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PalmSprings2002 - STATISTICAL LOAD MODELING Eugene A...

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STATISTICAL LOAD 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 Long Island Power Authority (LIPA) 175 East Old Country Road Hicksville, NY 11801, USA ABSTRACT This paper discusses the improvement of the statistical model that was developed in [6] by adding a new weather variable called sunshine. We also took into account other weather factors such as ambient temperature, humidity, wind speed, and sky cover as well as time factors such as the day of the week and the hour of the day. We developed a statistical model that describes the electric power demand during a summer period for close geographic areas that are called load pockets. 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 Accurate models for electric power load forecasting are essential to the operation and planning of a utility company. Load forecasting helps an electric utility in making important decisions including decisions on purchasing and generating electric power, load switching, area planning and development. Demand for electric power typically depends on the temperature and several other weather factors as well as the day of the week and the hour of the day. These factors are included in this model. 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 [9]. Usually load forecasting methods are based on statistical, mathematical, econometric, and other load models. In this paper we describe an improved statistical model for a load pocket compared to our model that was developed in [6]. This model takes into account weather parameters, a day of the week, and an hour during the day. In [6] four different weather parameters have been considered: the temperature, humidity, sky cover, and wind speed. In this paper we include another variable called sunshine. This new variable indicates when the sun is up. 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 parameters were estimated using two different approaches. 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.
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