基于误差分类的风电功率区间评估

为了提高风电功率的区间评估精度,结合预测误差数据的特性,提出了一种基于误差分类的区间评估方法。首先,引入K-means聚类算法,以欧氏距离为聚类指标对风电预测误差的整体水平进行分类。然后,建立误差区间评估模型,以风电功率数据和历史预测误差为模型输入,以预测误差区间为输出,利用长短期记忆(LSTM)神经网络深度学习模型输入和输出之间的关联。最后,利用Elia网站风电数据进行验证,结果表明,与其他评估模型和传统的误差概率分布方法相比,所提方法更能抓住误差数据的特性,能够得到更为准确的风电功率区间评估结果。

Interval Estimation of Wind Power Based on Error Classification

In order to improve the interval estimation accuracy of wind power, an interval estimation method based on error classification is proposed combined with the characteristics of prediction error data. Firstly, the K-means clustering algorithm is introduced to classify the overall levels of wind power prediction errors by using the Euclidean distance as the clustering index. Secondly, an error interval estimation model is established, of which the input is the wind power and the historical prediction error, and the output is the prediction error interval. Long short-term memory (LSTM) network is utilized to deeply learn the correlation between the input and output of the model. Finally, the wind power data from Elia website is used for verification. The results show that, compared with other estimation models and traditional error probability distribution methods, the proposed method can capture the characteristics of prediction error data and obtain more accurate interval estimation results of wind power.