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Richard L. Lehman
and
Henry E. Warren

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 1974–75 the trends indicate declines of 50% in non-weather- sensitive sales per customer, and 35% in monthly sales per degree-day per customer, with most of the changes occurring prior to 1985. The mode is adapted by using a regression equation based on historical data through the prior 12 months with degree-days as the independent variable. Estimates for sales in the coming period are based on official National Oceanic and Atmospheric Administration (NOAA) monthly temperature outlooks (outlooks) for the Columbus region. For comparison purposes, four lagged monthly degree-day sets are used in a model: 1) a set of degree-day normals, 2) a set of 100% projected degree-day values obtained by use of NOAA outlooks, 3) a set in which the first half of the degree-days in each monthly period are observations and the second half are projected, and 4) a set that is 100% observed (the perfect case). The skill of the degree-day sets for projecting monthly sales is evaluated by a statistical analysis of the projection errors (differences between projected and reported sales). Errors from the sales projection models using the four different degree-day sets are compared with errors from two sets of baseline sales. The first set of baseline sales is estimated with and the second set without foreknowledge of monthly sales norms and annual total sales. The models using partially and fully projected degree-days are found to have measurable skill over models using climatology in projecting monthly gas sales during the heating season.

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Henry E. Warren
and
Sharon K. LeDuc

Abstract

The NOAA/EDIS Center for Environmental Assessment Services conducted a benefit-cost study of its publication on assessments of crop-related weather information for foreign countries. As a background for the investigation, a review was conducted of the literature on the theory and methods related to the value and use of information in government programs and private marketing and consumption. Using guidelines suggested by the literature, a questionnaire was designed and the subscribers qualitative and quantitative responses evaluated to determine use of the publication, perceived benefits, and willingness to pay for the publication.

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Henry E. Warren
and
Sharon K. LeDuc

Abstract

Assessments of economic conditions by region or sector attempt to include relevant climatic variability through residual adjustment techniques. There is no direct consideration of climatic fluctuations. Three recent severe winters combined with the increasing price of energy have intensified the need to quantify the interaction of climate with the energy sector of the economy. This paper presents examples of the uses of climatic data by utilities, public service commissions and the NOAA Center for Environmental Assessment Services to determine econoclimatic energy relationships at the local, state, regional and national levels. A technique based on the linear relationship between heating degree days and natural gas consumption for space heating is used to quantify the interaction of climate and prices on gas consumption. This provides regional estimates of the response of gas consumption to degree days and price.

Climate alone does not explain all of the variations in energy consumption. Price and seasonal adjustments are necessary, as are considerations of the various classes of consumers. However, in any assessment of our economy it is critical to account for fluctuations due to known causes such as climate.

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Douglas M. Le Comte
and
Henry E. Warren

Abstract

National population-weighted weekly degree day totals, which have been used to model and assess temperature-related natural gas consumption, are compared with summertime electricity consumption. A very close relationship between national cooling degree days and electricity consumption is found. A multiple regression equation depicting the relationship is developed. This model can be used to assess the impact of current weather anomalies and projected weather or climate changes on electricity use, as well as the impact of various national conservation measures, directives, or laws on temperature-related electricity use.

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