基于稀疏编码的短期风电功率时间序列预测

针对短期风电功率时间序列,提出一类基于字典的稀疏编码预测方法。为构建预测模型,将历史风电功率时间序列数据组成具有时延的输入-输出数据对,其输入与输出数据向量以原子形式分别构成两个字典,无需模型的训练阶段。针对待预测的时延输入数据向量,使用1l范数或弹性网络正则化的稀疏分解凸优化算法计算稀疏编码的权值,进一步借助历史输出数据所构成的字典,以得到相应的预测输出。与此同时,还分析了将测试数据实时加入字典,并维持字典容量不变的三种自适应更新策略,以进一步提升模型的预测精度。为了验证该方法的有效性,将不同的稀疏编码方法首先应用于Santa Fe混沌时间序列预测中,其次,将其分别应用于短期风电功率间接预测中,在同等条件下,与SVM方法进行了比较。结果表明,不同的稀疏编码方法均取得了很好的预测效果,其中基于弹性网络正则化的稀疏编码方法具有较高的预测精度,显示出其有效性。

Short-term wind power time series prediction based on sparse coding method

Aiming at short-term wind power time series, a class of dictionary-based sparse coding prediction method is proposed. The historical wind power time series data are composed to the time-lagged input-output data pairs to build the prediction model, while the two dictionaries are respectively constructed by regarding the all input and output data vectors as atoms, without the training phase of the model. For the time-lagged test input data vectors need predicting, convex optimization algorithms with norm or elastic net regularization using sparse decomposition technology are applied to calculate the weights by sparse coding. Furthermore, by the dictionary which is constructed using the historical output data, the corresponding predicted outputs are obtained. Simultaneously, adaptive dictionary updating strategies are given, where test data is successively added to the dictionary in real time while maintaining the dictionary size by using three algorithms, so as to further improve the prediction accuracy of the model. In order to verify the effectiveness of this method, different sparse coding methods are firstly applied to the prediction of Santa Fe chaotic time series, and then applied to the indirect prediction for short-term wind power respectively. Under the same condition, compared with the SVM method, experimental results show that different sparse coding methods have achieved good prediction results, and the sparse coding methods using elastic net regularization have higher prediction precision and show their effectiveness. This work is supported by National Natural Science Foundation of China (No. 51467008).