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Industrial and Commercial Demand for Electricity by Time-of-Day: A California Case Study

Chinbang Chung, Dennis J. Aigner

Year: 1981
Volume: Volume 2
Number: Number 3
DOI: 10.5547/ISSN0195-6574-EJ-Vol2-No3-7
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Abstract:
Recently there has been much interest in time-of-use (TOU) pricing structures for electric utilities. TOU pricing reflects more closely than conventional pricing the cost components of supplying electricity, which vary over the course of a single day as well as over days of the week and seasons of the year. Although such pricing structures have long been used in Europe, they did not receive much attention in the United States prior to 1974.



Testing the Joint Billing Effect Hypothesis

Dennis M. Keane and Dennis J. Aigner

Year: 1982
Volume: Volume 3
Number: Number 3
DOI: 10.5547/ISSN0195-6574-EJ-Vol3-No3-7
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Abstract:
With the recent national emphasis on energy conservation, greater attention has been focused on the ability of natural gas and electric utilities to induce customers to alter their consumption habits through pricing policies. As a consequence, a great deal of research has been done recently aimed to measure residential households' consumption responses to changes in energy prices. One of the important unresolved issues arising from this research concerns the possible existence of a subset of households that, because of the way they purchase their energy inputs, behave differently from other households.



An Analysis of Commercial and Industrial Customer Response to Time-of-Use Rates

Joseph G. Hirschberg and Dennis J. Aigner

Year: 1983
Volume: Volume 4
Number: Special Issue
DOI: 10.5547/ISSN0195-6574-EJ-Vol4-NoSI-7
No Abstract



Conditional Demand Analysis for Estimating Residential End-Use Load Profiles

Dennis J. Aigner, Cynts Sorooshian, and Pamela Kerwin

Year: 1984
Volume: Volume 5
Number: Number 3
DOI: 10.5547/ISSN0195-6574-EJ-Vol5-No3-6
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Abstract:
This paper reports some preliminary results from an ongoing study that uses regression methods to break down total household load into its constituent parts, each associated with a particular electricity-using end use or appliance. The data base used for this purpose consists of 15-minute integrated demand readings on a random sample of statistical control group customers from the Los Angeles Department of Water and Power TOD (time of day)-pricing experiment for the months of August 1978 (132 customers), 1979 (108 customers), and 1980 (80 customers). Twenty-four regression equations are fitted, each one aimed at explaining variation in the time-averaged load (averaged over days of the month) over customers as a function of temperature, house size, and binary indicator variables that indicate the presence or absence of each of the end uses of interest. This sort of method for extracting the individual contributions of end uses to total household load has become known as conditional demand analysis (Parti and Parti, 1981). The success of this method for isolating end-use loads statistically, without direct metering of the appliance, depends crucially on whether the ownership patterns of appliances are well mixed. For example, if (as in our sample) everyone owns at least one refrigerator, it will be impossible to isolate refrigerator load. Similarly,





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