Modeling electricity loads in California: ARMA models with hyperbolic noise
AbstractIn 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.
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Bibliographic InfoPaper provided by Hugo Steinhaus Center, Wroclaw University of Technology in its series HSC Research Reports with number HSC/02/02.
Length: 21 pages
Date of creation: 2002
Date of revision:
Publication status: Published in Signal Processing 82 (2002) 1903-1915.
Electricity load; ARMA model; Heavy tails; Hyperbolic distribution; Forecast;
Find related papers by JEL classification:
- C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
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