Abstract
The ANN-based mesoscale-microscale coupling model forecasts wind speed and wind direction with high accuracy for wind parks located in complex terrain onshore, yet some weather regimes remains unresolved and forecast of such events failing. The model’s generalization improved significantly when categorization information added as an input. The improved model is able to resolve extreme events and converged faster with significantly smaller number of hidden neurons. The new model performed equally good on test data sets from both onshore and offshore wind park sites.
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References
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This work is sponsored by Norwegian Research Council, project ENERGIX, 2013–2014.
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Sapronova, A., Meissner, C., Mana, M. (2014). Improving an Accuracy of ANN-Based Mesoscale-Microscale Coupling Model by Data Categorization: With Application to Wind Forecast for Offshore and Complex Terrain Onshore Wind Farms. In: Woon, W., Aung, Z., Madnick, S. (eds) Data Analytics for Renewable Energy Integration. DARE 2014. Lecture Notes in Computer Science(), vol 8817. Springer, Cham. https://doi.org/10.1007/978-3-319-13290-7_5
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DOI: https://doi.org/10.1007/978-3-319-13290-7_5
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