兰州交通大学学报
蘭州交通大學學報
란주교통대학학보
JOURNAL OF LANZHOU JIAOTONG UNIVERSITY（Natural Sciences）
2014年
4期
36-39
,共4页
短期预测%LS-SVM%上网发电量%太阳能发电
短期預測%LS-SVM%上網髮電量%太暘能髮電
단기예측%LS-SVM%상망발전량%태양능발전
short-term prediction%LS-SVM%grid connected power generation%solar power gener-ation
对光伏上网发电量进行短期预测,可以为电力部门的调度以及用电计划的调整提供参考。提出了一种基于最小二乘支持向量机(least square support vector machines,LS-SVM)对短期光伏上网发电量的预测方法,LS-SVM方法具有好的泛化能力。以甘肃某地区电厂的并网发电全年实测数据为实例,同时考虑到短期太阳辐射和光伏电池温度对光伏发电量的影响,建立了基于 LS-SVM的短期预测模型。与现有的前向神经网络预测方法进行比较,实验结果表明,该方法能获得更好的预测效果,具有一定的应用潜力。
對光伏上網髮電量進行短期預測,可以為電力部門的調度以及用電計劃的調整提供參攷。提齣瞭一種基于最小二乘支持嚮量機(least square support vector machines,LS-SVM)對短期光伏上網髮電量的預測方法,LS-SVM方法具有好的汎化能力。以甘肅某地區電廠的併網髮電全年實測數據為實例,同時攷慮到短期太暘輻射和光伏電池溫度對光伏髮電量的影響,建立瞭基于 LS-SVM的短期預測模型。與現有的前嚮神經網絡預測方法進行比較,實驗結果錶明,該方法能穫得更好的預測效果,具有一定的應用潛力。
대광복상망발전량진행단기예측,가이위전력부문적조도이급용전계화적조정제공삼고。제출료일충기우최소이승지지향량궤(least square support vector machines,LS-SVM)대단기광복상망발전량적예측방법,LS-SVM방법구유호적범화능력。이감숙모지구전엄적병망발전전년실측수거위실례,동시고필도단기태양복사화광복전지온도대광복발전량적영향,건립료기우 LS-SVM적단기예측모형。여현유적전향신경망락예측방법진행비교,실험결과표명,해방법능획득경호적예측효과,구유일정적응용잠력。
Prediction of short-term photovoltaic electricity can provide reference for electric power dispatching department to dispatch and adj ust the power plan.The prediction method of short-term photovoltaic electricity is presented based on Least Square Support Vector Machines (LS-SVM)with good generalization ability.Taking the annual measured data of grid-connected photo-voltaic power plant as example and considering the influence of short-term solar radiation and temperature of photovoltaic cells on the photovoltaic generation,we establish the short-term pre-diction model based on LS-SVM.This method is compared with the existing forward neural net-work prediction method.The experimental results show that this method can obtain the good pre-diction performance and has great potential application.
