Influence of kNN-Based Load Forecasting Errors on Optimal Energy Production

  • Alicia Troncoso Lora
  • José C. Riquelme
  • José Luís Martínez Ramos
  • Jesús M. Riquelme Santos
  • Antonio Gómez Expósito
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2902)

Abstract

This paper presents a study of the influence of the accuracy of hourly load forecasting on the energy planning and operation of electric generation utilities. First, a k Nearest Neighbours (kNN) classification technique is proposed for hourly load forecasting. Then, obtained prediction errors are compared with those obtained results by using a M5’. Second, the obtained kNN-based load forecast is used to compute the optimal on/off status and generation scheduling of the units. Finally, the influence of forecasting errors on both the status and generation level of the units over the scheduling period is studied.

Keywords

Nearest neighbours load forecasting optimal energy production 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Alicia Troncoso Lora
    • 1
  • José C. Riquelme
    • 1
  • José Luís Martínez Ramos
    • 2
  • Jesús M. Riquelme Santos
    • 2
  • Antonio Gómez Expósito
    • 2
  1. 1.Department of Languages and SystemsUniversity of SevillaSevillaSpain
  2. 2.Department of Electrical EngineeringUniversity of SevillaSevillaSpain

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