Price forecast is a key issue in competitive electricity markets. It provides useful information for the market players and the regulators, in both short and long run. Different approaches have been proposed and implemented. A new dynamic approach for forecasting the market price of electricity in the short term is proposed. The price dates are first clustered according to different types of daily profiles and then, given a proper function representing the trend in price, the set of unknown parameters are identified based on the zeroing of a Lyapunov function. The forecast can be dynamically updated with the latest data available. Higher weight can be attributed to this data in determining the future prices. The proposed approach is validated with reference to real systems in the form of the Italian, New England and New York electricity markets. In addition, an extensive price forecast is provided for the Italian market, an example of a young market that is rather difficult to predict patterns for.
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Bompard et al. (2007) studied this question.
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