Abstract
Abstract The problem of projecting monthly residential natural gas sales and evaluating interannual changes in demand is investigated using a linear regression model adjusted monthly. with lagged monthly heating degree-days as the independent variable. The relationship between sales and degree-day data for customers of Columbia Gas Company (serving the Columbus, Ohio, area) is studied for a 20-yr period ending in June 1990. Analysis of the phases of the monthly billed sales and the degree-day data indicated that monthly sales reports lagged degree-days and gas consumption by 15 days on average. Running 12-month regressions of Columbia Gas sales on 15-day-lagged degree-days show that lagged degree-days explain, on average, 97% of the variability in the monthly sales reports for the study years. Annualized trends in the regression coefficients indicate changes in consumption due to conservation and changes in price. Since 197475 the trends indicate declines of 50% in non-weather- sensitive sales per custom...
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