基于自适应优化AP聚类与BP加权网络的多区域复合短期风电功率预测

MULTI-REGIONAL COMPOSITE SHORT-TERM WIND POWER PREDICTION BASED ON ADAPTIVE OPTIMIZATION AP CLUSTERING AND BP WEIGHTED NETWORK

精准的风电集群区域功率预测对电源侧的竞价上网具有重要意义。由于同一地区多个风电场受气候影响波动程度相近,可看作具有时空相关性的风电场群,并以此进行集群的合理划分。为此,提出一种基于自适应优化近邻传播(AP)聚类与反向传播(BP)加权神经网络的多区域复合短期风电功率预测模型。首先,通过粒子群优化算法(PSO)优化AP聚类方法对风电场群的历史数据进行集群的聚类与划分;然后,根据得到的最优聚类结果构建风电场群子区域样本训练集;最后,利用基于相关系数权重的BP神经网络对各子区域进行功率预测。算例结果表明:所提方法在24 h日前预测相较传统叠加法与单一BP神经网络可提高1.35%和2.62%的精度,可表明该模型具有优越的预测性能。

Accurate regional power prediction of wind power clusters is of great significance to the bidding grid on the supply side.Since multiple wind farms in the same area have similar fluctuations under climate influence,they can be regarded as wind farm clusters with temporal and spatial correlation,and the clusters are reasonably divided accordingly.Therefore,a multi-region composite short-term wind power prediction model based on adaptive optimization of affinity propagation(AP)clustering and back-propagation(BP)weighted neural network is proposed.Firstly,the historical data of wind farm clusters are clustered and divided by particle swarm optimization AP clustering method.Then,according to the obtained optimal clustering results,the training set of sub-region samples of wind farm groups is constructed.Finally,the BP neural network based on correlation coefficient weights is used to predict the power of each subregion.The example results show that the proposed method can improve the accuracy of prediction by 1.35%and 2.62%compared with the traditional superposition method and a single BP neural network before 24 h.The results show that the model has superior prediction performance.