METHOD FOR THE FORECASTING OF THE PROBABILITY DENSITY FUNCTION OF POWER SYSTEM LOADS.

G. Heydt, A. Khotanzad, N. Farahbakhshian

Research output: Contribution to conferencePaper

Abstract

Conventional load forecasting involves the prediction of the mean value of the demand of an electric power system. The mean value of a quantity which is subject to uncertainty does not fully characterize that quantity. In this paper, two well known load forecasting methods are generalized to predict the entire probability density function of the load. Note that the proposed technique is not to calculate the probability density of the forecasted load, but, rather, the probability density function of the load itself. From this density function, a wide variety of quantities may be calculated: the mean value; the probability that the load will exceed some threshold; a figure of confidence of the forecast mean; conditional probabilities (under special conditions such as negative generation margin), and conditional expectations. Both methods presented rely on the forecasting of the statistical moments of the demand, and using those moments to calculate the probability density function using the Gram-Charlier series type A. An example using typical data is given.

Original languageEnglish (US)
StatePublished - Jan 1 2017
EventIEEE Power Eng Soc Summer Meet, Conf Pap - Portland, OR, USA
Duration: Jul 26 1981Jul 31 1981

Other

OtherIEEE Power Eng Soc Summer Meet, Conf Pap
CityPortland, OR, USA
Period7/26/817/31/81

Fingerprint

Probability density function
Electric power systems

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Heydt, G., Khotanzad, A., & Farahbakhshian, N. (2017). METHOD FOR THE FORECASTING OF THE PROBABILITY DENSITY FUNCTION OF POWER SYSTEM LOADS.. Paper presented at IEEE Power Eng Soc Summer Meet, Conf Pap, Portland, OR, USA, .

METHOD FOR THE FORECASTING OF THE PROBABILITY DENSITY FUNCTION OF POWER SYSTEM LOADS. / Heydt, G.; Khotanzad, A.; Farahbakhshian, N.

2017. Paper presented at IEEE Power Eng Soc Summer Meet, Conf Pap, Portland, OR, USA, .

Research output: Contribution to conferencePaper

Heydt, G, Khotanzad, A & Farahbakhshian, N 2017, 'METHOD FOR THE FORECASTING OF THE PROBABILITY DENSITY FUNCTION OF POWER SYSTEM LOADS.' Paper presented at IEEE Power Eng Soc Summer Meet, Conf Pap, Portland, OR, USA, 7/26/81 - 7/31/81, .
Heydt G, Khotanzad A, Farahbakhshian N. METHOD FOR THE FORECASTING OF THE PROBABILITY DENSITY FUNCTION OF POWER SYSTEM LOADS.. 2017. Paper presented at IEEE Power Eng Soc Summer Meet, Conf Pap, Portland, OR, USA, .
Heydt, G. ; Khotanzad, A. ; Farahbakhshian, N. / METHOD FOR THE FORECASTING OF THE PROBABILITY DENSITY FUNCTION OF POWER SYSTEM LOADS. Paper presented at IEEE Power Eng Soc Summer Meet, Conf Pap, Portland, OR, USA, .
@conference{a2d7c2bdbfce47b3962c397f809475ae,
title = "METHOD FOR THE FORECASTING OF THE PROBABILITY DENSITY FUNCTION OF POWER SYSTEM LOADS.",
abstract = "Conventional load forecasting involves the prediction of the mean value of the demand of an electric power system. The mean value of a quantity which is subject to uncertainty does not fully characterize that quantity. In this paper, two well known load forecasting methods are generalized to predict the entire probability density function of the load. Note that the proposed technique is not to calculate the probability density of the forecasted load, but, rather, the probability density function of the load itself. From this density function, a wide variety of quantities may be calculated: the mean value; the probability that the load will exceed some threshold; a figure of confidence of the forecast mean; conditional probabilities (under special conditions such as negative generation margin), and conditional expectations. Both methods presented rely on the forecasting of the statistical moments of the demand, and using those moments to calculate the probability density function using the Gram-Charlier series type A. An example using typical data is given.",
author = "G. Heydt and A. Khotanzad and N. Farahbakhshian",
year = "2017",
month = "1",
day = "1",
language = "English (US)",
note = "IEEE Power Eng Soc Summer Meet, Conf Pap ; Conference date: 26-07-1981 Through 31-07-1981",

}

TY - CONF

T1 - METHOD FOR THE FORECASTING OF THE PROBABILITY DENSITY FUNCTION OF POWER SYSTEM LOADS.

AU - Heydt, G.

AU - Khotanzad, A.

AU - Farahbakhshian, N.

PY - 2017/1/1

Y1 - 2017/1/1

N2 - Conventional load forecasting involves the prediction of the mean value of the demand of an electric power system. The mean value of a quantity which is subject to uncertainty does not fully characterize that quantity. In this paper, two well known load forecasting methods are generalized to predict the entire probability density function of the load. Note that the proposed technique is not to calculate the probability density of the forecasted load, but, rather, the probability density function of the load itself. From this density function, a wide variety of quantities may be calculated: the mean value; the probability that the load will exceed some threshold; a figure of confidence of the forecast mean; conditional probabilities (under special conditions such as negative generation margin), and conditional expectations. Both methods presented rely on the forecasting of the statistical moments of the demand, and using those moments to calculate the probability density function using the Gram-Charlier series type A. An example using typical data is given.

AB - Conventional load forecasting involves the prediction of the mean value of the demand of an electric power system. The mean value of a quantity which is subject to uncertainty does not fully characterize that quantity. In this paper, two well known load forecasting methods are generalized to predict the entire probability density function of the load. Note that the proposed technique is not to calculate the probability density of the forecasted load, but, rather, the probability density function of the load itself. From this density function, a wide variety of quantities may be calculated: the mean value; the probability that the load will exceed some threshold; a figure of confidence of the forecast mean; conditional probabilities (under special conditions such as negative generation margin), and conditional expectations. Both methods presented rely on the forecasting of the statistical moments of the demand, and using those moments to calculate the probability density function using the Gram-Charlier series type A. An example using typical data is given.

UR - http://www.scopus.com/inward/record.url?scp=85040268200&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85040268200&partnerID=8YFLogxK

M3 - Paper

ER -