CAJ | 학술논문

在中长期风速预测中，正确处理相关因素的影响是提高风速预测精度的关键。该文提出一种粗糙集理论预测方法。利用粗糙集理论分析出风速预测的主要影响因素，将其作为中长期风速预测模型的附加输入，建立粗糙集神经网络预测模型。利用黑龙江某风电场的数据进行训练和预测，并将预测结果与单纯的混沌神经网络预测方法和持续模型方法进行对比，结果表明，粗糙集神经网络模型的预测精度最高。粗糙集方法在中长期风速预测中将是一个有用的工具。
재중장기풍속예측중，정학처리상관인소적영향시제고풍속예측정도적관건。해문제출일충조조집이론예측방법。이용조조집이론분석출풍속예측적주요영향인소，장기작위중장기풍속예측모형적부가수입，건립조조집신경망락예측모형。이용흑룡강모풍전장적수거진행훈련화예측，병장예측결과여단순적혼돈신경망락예측방법화지속모형방법진행대비，결과표명，조조집신경망락모형적예측정도최고。조조집방법재중장기풍속예측중장시일개유용적공구。
In mid-long term wind speed prediction, dealing with the relevant factors correctly is the key point to improve the prediction accuracy. A new prediction scheme that uses rough set method was presented. The key factors that affect the wind speed prediction were identified by rough set theory. Then the rough set neural network prediction model was built by adding the key factors as the additional inputs to the pure chaos neural network model. To test the approach, the data from a wind farm of Heilongjiang province were used. The prediction results were presented and compared to the chaos neural network model and persistence model. The results show that the prediction accuracy of rough set method is the best, and rough set method is a useful tool in mid-long term wind speed prediction.