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Wind speed forecasting by subspace and nuclear norm optimization based algorithms
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DOI：10.1016/j.seta.2019.07.003.png)
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Abstract
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In this paper, we study properties of two wind speed forecasting schemes from mid-to-short term wind velocity measurements. The measurements are assumed to be collected over time intervals shorter than the ones previously studied by the authors. Two application examples illustrate the properties of these schemes. In the first example, historical data originating from five meteorological stations are considered. This example demonstrates that the first scheme outperforms persistence and artificial neural network (ANN) predictors by a large margin for all step sizes considered. This result enlarges domain of application of the first forecasting scheme from multistep-ahead only to one and multi-step-ahead and from long-term observations only to long and mid-term observations. A local wildly fluctuating dense data set obtained from a renewable energy research home unit is studied in the second example to check the performance of the first scheme under non-standard operating conditions. The second scheme is a compressive subspace algorithm developed recently for innovation models. The second scheme complements the first scheme in that it uses short-term observations and outperforms the multi-step-ahead persistence and the ANN predictors.
Keywords:
Wind speed forecasting
Subspace method
Nuclear norm
Regularization
Sparsity
Artificial neural network
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