东北电力大学学报
東北電力大學學報
동북전력대학학보
JOURNAL OF NORTHEAST DIANLI UNIVERSITY
2014年
4期
28-33
,共6页
空间负荷预测%支持向量机%粒子群优化算法%元胞负荷%城市电网
空間負荷預測%支持嚮量機%粒子群優化算法%元胞負荷%城市電網
공간부하예측%지지향량궤%입자군우화산법%원포부하%성시전망
Spatial load forecasting%Support vector machine%Particle swarm optimization%Cellular load%Urban power system
提出了一种基于支持向量机( Support Vector Machine, SVM )的城市电网空间负荷预测( Spatial Load forecasting,SLF)方法。该方法首先以等大小网格划分的规则生成元胞,并获取元胞历年负荷；然后将各元胞历年负荷最大值及其对应的年份输入支持向量机预测模型进行训练,其中采用粒子群优化( Particle Swarm Optimization,PSO)算法寻求预测模型的最优参数,预测各元胞目标年负荷最大值,从而实现空间负荷预测；最后对吉林市城市电网进行实例分析,结果验证了该方法的实用性和有效性。
提齣瞭一種基于支持嚮量機( Support Vector Machine, SVM )的城市電網空間負荷預測( Spatial Load forecasting,SLF)方法。該方法首先以等大小網格劃分的規則生成元胞,併穫取元胞歷年負荷；然後將各元胞歷年負荷最大值及其對應的年份輸入支持嚮量機預測模型進行訓練,其中採用粒子群優化( Particle Swarm Optimization,PSO)算法尋求預測模型的最優參數,預測各元胞目標年負荷最大值,從而實現空間負荷預測；最後對吉林市城市電網進行實例分析,結果驗證瞭該方法的實用性和有效性。
제출료일충기우지지향량궤( Support Vector Machine, SVM )적성시전망공간부하예측( Spatial Load forecasting,SLF)방법。해방법수선이등대소망격화분적규칙생성원포,병획취원포력년부하；연후장각원포력년부하최대치급기대응적년빈수입지지향량궤예측모형진행훈련,기중채용입자군우화( Particle Swarm Optimization,PSO)산법심구예측모형적최우삼수,예측각원포목표년부하최대치,종이실현공간부하예측；최후대길림시성시전망진행실례분석,결과험증료해방법적실용성화유효성。
Spatial load forecasting(SLF) of urban power system based on support vector machine(SVM) is proposed. Firstly,cells are generated following the rules of grid size division,and cellular historical loads are obtained;then the maximum load of cells and the years are trained in the prediction model of support vector machine,and the optimal parameters of prediction models are calculated by using the particle swarm optimiza-tion( PSO) algorithm,the maximum load values of cells in the target-year are forecasted for spatial load fore-casting;Finally,as an example of urban power system in Jilin,results of actural case verify the practicality and availability of the proposed method.
