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The relationship between weather var...

Tribble, Ahsha N.

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The relationship between weather variables and electricity demand to improve short-term load forecasting.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The relationship between weather variables and electricity demand to improve short-term load forecasting./
作者:	Tribble, Ahsha N.
面頁冊數:	221 p.
附註:	Source: Dissertation Abstracts International, Volume: 64-03, Section: B, page: 1292.
Contained By:	Dissertation Abstracts International64-03B.
標題:	Physics, Atmospheric Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3082952

The relationship between weather variables and electricity demand to improve short-term load forecasting.
Tribble, Ahsha N.

The relationship between weather variables and electricity demand to improve short-term load forecasting. - 221 p.

Source: Dissertation Abstracts International, Volume: 64-03, Section: B, page: 1292.

Thesis (Ph.D.)--The University of Oklahoma, 2003.

The power utility industry has become highly volatile with a deregulated market on the horizon and with enormous profit and loss swings in the energy trading market. Electricity, in particular, has become a commodity that is bought and sold at market prices, where load forecasting plays a crucial role in the composition of those prices. Public and private utilities must contend with the fact that a small error in an electric load forecast can create a large financial loss for the company. Hence, improving the accuracy of electricity load forecasts has become necessary for the long-term viability of all power utilities.Subjects--Topical Terms:

1019431
Physics, Atmospheric Science.

The relationship between weather variables and electricity demand to improve short-term load forecasting.
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245 1 0 $a The relationship between weather variables and electricity demand to improve short-term load forecasting.
300 $a 221 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-03, Section: B, page: 1292.
500 $a Adviser: Kenneth Crawford.
502 $a Thesis (Ph.D.)--The University of Oklahoma, 2003.
520 $a The power utility industry has become highly volatile with a deregulated market on the horizon and with enormous profit and loss swings in the energy trading market. Electricity, in particular, has become a commodity that is bought and sold at market prices, where load forecasting plays a crucial role in the composition of those prices. Public and private utilities must contend with the fact that a small error in an electric load forecast can create a large financial loss for the company. Hence, improving the accuracy of electricity load forecasts has become necessary for the long-term viability of all power utilities.
520 $a Weather has a significant impact on load demand and load forecasting. However, the weather-load relationship is unknown at the substation-level—mostly because substation-level load data have rarely been available to those outside the corporate infrastructure. Equally as important, most utilities have made inconsistent and antiquated use of weather data.
520 $a This study used electric load data from four substations in Oklahoma and concurrent weather observations from co-located Oklahoma Mesonet sites to: (1) determine the interrelationships between weather variables and electric load demand; (2) determine the impact of weather on the consumption of electricity by different customer classes (e.g., residential, commercial, industrial); (3) establish thresholds of temperature associated with changes in the patterns of the use of electricity; and (4) produce load model simulations to quantify the improvements in the accuracy of a load forecast. This study also links a much improved, high-resolution numerical weather prediction model to a neural network load model to quantify the economic value of improved accuracy in load forecasts. In the end, this dissertation determined that a comprehensive understanding of the relationship between weather variables and electricity demand will improve the accuracy of load forecasting. The results of this study can save a small utility in excess of $0 .5 million annually. If the results are applied to the larger power companies around the United States, a decrease in operating costs could exceed millions of dollars.
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