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Short Term Load Forecasting in Electric Power Systems with Artificial Neural Networks

  • G. J. TsekourasEmail author
  • F. D. Kanellos
  • N. Mastorakis
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 343)

Abstract

The demand in electric power should be predicted with the highest possible accuracy as it affects decisively many of power system’s operations. Conventional methods for load forecasting were built on several assumptions, while they had to cope with relations between the data used that could not be described analytically. Artificial Neural Networks (ANNs) gave answers to many of the above problems and they became the predominant load forecasting technique. In this chapter the reader is first introduced to Artificial Neural Networks and their usage in forecasting the load demand of electric power systems. Several of the major training techniques are described with their pros and cons being discussed. Finally, feed- forward ANNs are used for the short-term forecasting of the Greek Power System load demand. Various ANNs with different inputs, outputs, numbers of hidden neurons etc. are examined, techniques for their optimization are proposed and the obtained results are discussed.

Keywords

Artificial neural networks ANN evaluation Load Forecasting Short term load forecasting Training methods 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • G. J. Tsekouras
    • 1
    Email author
  • F. D. Kanellos
    • 2
  • N. Mastorakis
    • 1
  1. 1.Department of Electrical Engineering and Computer ScienceHellenic Naval Academy, Terma HatzikiriakuPIRAEUS, AthensGreece
  2. 2.School of Production Engineering and ManagementTechnical University of CreteChaniaGreece

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