Crete2002 - LOAD POCKET MODELING Eugene A. Feinberg, Dora...

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LOAD POCKET MODELING Eugene A. Feinberg, Dora Genethliou Department of Applied Mathematics and Statistics State University of New York at Stony Brook Stony Brook, NY 11794-3600, USA Janos T. Hajagos KeySpan Energy 175 East Old Country Road Hicksville, NY 11801, USA ABSTRACT Demand for electric power typically depends on the temperature and several other weather factors. It also depends on the day of the week and the hour of the day. This paper models electric power demand for close geographic areas that are called load pockets. We have developed a statistical model for load pockets. This model describes the load during the summer period. In this model we took into account the day of the week, the hour of the day, as well as weather data, which include the ambient temperature, humidity, wind speed, and sky cover. The proposed method was evaluated on real data for several load pockets in the Northeastern part of the USA. KEY WORDS Load Pocket Modeling, Load Forecasting 1. Introduction Electric power load forecasting is important for electric utilities. Load forecasting helps an electric utility in making important decisions including decisions on purchasing and generating electric power, load switching, area planning and development. Load forecasts can be divided into three categories: short term forecasts which are usually from one hour to a week, medium forecasts which are usually from a month to a year or even up to three years, and long term forecasts which are over three years [8]. Usually load forecasting methods are based on statistical, mathematical, econometric, and other load models. In this paper we describe a statistical model for a load pocket. This model takes into account weather parameters, a day of the week, and an hour during the day. Four different weather parameters have been considered: the temperature, humidity, sky cover, and wind speed. We developed an algorithm to compute the model parameters and tested the importance of particular weather characteristics. The algorithm finds model parameters by performing a sequence of linear regressions. The model includes weather parameters as well as time parameters that consist of a day of the week and an hour during the day. The advantages of this model are its accuracy, simplicity, and the use of only two types of data: weather and load. In the literature there are several papers discussing load modeling by using various techniques . Linear regression models have been widely used [1,2,4,5]. In [5] end-use data were collected and were used to compare three types of models for allocating estimates of annual residential central air conditioning energy use to hours of the year. In [4] different regression models are presented for peak modeling. They conclude that the best model to fit their data is a model that has current weather, past average load, past peak load, and holiday and weekend as dummy variables. A method for forecasting the heat sensitive portion of electrical demand and energy utilizing a summer weather load model and taking variation of weather factors is introduced in [1].
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Crete2002 - LOAD POCKET MODELING Eugene A. Feinberg, Dora...

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