CAJ | 학술논문

利用重庆市九龙坡区电网2009年7月1日0：00—10月8日4：0099 d共2380个历史电力负荷数据，分析其特点和规律。将构建混沌理论的平均位移（AD）法和支持向量机（SVM）相结合，提出了一种新的短期电力负荷预测模型。通过仿真计算，将结果与神经网络法预测结果进行对比，可得新方法能较好反应数据变化趋势，并且具备较好的拟合能力，能够提高负荷预测精度。在实际短期电力负荷预测中，可优先选用平均位移法与支持向量机相结合的新方法。
이용중경시구룡파구전망2009년7월1일0：00—10월8일4：0099 d공2380개역사전력부하수거，분석기특점화규률。장구건혼돈이론적평균위이（AD）법화지지향량궤（SVM）상결합，제출료일충신적단기전력부하예측모형。통과방진계산，장결과여신경망락법예측결과진행대비，가득신방법능교호반응수거변화추세，병차구비교호적의합능력，능구제고부하예측정도。재실제단기전력부하예측중，가우선선용평균위이법여지지향량궤상결합적신방법。
The characteristics and rules of the data of 2 380 historical power load in Jiulongpo district of Chongqing, from 0:00 July 1, 2009 to 4:00 October 8 were obtained and their characteristics and regularity were analyzed. Based on the average displacement (AD) method of chaos theory and the support vector machine(SVM), a new short?term power load forecasting model is built. Through simulation, the results of the new model and load forecasting method of chaotic neural networks is analyzed and compared. The results show that the support vector machine reflects the trend of data better and thus improves the precision of load forecasting. Therefore, in practical short?term power load forecasting, the new approach based on the average displacement method and the support vector machine can be chosen preferentially.