Canada. The empirical results reveal that following the methods available in the literature results in weather-corrected data that still contain a significant portion of the abnormal weather impact. In addition, the study suggests that residential and commercial natural gas data require more weather correction than the data for the industrial sector.