Filters: Contacts: Bessa, R. J. (X)
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This paper reports a study on the importance of the training criteria for wind power forecasting and calls into question the generally assumed neutrality of the ‘goodness’ of particular forecasts. The study, focused on the Spanish Electricity Market as a representative example, combines different training criteria and different users of the forecasts to compare them in terms of the benefits obtained. In addition to more classical criteria, an information theoretic learning training criterion, called parametric correntropy, is introduced as a means to correct problems detected in other criteria and achieve more satisfactory compromises among conflicting criteria, namely forecasting value and quality. We show that...
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Tags: bad forecasts,
correntropy,
electricity markets,
good forecasts,
neural networks,
Wind power forecasting is becoming an important tool in electricity markets as the amount of wind power rapidly increases. However, the use of wind power forecasting in market operations and among market participants is still at an early stage. We discuss the current use of wind power forecasting in U.S. ISO/RTO markets, and give recommendations for how to make efficient use of the information in state-of-the-art wind power forecasts.
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