A Nonparametric Bayesian Framework for Short-Term Wind Power Probabilistic Forecast

Xie, Wei; Zhang, Pu; Chen, Rong; Zhou, Zhi

doi:10.1109/TPWRS.2018.2858265

A Nonparametric Bayesian Framework for Short-Term Wind Power Probabilistic Forecast

Journal Article · Mon Jul 23 00:00:00 EDT 2018 · IEEE Transactions on Power Systems

DOI:https://doi.org/10.1109/TPWRS.2018.2858265· OSTI ID:1493433

^[1]; Zhang, Pu ^[2]; Chen, Rong ^[3]; ^[4]

Northeastern Univ., Boston, MA (United States)
Rensselaer Polytechnic Inst., Troy, NY (United States)
Rutgers Univ., Piscataway, NJ (United States)
Argonne National Lab. (ANL), Argonne, IL (United States)

To improve the energy system resilience and economic efficiency, the wind power as a renewable energy starts to be deeply integrated into smart power grids. However, the wind power forecast uncertainty brings operational challenges. In order to provide a reliable guidance on operational decisions, in this paper, we propose a short-term wind power probabilistic forecast. Specifically, to model the rich dynamic behaviors of underlying physical wind power stochastic process occurring in various meteorological conditions, we first introduce an infinite Markov switching autoregressive model. This nonparametric time series model can capture the important properties in the real world data to improve the prediction accuracy. Then, given finite historical data, the posterior distribution of flexible forecast model can correctly quantify the model estimation uncertainty. Built on it, we develop the posterior predictive distribution to rigorously quantify the overall forecasting uncertainty accounting for both inherent stochastic uncertainty and model estimation error. Furthermore, the proposed approach can provide accurate and reliable short-term wind power probabilistic forecast, which can be used to support smart power grids real-time risk management.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Lab. (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Wind and Hydropower Technologies; National Science Foundation (NSF)

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1493433

Journal Information:: IEEE Transactions on Power Systems, Journal Name: IEEE Transactions on Power Systems Journal Issue: 1 Vol. 34; ISSN 0885-8950

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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