Abstract
Wind energy and wind power forecast errors have a direct impact on operational decision problems involved in the integration of this form of energy into the electricity system. As the relationship between wind and the generated power is highly nonlinear and time-varying, and given the increasing number of available forecasting techniques, it is possible to use alternative models to obtain more than one prediction for the same hour and forecast horizon. To increase forecast accuracy, it is possible to combine the different predictions to obtain a better one or to dynamically select the best one in each time period. Hybrid alternatives based on combining a few selected forecasts can be considered when the number of models is large. One of the most popular ways to combine forecasts is to estimate the coefficients of each prediction model based on its past forecast errors. As an alternative, we propose using multivariate reduction techniques and Markov chain models to combine forecasts. The combination is thus not directly based on the forecast errors. We show that the proposed combination strategies based on dimension reduction techniques provide competitive forecasting results in terms of the Mean Square Error.
Highlights
Wind power, measured by its installed capacity, has soared in recent years driven by technological development
We compare our results according to the following criteria: bias, root mean squared error (RMSE), mean absolute error (MAE) and quantile score (QP) for some quantiles
Large wind power forecast errors have a large impact on the system operation and could cause, at certain moments, concerns about the security of supply
Summary
Wind power, measured by its installed capacity, has soared in recent years driven by technological development. This trend will continue as many countries have ambitious objectives in wind power. As wind power is intermittent by nature and is a non-dispatchable form of energy, wind power forecasts are needed for large-scale integration of wind turbines into the electrical grid. There have been several attempts to produce prediction tools to forecast wind energy. For recent reviews of the literature about wind power forecasting see, for instance, [1,2,3,4]. For a general overview of energy forecasting (load, electricity prices, wind and solar energy), see Hong et al [5]
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