Forecasting electricity demand using generalized long memory

@inproceedings{Soares2006ForecastingED,
  title={Forecasting electricity demand using generalized long memory},
  author={Lacir J. Soares and Leonardo Rocha Souza},
  year={2006}
}
This paper studies the electricity hourly load demand in the area covered by a utility situated in the southeast of Brazil. We propose a stochastic model which employs generalized long memory (by means of Gegenbauer processes) to model the seasonal behavior of the load. The model is proposed for sectional data, that is, each hour’s load is studied separately as a single series. This approach avoids modeling the intricate intra-day pattern (load profile) displayed by the load, which varies… CONTINUE READING
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Neural networks for short-term load forecasting: a review and evaluation

  • H. S. Hippert, C. E. Pedreira, Souza R.C
  • IEEE Transactions on Power Systems
  • 2001

Estimation and applications of Gegenbauer processes

  • L. Ferrara, D. Guégan
  • 1999
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