LOAD POCKET MODELING
Eugene A. Feinberg,
Department of Applied Mathematics and Statistics
State University of New York at Stony Brook
Stony Brook, NY 11794-3600, USA
Janos T. Hajagos
175 East Old Country Road
Hicksville, NY 11801, USA
Demand for electric power typically depends on the
temperature and several other weather factors.
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.
developed a statistical model for load pockets.
model describes the load during the summer period.
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
Load Pocket Modeling, Load Forecasting
Electric power load forecasting is important for electric
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
Usually load forecasting methods are based on
statistical, mathematical, econometric, and other load
In this paper we describe a statistical model for a load
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
The algorithm finds model parameters by
performing a sequence of linear regressions.
includes weather parameters as well as time parameters
that consist of a day of the week and an hour during the
The advantages of this model are its accuracy,
simplicity, and the use of only two types of data: weather
In the literature there are several papers discussing load
modeling by using various techniques
models have been widely used [1,2,4,5].
In  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.
 different regression models are presented for peak
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
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 .