Modeling electricity loads in California: ARMA models with hyperbolic noise
In this paper we address the issue of modeling and forecasting electricity loads. We apply a two-step procedure to a series of system-wide loads from the California power market. First, we remove the weekly and annual seasonalities. Then, after analyzing properties of the deseasonalized data we fit an autoregressive moving average model. The obtained residuals seem to be independent but with tails heavier than Gaussian. It turns out that the hyperbolic distribution provides an excellent fit. As a justification for our approach we supply out-of-sample forecasts. As it turns out, our method performs significantly better than the one used by the California System Operator.
|Date of creation:||2002|
|Date of revision:|
|Publication status:||Published in Signal Processing 82 (2002) 1903-1915.|
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"Energy price risk management,"
HSC Research Reports
HSC/00/02, Hugo Steinhaus Center, Wroclaw University of Technology.
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