基于非负矩阵分解和相关向量机的短期负荷预测

The Short-Term Load Forecasting of Power System with Nonnegative Matrix Factorization and Relevant Vector Machine

为了能够进一步提高电力系统负荷预测的准确性,提出了一种基于非负矩阵分解(nonnegative matrix factorization,NMF)和相关向量机(relevant vector machine,RVM)的短期负荷预测方法。此方法通过利用NMF对输入样本进行分解,得到低维的非负映射矩阵,将该低维矩阵输入到相关向量机进行训练预测。仿真结果表明,预测效果有明显的改善,而所提出的NMF-RVM模型较之单一的RVM模型具有更高的预测精度。

In order to further improve the accuracy of power system load forecasting, a short-term load forecasting method based on nonnegative matrix factorization（NMF） and relevant vector machine（RVM） is proposed. By using the nonnegative matrix factorization （NMF） algorithm, nonnegative lower-dimensional mapping matrix is obtained, and then the nonnegative lower-dimension mapping matrix derived is taken as the input of RVM for training and prediction. The simulation results show that the predictive validity based on decomposition by NMF has been improved significantly, and the proposed NMF-RVM method has higher precision and greater generalization ability than RVM method.