CAJ | 학술논문

由于风电场输出功率受风速、气温、气压和湿度等不确定性因素的影响，导致风电功率的预测精度偏低。因此，提出了模糊理论与支持向量机理论相结合的组合预测模型。该组合预测方法用模糊隶属函数计算样本数据的模糊隶属度值，生成模糊样本集。然后用支持向量机对模糊样本集训练建模，最后利用预测模型预测未来24小时的风电功率。仿真实验表明，该组合预测方法有效地提高了风电功率预测精度，具有很强的工程实用性。
유우풍전장수출공솔수풍속、기온、기압화습도등불학정성인소적영향，도치풍전공솔적예측정도편저。인차，제출료모호이론여지지향량궤이론상결합적조합예측모형。해조합예측방법용모호대속함수계산양본수거적모호대속도치，생성모호양본집。연후용지지향량궤대모호양본집훈련건모，최후이용예측모형예측미래24소시적풍전공솔。방진실험표명，해조합예측방법유효지제고료풍전공솔예측정도，구유흔강적공정실용성。
Power output of wind farm is affected by wind speed, temperature, pressure, humidity and other uncertain factors, resulting in that the prediction accuracy of wind power is lower. Therefore, combined forecasting model of fuzzy theory and support vector machine theory is put forward in this pa-per. The combination forecasting method calculates fuzzy membership values corresponding to the sample data by using fuzzy membership function, and then generates fuzzy sample set. Then model of fuzzy sam-ple set is done by using support vector machine (SVM). Finally prediction model is used for wind power prediction in the next 24 hours. The simulation experiment shows that the combined forecasting method can effectively improve forecasting accuracy of the wind power and has comparatively strong practicality in engineering.