Electricity demand uncertainty modeling using enhanced path-based scenario generation method

Mehrdad Tahmasebi, Pasupuleti Jagadeesh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

One of the most important realities and uncertainties in the deregulated electricity market is electricity demand. Electricity demand scenario generation in day-ahead markets using newly proposed Enhanced path-based scenario generation method based on autoregressive moving average is developed in this paper. A new enhanced path-based scenario generation method to generate scenarios of the random variable and uncertainties modeling to achieve lower mean absolute percentage error for scenario generation compared to path-based autoregressive moving average method is proposed. Comparison of expected values obtained from the proposed method and path-based ARMA method, as well as real values, shows lower mean absolute percentage error for proposed method. It is observed that the mean absolute percentage error is decreased 5% for electricity demand using newly proposed scenario generation method. Lower mean absolute percentage error indicates higher accuracy of this method for generation of scenarios.

Original languageEnglish
Title of host publication2017 6th International Youth Conference on Energy, IYCE 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509064090
DOIs
Publication statusPublished - 07 Aug 2017
Event6th International Youth Conference on Energy, IYCE 2017 - Budapest, Hungary
Duration: 21 Jun 201724 Jun 2017

Publication series

Name2017 6th International Youth Conference on Energy, IYCE 2017

Other

Other6th International Youth Conference on Energy, IYCE 2017
CountryHungary
CityBudapest
Period21/06/1724/06/17

Fingerprint

Electricity
Random variables
Uncertainty

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Nuclear Energy and Engineering
  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Fuel Technology

Cite this

Tahmasebi, M., & Jagadeesh, P. (2017). Electricity demand uncertainty modeling using enhanced path-based scenario generation method. In 2017 6th International Youth Conference on Energy, IYCE 2017 [8003747] (2017 6th International Youth Conference on Energy, IYCE 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IYCE.2017.8003747
Tahmasebi, Mehrdad ; Jagadeesh, Pasupuleti. / Electricity demand uncertainty modeling using enhanced path-based scenario generation method. 2017 6th International Youth Conference on Energy, IYCE 2017. Institute of Electrical and Electronics Engineers Inc., 2017. (2017 6th International Youth Conference on Energy, IYCE 2017).
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abstract = "One of the most important realities and uncertainties in the deregulated electricity market is electricity demand. Electricity demand scenario generation in day-ahead markets using newly proposed Enhanced path-based scenario generation method based on autoregressive moving average is developed in this paper. A new enhanced path-based scenario generation method to generate scenarios of the random variable and uncertainties modeling to achieve lower mean absolute percentage error for scenario generation compared to path-based autoregressive moving average method is proposed. Comparison of expected values obtained from the proposed method and path-based ARMA method, as well as real values, shows lower mean absolute percentage error for proposed method. It is observed that the mean absolute percentage error is decreased 5{\%} for electricity demand using newly proposed scenario generation method. Lower mean absolute percentage error indicates higher accuracy of this method for generation of scenarios.",
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Tahmasebi, M & Jagadeesh, P 2017, Electricity demand uncertainty modeling using enhanced path-based scenario generation method. in 2017 6th International Youth Conference on Energy, IYCE 2017., 8003747, 2017 6th International Youth Conference on Energy, IYCE 2017, Institute of Electrical and Electronics Engineers Inc., 6th International Youth Conference on Energy, IYCE 2017, Budapest, Hungary, 21/06/17. https://doi.org/10.1109/IYCE.2017.8003747

Electricity demand uncertainty modeling using enhanced path-based scenario generation method. / Tahmasebi, Mehrdad; Jagadeesh, Pasupuleti.

2017 6th International Youth Conference on Energy, IYCE 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 8003747 (2017 6th International Youth Conference on Energy, IYCE 2017).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - One of the most important realities and uncertainties in the deregulated electricity market is electricity demand. Electricity demand scenario generation in day-ahead markets using newly proposed Enhanced path-based scenario generation method based on autoregressive moving average is developed in this paper. A new enhanced path-based scenario generation method to generate scenarios of the random variable and uncertainties modeling to achieve lower mean absolute percentage error for scenario generation compared to path-based autoregressive moving average method is proposed. Comparison of expected values obtained from the proposed method and path-based ARMA method, as well as real values, shows lower mean absolute percentage error for proposed method. It is observed that the mean absolute percentage error is decreased 5% for electricity demand using newly proposed scenario generation method. Lower mean absolute percentage error indicates higher accuracy of this method for generation of scenarios.

AB - One of the most important realities and uncertainties in the deregulated electricity market is electricity demand. Electricity demand scenario generation in day-ahead markets using newly proposed Enhanced path-based scenario generation method based on autoregressive moving average is developed in this paper. A new enhanced path-based scenario generation method to generate scenarios of the random variable and uncertainties modeling to achieve lower mean absolute percentage error for scenario generation compared to path-based autoregressive moving average method is proposed. Comparison of expected values obtained from the proposed method and path-based ARMA method, as well as real values, shows lower mean absolute percentage error for proposed method. It is observed that the mean absolute percentage error is decreased 5% for electricity demand using newly proposed scenario generation method. Lower mean absolute percentage error indicates higher accuracy of this method for generation of scenarios.

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Tahmasebi M, Jagadeesh P. Electricity demand uncertainty modeling using enhanced path-based scenario generation method. In 2017 6th International Youth Conference on Energy, IYCE 2017. Institute of Electrical and Electronics Engineers Inc. 2017. 8003747. (2017 6th International Youth Conference on Energy, IYCE 2017). https://doi.org/10.1109/IYCE.2017.8003747