HYBRID NEURAL NETWORK SYSTEM FOR ELECTRIC LOAD FORECASTING OF TELECOMUNICATION STATION

• Abstract:

  This paper describes a neural network system for power electric load forecasting of telecommunication station. Getting an accuracy useful for contractual purpose a separately daily forecast of both main load and its oscillation is proposed.
  For the mean daily forecast we used a three layers multi-layer perceptron (MLP), while to the oscillation forecasting we realized a system composed by a MLP and a self organizing map (SOM): the typology information obtained by the SOM unsupervised algorithm has been utilized as binary code in MLP input.
  The proposed system with hourly power load data of a big telecommunication operator has been tested.
  The total forecast has been obtained combining the two components. The forecasting accuracy for a whole year test data is around 2%. Some problem exists in the forecasted load of summer time.
• Keywords:

  short term load forecasting, SOM, MLP
• Download:

  IMEKO-WC-2009-TC4-056.pdf
• DOI:

  _unreg_wc-2009.056

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