Abstract
Given the imminent threats of climate change, it is urgent to boost the use of clean energy, being wind energy a potential candidate. Nowadays, deployment of wind turbines has become extremely important and long-term estimation of the produced power entails a challenge to achieve good prediction accuracy for site assessment, economic feasibility analysis, farm dispatch, and system operation. We present a method for long-term wind power forecasting using wind turbine properties, statistics, and genetic programming. First, due to the high degree of intermittency of wind speed, we characterize it with Weibull probability distributions and consider wind speed data of time intervals corresponding to prediction horizons of 30, 25, 20, 15 and 10 days ahead. Second, we perform the prediction of a wind speed distribution with genetic programming using the parameters of the Weibull distribution and other relevant meteorological variables. Third, the estimation of wind power is obtained by integrating the forecasted wind velocity distribution into the wind turbine power curve. To demonstrate the feasibility of the proposed method, we present a case study for a location in Mexico with low wind speeds. Estimation results are promising when compared against real data, as shown by MAE and MAPE forecasting metrics.
Highlights
To achieve the maximum use of wind energy, the prediction of the wind resource is mandatory since it is one of the main ingredients to estimate the generated wind power by a wind turbine generator or a wind power plant
The long-term power estimation methodology presented in the previous sections is general enough to be applied to any site, with any arbitrary wind distribution and with wind turbines of any size that can be driven with the available wind resource
As shown by the the results in this work, the proposed forecasting methodology provides better results as the prediction results in this work, the proposed forecasting methodology provides better results as the prediction horizon becomes larger. This trend relays on the fact that, even though the prediction of the scale horizon becomes larger. This trend relays on the fact that, even though the prediction of the scale parameter could be better for smaller horizons, the fitting of the wind speed distribution to a Weibull parameter could be better for smaller horizons, the fitting of the wind speed distribution to a Weibull distribution is better as more data is provided, or equivalently as a larger interval of time is considered
Summary
To achieve the maximum use of wind energy, the prediction of the wind resource is mandatory since it is one of the main ingredients to estimate the generated wind power by a wind turbine generator or a wind power plant. Time scales for wind forecasting can be divided into four categories according to the literature [1]: . Very short-term forecast: From a few minutes to one hour ahead. Short-term forecast: From one hour to several hours ahead. Medium-term forecast: From several hours to one week ahead. Long-term forecast: From one week to one year or more ahead. The usefulness of the prediction depends on the prediction horizon. Very short forecasting is important for the electricity market clearing and real-time grid operation.
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