Online distributed learning in wind power forecasting
نویسندگان
چکیده
Forecasting wind power generation up to a few hours ahead is of the utmost importance for efficient operation systems and participation in electricity markets. Recent statistical learning approaches exploit spatiotemporal dependence patterns among neighbouring sites, but their requirement sharing confidential data with third parties may limit use practice. This explains recent interest distributed, privacy preserving algorithms high-dimensional learning, e.g. auto-regressive models. The that have been proposed are based on batch learning. However, these potentially computationally expensive do not allow accommodation nonstationary characteristics stochastic processes like generation. paper closes gap between online distributed optimisation by presenting two novel recursively update model parameters while limiting information exchange farm operators other potential providers. A simulation study compared convergence tracking ability both approaches. In addition, case using large dataset from 311 farms Denmark confirmed generally outperform existing such agents actively share private data.
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ژورنال
عنوان ژورنال: International Journal of Forecasting
سال: 2021
ISSN: ['1872-8200', '0169-2070']
DOI: https://doi.org/10.1016/j.ijforecast.2020.04.004