A Nonparametric Bayesian Framework for Short-Term Wind Power Probabilistic Forecast
A Nonparametric Bayesian Framework for Short-Term Wind Power Probabilistic Forecast
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DOI：
10.1109/tpwrs.2018.2858265
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发表时间：
2019-01
影响因子：
6.6
通讯作者：
Wei Xie;Pu Zhang;Rong Chen;Zhi Zhou
中科院分区：
文献类型：
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作者：
Wei Xie;Pu Zhang;Rong Chen;Zhi Zhou
为了提高能源系统的弹性和经济效率，风电作为一种可再生能源开始深度融入智能电网。然而，风电预测的不确定性带来了运营挑战。为了给运营决策提供可靠的指导，本文提出了短期风电功率概率预测方法。具体而言，为了模拟各种气象条件下发生的潜在物理风电功率随机过程的丰富动态行为，我们首先引入了一个无限马尔可夫切换自回归模型。这种非参数时间序列模型可以捕捉到真实世界数据中的重要属性，以提高预测精度。然后，在有限历史数据下，柔性预测模型的后验分布能够正确量化模型估计的不确定性。在此基础上，我们开发了后验预测分布，以严格量化整体预测不确定性，同时考虑固有的随机不确定性和模型估计误差。因此，该方法可以提供准确可靠的短期风电功率概率预测，可用于支持智能电网的实时风险管理。
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. Therefore, 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.