微型机与应用
微型機與應用
미형궤여응용
MICROCOMPUTER & ITS APPLICATIONS
2012年
15期
64-66
,共3页
灰色模型GM%模拟退火SA%负荷预测
灰色模型GM%模擬退火SA%負荷預測
회색모형GM%모의퇴화SA%부하예측
gray model%simulated annealing%load forecasting
分析了灰色模型(GM)和模拟退火模型(SA),GM(1,1)学习参数的计算采用最小二乘法,而最小二乘法是基于残差平方和最小寻优,容易陷入局部最小,对于非线性较强的负荷,会产生很大的偏差。提出了一种GM(1,1)与SA相结合的方法,根据模拟退火原理,结合概率突跳特性在解空间中随机寻找目标函数的全局最优解,自动优化GM(1,1)的参数,在负荷预测的实例中取得良好效果。
分析瞭灰色模型(GM)和模擬退火模型(SA),GM(1,1)學習參數的計算採用最小二乘法,而最小二乘法是基于殘差平方和最小尋優,容易陷入跼部最小,對于非線性較彊的負荷,會產生很大的偏差。提齣瞭一種GM(1,1)與SA相結閤的方法,根據模擬退火原理,結閤概率突跳特性在解空間中隨機尋找目標函數的全跼最優解,自動優化GM(1,1)的參數,在負荷預測的實例中取得良好效果。
분석료회색모형(GM)화모의퇴화모형(SA),GM(1,1)학습삼수적계산채용최소이승법,이최소이승법시기우잔차평방화최소심우,용역함입국부최소,대우비선성교강적부하,회산생흔대적편차。제출료일충GM(1,1)여SA상결합적방법,근거모의퇴화원리,결합개솔돌도특성재해공간중수궤심조목표함수적전국최우해,자동우화GM(1,1)적삼수,재부하예측적실례중취득량호효과。
The gray model (GM) and simulated annealing model (SA) were analyzed.Learning parameters of GM (1,1) were calculated by the least squares method, while the least squares method was based on the minimum residual sum of squares optimization.This method was easy to fall into local minimum and would have a huge bias for the strong non-linear load. A method based on GM (1,1) and SA was proposed,combined with the probability of sudden jump in the solution space characteristics of the objective function of a random search for global optimal solution, automatic optimization of GM(1,1) of the parameters.This proposed method can efficiently select the parameters of LS-SVM methed ,and the accuracy of load forecasting is effectively improved.