CAJ | 학술논문

提出一种基于模糊信息粒化和最小二乘支持向量机的风电功率平均值预测和风电功率波动范围预测的联合预测模型建模方法。该方法首先对训练样本进行模糊信息粒化，根据需要提取各个窗口的有效分量信息，即各窗口的最小值、大致平均值和最大值。其次应用最小二乘支持向量机对各个分量分别建立预测模型，并使用自适应粒子群算法对各个分量模型进行优化。最后使用优化后的最小二乘支持向量机模型对风电功率平均值和风电功率波动范围进行联合预测。实例研究表明，该联合预测模型可以有效进行风电功率平均值预测和风电功率波动范围预测的联合预测，并能有效跟踪风电功率变化。
제출일충기우모호신식립화화최소이승지지향량궤적풍전공솔평균치예측화풍전공솔파동범위예측적연합예측모형건모방법。해방법수선대훈련양본진행모호신식립화，근거수요제취각개창구적유효분량신식，즉각창구적최소치、대치평균치화최대치。기차응용최소이승지지향량궤대각개분량분별건립예측모형，병사용자괄응입자군산법대각개분량모형진행우화。최후사용우화후적최소이승지지향량궤모형대풍전공솔평균치화풍전공솔파동범위진행연합예측。실례연구표명，해연합예측모형가이유효진행풍전공솔평균치예측화풍전공솔파동범위예측적연합예측，병능유효근종풍전공솔변화。
A combination prediction model modeling method for wind power average value prediction and wind power fluctuation range prediction is proposed, which is based on the fuzzy information granulation and least squares support vector machine (LSSVM). Firstly, fuzzy information granulation of the training samples is made, and effective component information of each window is extracted according to the need, namely the minimum, average and maximum value of each window. Secondly, LSSVM of the prediction models are established for each component, and then the adaptive particle swarm algorithm is used to optimize each component model. Finally, the optimized LSSVM model is used for combined forecast in terms of wind power average value and wind power fluctuation range. The case study shows that the combined prediction model can effectviely predict wind power average value prediction and wind power fluctuation range, and accurately track the wind electric power change.