基于VMD-WSGRU的风电场发电功率中短期及短期预测

Short-term and Mid-short-term Wind Power Forecasting Based on VMD-WSGRU

针对风电功率随机性较强、时序关联难以建模的问题,构建了变分模式分解(variational mode decomposition,VMD)与权值共享门控循环单元(weight sharing gate recurrent unit,WSGRU)组合而成的VMD-WSGRU预测模型。模型首先应用变分模式分解将历史风力发电功率等序列信息非递归地分解为指定层数的模态分量,不同模态分量代表了其不同尺度的特征,同时降低了原始序列的不平稳度,随后使用WSGRU对分析出的所有子分量整体进行快速准确的建模预测,最后使用人工神经网络(artificial neural network,ANN)修正并得到风功率的预测结果。算例结果表明,与传统单一模型预测方法相比,所提集成预测模型能够更好地把握风功率的趋势,具有更好的预测精度。与其他常见组合预测方法相比,本方法的训练也更加准确高效。

Aiming at the problem of strong randomness of wind power and difficulty in modeling time series correlation, a combination of variational mode decomposition(VMD) and weight sharing gate recurrent unit neural network(WSGRU)The integrated VMD-WSGRU integrated learning and prediction method. Case studies show that the proposed model can effectively track the change of wind power and has high short-term prediction accuracy. The model uses variational modal decomposition to non-recursively decompose the original wind speed sequence into sub-components with a predetermined number of layers at first. The modal function components at different frequencies represent different characteristics of the power load, while reducing the original sequence. Stationary degree, then use WSGRU to quickly and accurately model and predict all the analyzed sub-components as a whole, and finally use ANN to modify and obtain the prediction result of wind power. The calculation results show that, compared with the traditional single model prediction method, the proposed integrated prediction model can better grasp the trend of wind power and has better prediction accuracy. Compared with other common combined prediction methods, the training of this method is more accurate and efficient.