In this paper we study two statistical approaches to load forecasting. Both of them model electricity load as a sum of two components – a deterministic (representing seasonalities) and a stochastic (representing noise). They differ in the choice of the seasonality reduction method. Model A utilizes differencing, while Model B uses a recently developed seasonal volatility technique. In both models the stochastic component is described by an ARMA time series. Models are tested on a time series of system-wide loads from the California power market and compared with the official forecast of the California System Operator (CAISO).
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Paper provided by EconWPA in its series Econometrics with number
0502004.
Length: 8 pages Date of creation: 07 Feb 2005 Date of revision: Handle: RePEc:wpa:wuwpem:0502004
Note: Type of Document - pdf; pages: 8. ”The European Electricity Market EEM-04”, Proceedings Volume, pp. 135-142 Contact details of provider: Web page: http://129.3.20.41
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Find related papers by JEL classification: C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
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