Improving the performance of the truncated fourier series least squares (TFSLS)power system load model using an artificial neural network paradigm

  • Authors:

  • Shonique L. Miller;Gary L. Lebby;Ali R. Osareh

  • Affiliations:

  • Green Renewable Energy and Advanced Technology Transfer, North Carolina Agricultural and Technical State University, Greensboro, North Carolina;Green Renewable Energy and Advanced Technology Transfer, North Carolina Agricultural and Technical State University, Greensboro, North Carolina;Green Renewable Energy and Advanced Technology Transfer, North Carolina Agricultural and Technical State University, Greensboro, North Carolina

  • Venue:
  • IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
  • Year:
  • 2010

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Abstract

Power System Load models have a wide range of application in the electric power industry including applications involving: (i) load management policy monitoring; (ii) assisting with the generator commitment problem; (iii) providing short term forecasts; (iv) aiding with system planning by providing long term forecasts. A method that has been utilized in the power systems planning community involves modeling the power system load (PSL) utilizing a truncated Fourier series. Presented herein is an innovative method based upon analyzing nine weeks of data and generating an optimum number of Fourier series terms included in model structure from a set of preselected heuristic basis functions for prediction. The resulting PSL model capable of providing high quality middle-long term forecasts and retain the shape prediction of the load curve out in time.