基于动态熵权的短期风速组合预测

为了提高风电场风速预测的准确性,将不同预测方法的权重推广到权重序列,生成权重矩阵,同时采用新的预测误差更新权重矩阵,获得所需模型.建立三种单一预测模型,统计它们十天的预测误差,获得误差序列,在此基础上,提出动态熵权法.采用熵权法确定各单一预测模型在96个预测时刻的权值,并根据新的24小时预测误差更新误差序列和权重矩阵,从而获得动态组合预测模型.结果表明,动态组合预测模型的整体误差指标比单一预测模型较小,预测精度显然增高,证明了所建模型有效且实用.

Short term wind speed combination forecast based on dynamic entropy weight

In order to improve the accuracy of wind speed forecast in wind farm, the weights of different forecast methods were extended to weight series so as to generate the weight matrix. Meanwhile, the new forecast error was used to update the weight matrix, and the required model was obtained. Three single forecast models were established, and the forecast errors of the models in 10 days were counted, and the error series was obtained. On this basis, the dynamic entropy method was proposed. In addition, the weights of each single forecast model at 96 forecast moments were determined with the entropy weight method, and the error series and weight matrix were updated according to new 24h forecast error so as to obtain the dynamic combination forecast model. The results show that compared with the sigle forecast model, the dynamic combination forecast model has smaller overall error index, and the forecast accuracy gets effectively improved, which proves the validity and practicability of the proposed model.