基于多重联合概率与改进加权HMM的风电功率预测方法

doi:10.19912/j.0254-0096.tynxb.2022-1055

摘要/Abstract

摘要： 为解决传统风电功率预测中气象因素与功率数据结合应用时相互约束性不足且风电功率序列的历史时序信息随时间逐步衰减的问题,提出一种基于多重联合概率与改进加权隐马尔可夫模型（HMM）的风电功率预测方法。首先,提出多重联合概率方法将数值天气预报（NWP）中的气象因素进行逐步联合,并改进HMM中的释放概率,使NWP气象数据与功率时间序列融合以互相约束;然后,采用条件熵改进粗糙集以计算多步预测的合理属性权重,加权运算多步预测值,以获得风电功率预测值;最后,经过风电场实际算例验证,通过将NWP数据与功率数据相互融合、互相约束,可有效地提高风电功率预测精度。

关键词: 风电功率, 隐马尔可夫模型, 粗糙集理论, 条件熵, 多重联合概率

Abstract: Solving the poor constraint of combining meteorological factors data with power data and the decay of historical time series information of wind power in traditional wind power prediction, a wind power prediction method based on multiple joint probabilities and improved weighted Hidden Markov Model （HMM） is proposed in this paper. Meteorological factors in Numerical Weather Prediction （NWP） are combined by multiple joint probability firstly. Subsequently, the NWP data and the power time series are fused by improving the release probabilities in the HMM to constrain each other. Then, the multi-step predicted values are weighted by the conditional entropy improved rough set with improved conditional entropy, and obtain the final wind power prediction results. Finally, the wind power prediction accuracy can be effectively improved by fusing and confining NWP data and power data with each other, as verified by actual arithmetic cases in wind farms.

Key words: wind power, hidden Markov models, rough set theory, condition entropy, multiple joint probability

中图分类号:

TM615

师洪涛, 李艺萱, 丁茂生, 高峰, 李希彬, 何竹. 基于多重联合概率与改进加权HMM的风电功率预测方法[J]. 太阳能学报, 2023, 44(11): 247-254.

Shi Hongtao, Li Yixuan, Ding Maosheng, Gao Feng, Li Xibin, He Zhu. WIND POWER PREDICTION METHOD BASED ON MULTIPLE JOINT PROBABILITY AND IMPROVED WEIGHTED HMM[J]. Acta Energiae Solaris Sinica, 2023, 44(11): 247-254.

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