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|Title:||Climatic Influences on Residential Energy Consumption|
|Author(s):||Cohen, Stewart Jay|
|Department / Program:||Geography|
|Degree Granting Institution:||University of Illinois at Urbana-Champaign|
|Abstract:||A new climatic index, applicable to the estimation of residential energy consumption, has been developed. The index is based on seasonal frequencies of upper-air (500-mb height) circulation patterns. Upper air circulation patterns are related to surface weather conditions, in that the movement of surface weather systems is governed (to some degree) by the direction of upper air winds. Thus, the frequency value is a surrogate, representing the occurrence of a surface weather environment, unlike many present climatic indices which are largely based on air temperature alone.
A multivariate statistical technique, principal component analysis with oblique rotation of eigenvectors, was used to classify 33 years (1946-1979) of daily 500-mb height anomaly patterns into 30 components. Each component represents two modes, positive and inverse, thereby resulting in sixty 500-mb types. Additional aggregation procedures reduced the number of types to 48. 78% of all days were categorized by this method.
500-mb type frequencies and degree days were compared as predictors of natural gas consumption. The energy data base consisted of 1960-1978 annual per household natural gas consumption for each state in the continental U.S. (except Alaska), and the District of Columbia. Two sets of regression analyses were performed on the data base, one set using 500-mb type frequencies, the other using population weighted heating degree days (POPHDD) and unweighted heating degree days (HDD).
Results show that the 500-mb type frequency is a better climatic index of long term residential natural gas consumption than either HDD or POPHDD. Unlike the degree day results, significant linear relationships have been computed in each state using the 500-mb models. The regression slopes and Y-intercepts of the 48 models exhibit spatial continuity. It is suggested that the median intercepts of the 48 models represent "average weather" demand, which is relatively high in the Great Lakes and mid-continent regions, and relatively low in coastal states, New England, Wisconsin, and Minnesota. Negative regression slopes are interpreted as "low demand" weather, while positive regression slopes indicate "high demand" weather. The absolute magnitudes of these slopes indicate relative sensitivity (or elasticity) or residential gas consumption to weather. Areas with high sensitivity are the northwest, the Rocky Mountain states, New England, and portions of the southern Appalachians and the southern Mississippi basin. Areas with low sensitivity are the Gulf Coast, California, and portions of the Great Lakes and the northern Appalachians.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1981.
|Date Available in IDEALS:||2014-12-14|
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Dissertations and Theses - Geography and Geographic Information Science
Graduate Dissertations and Theses at Illinois
Graduate Theses and Dissertations at Illinois