基于ARMAX-GARCH模型的微电网风功率预测
收稿日期: 2013-07-29
修回日期: 2013-11-27
网络出版日期: 2013-12-27
基金资助
广东省粤港招标项目（2011BZ100101）；广州市重大科技专项（2010U1-D00231）；国家自然科学基金青年科学基金（51206170）
Wind Power Forecasting Base on ARMAX-GARCH for a Microgrid
Received date: 2013-07-29
Revised date: 2013-11-27
Online published: 2013-12-27
目前风功率预测多为风功率期望的点预测，且以采样间隔较大的功率序列作为建模序列，这样会降低预测模型对风功率时序特征模拟的准确度和可信度。文中基于小采样间隔风功率序列，提出ARMAX-GARCH风功率预测模型。通过构造风功率新息序列，结合小时平均风功率序列，建立ARMAX点预测模型，采用BIC最小信息准则和相关性分析实现模型定阶和外生变量选择；采用GARCH模型模拟残差的波动特性实现区间预测。以海岛微电网实测风功率数据为例，进行提前1 h风功率预测。结果表明，与持续法、ARMA和RBF神经网络相比，该预测模型能显著提高风功率期望的点预测精度并具有较好的区间预测效果。
黄磊 , 舒杰 , 崔琼 , 姜桂秀 . 基于ARMAX-GARCH模型的微电网风功率预测[J]. 新能源进展, 2013 , 1(3) : 224 -229 . DOI: 10.3969/j.issn.2095-560X.2013.03.004
Wind power forecasting models are often built for point forecasting using wind power series with large sampling intervals, which reduces the accuracy and reliability of the forecasting models. Based on wind power series with small sampling interval, this paper proposes an ARMAX-GARCH wind power forecasting model. The ARMAX model is built for point forecasting by combining the constructed innovation series of wind power and the hourly-average wind power series. The model identification and exogenous covariates selection are based on Bayesian information criterion (BIC) and correlation analysis. The GARCH model is used for interval forecasting to simulate the fluctuation characteristic of the residual series. To demonstrate the effectiveness, the model for 1 h ahead forecasting is applied and tested on a microgrid located on an island in the south of China. Comparing with persistent method, ARMA and RBF neural network, simulation results demonstrate that the proposed forecasting model improves the accuracy of point forecasting significantly and has a better interval forecasting result.
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