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@@ -803,7 +803,8 @@ if __name__ == "__main__" :
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GradientCalAmount_mode_Icing = []
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GradientCalAmount_mode_Icing = []
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GradientCalAmount_mode_StorageOnly = []
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GradientCalAmount_mode_StorageOnly = []
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GradientCalAmount_mode_Parallel = []
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GradientCalAmount_mode_Parallel = []
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- GradientCalAmount_mode_ChillerOnly = []
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+ GradientCalAmount_mode_ChillerOnly_1 = []
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+ GradientCalAmount_mode_ChillerOnly_2 = []
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isNan_Point = False
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isNan_Point = False
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for i in range(len(trainStatus)):
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for i in range(len(trainStatus)):
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for j in range(len(nan_point)):
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for j in range(len(nan_point)):
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@@ -815,10 +816,12 @@ if __name__ == "__main__" :
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GradientCalAmount_mode_Icing.append(trainCalAmount[i]-trainCalAmount[i-1])
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GradientCalAmount_mode_Icing.append(trainCalAmount[i]-trainCalAmount[i-1])
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elif trainStatus[i] == StorageOnly and trainCalAmount[i] < trainCalAmount[i-1]:
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elif trainStatus[i] == StorageOnly and trainCalAmount[i] < trainCalAmount[i-1]:
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GradientCalAmount_mode_StorageOnly.append(trainCalAmount[i]-trainCalAmount[i-1])
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GradientCalAmount_mode_StorageOnly.append(trainCalAmount[i]-trainCalAmount[i-1])
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- elif trainStatus[i] == Parallel and trainCalAmount[i] < trainCalAmount[i-1] and (trainRefStatus1[i] == 1 or trainRefStatus2[i] == 1):
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+ elif trainStatus[i] == Parallel and trainCalAmount[i] < trainCalAmount[i-1] and trainRefStatus1[i] + trainRefStatus2[i] == 1:
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GradientCalAmount_mode_Parallel.append(trainCalAmount[i]-trainCalAmount[i-1])
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GradientCalAmount_mode_Parallel.append(trainCalAmount[i]-trainCalAmount[i-1])
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- elif trainStatus[i] == ChillerOnly and trainRefStatus1[i] == 1 and trainRefStatus2[i] == 1:
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- GradientCalAmount_mode_ChillerOnly.append(trainCalAmount[i]-trainCalAmount[i-1])
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+ elif trainStatus[i] == ChillerOnly and trainRefStatus1[i] + trainRefStatus2[i] == 1:
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+ GradientCalAmount_mode_ChillerOnly_1.append(trainCalAmount[i]-trainCalAmount[i-1])
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+ elif trainStatus[i] == ChillerOnly and trainRefStatus1[i] + trainRefStatus2[i] == 2:
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+ GradientCalAmount_mode_ChillerOnly_2.append(trainCalAmount[i]-trainCalAmount[i-1])
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isNan_Point = False
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isNan_Point = False
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GradientCalAmount_w3sigma_mode_Icing = []
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GradientCalAmount_w3sigma_mode_Icing = []
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@@ -836,11 +839,15 @@ if __name__ == "__main__" :
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max3sigma_mode_Parallel = np.mean(GradientCalAmount_mode_Parallel)+np.std(GradientCalAmount_mode_Parallel)*3
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max3sigma_mode_Parallel = np.mean(GradientCalAmount_mode_Parallel)+np.std(GradientCalAmount_mode_Parallel)*3
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min3sigma_mode_Parallel = np.mean(GradientCalAmount_mode_Parallel)-np.std(GradientCalAmount_mode_Parallel)*3
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min3sigma_mode_Parallel = np.mean(GradientCalAmount_mode_Parallel)-np.std(GradientCalAmount_mode_Parallel)*3
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- GradientCalAmount_w3sigma_mode_ChillerOnly = []
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- if len(GradientCalAmount_mode_ChillerOnly) != 0:
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- max3sigma_mode_ChillerOnly = np.mean(GradientCalAmount_mode_ChillerOnly)+np.std(GradientCalAmount_mode_ChillerOnly)*3
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- min3sigma_mode_ChillerOnly = np.mean(GradientCalAmount_mode_ChillerOnly)-np.std(GradientCalAmount_mode_ChillerOnly)*3
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+ GradientCalAmount_w3sigma_mode_ChillerOnly_1 = []
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+ if len(GradientCalAmount_mode_ChillerOnly_1) != 0:
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+ max3sigma_mode_ChillerOnly_1 = np.mean(GradientCalAmount_mode_ChillerOnly_1)+np.std(GradientCalAmount_mode_ChillerOnly_1)*3
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+ min3sigma_mode_ChillerOnly_1 = np.mean(GradientCalAmount_mode_ChillerOnly_1)-np.std(GradientCalAmount_mode_ChillerOnly_1)*3
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+ GradientCalAmount_w3sigma_mode_ChillerOnly_2 = []
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+ if len(GradientCalAmount_mode_ChillerOnly_2) != 0:
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+ max3sigma_mode_ChillerOnly_2 = np.mean(GradientCalAmount_mode_ChillerOnly_2)+np.std(GradientCalAmount_mode_ChillerOnly_2)*3
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+ min3sigma_mode_ChillerOnly_2 = np.mean(GradientCalAmount_mode_ChillerOnly_2)-np.std(GradientCalAmount_mode_ChillerOnly_2)*3
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for i in range(len(GradientCalAmount_mode_Icing)):
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for i in range(len(GradientCalAmount_mode_Icing)):
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if GradientCalAmount_mode_Icing[i] <= max3sigma_mode_Icing and GradientCalAmount_mode_Icing[i] >= min3sigma_mode_Icing:
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if GradientCalAmount_mode_Icing[i] <= max3sigma_mode_Icing and GradientCalAmount_mode_Icing[i] >= min3sigma_mode_Icing:
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@@ -854,9 +861,13 @@ if __name__ == "__main__" :
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if GradientCalAmount_mode_Parallel[i] <= max3sigma_mode_Parallel and GradientCalAmount_mode_Parallel[i] >= min3sigma_mode_Parallel:
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if GradientCalAmount_mode_Parallel[i] <= max3sigma_mode_Parallel and GradientCalAmount_mode_Parallel[i] >= min3sigma_mode_Parallel:
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GradientCalAmount_w3sigma_mode_Parallel.append(GradientCalAmount_mode_Parallel[i])
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GradientCalAmount_w3sigma_mode_Parallel.append(GradientCalAmount_mode_Parallel[i])
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- for i in range(len(GradientCalAmount_mode_ChillerOnly)):
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- if GradientCalAmount_mode_ChillerOnly[i] <= max3sigma_mode_ChillerOnly and GradientCalAmount_mode_ChillerOnly[i] >= min3sigma_mode_ChillerOnly:
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- GradientCalAmount_w3sigma_mode_ChillerOnly.append(GradientCalAmount_mode_ChillerOnly[i])
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+ for i in range(len(GradientCalAmount_mode_ChillerOnly_1)):
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+ if GradientCalAmount_mode_ChillerOnly_1[i] <= max3sigma_mode_ChillerOnly_1 and GradientCalAmount_mode_ChillerOnly_1[i] >= min3sigma_mode_ChillerOnly_1:
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+ GradientCalAmount_w3sigma_mode_ChillerOnly_1.append(GradientCalAmount_mode_ChillerOnly_1[i])
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+
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+ for i in range(len(GradientCalAmount_mode_ChillerOnly_2)):
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+ if GradientCalAmount_mode_ChillerOnly_2[i] <= max3sigma_mode_ChillerOnly_2 and GradientCalAmount_mode_ChillerOnly_2[i] >= min3sigma_mode_ChillerOnly_2:
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+ GradientCalAmount_w3sigma_mode_ChillerOnly_2.append(GradientCalAmount_mode_ChillerOnly_2[i])
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#print(np.mean(GradientCalAmount_w3sigma_mode_Icing), np.mean(GradientCalAmount_w3sigma_mode_StorageOnly), np.mean(GradientCalAmount_w3sigma_mode_Parallel), np.mean(GradientCalAmount_w3sigma_mode_ChillerOnly))
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#print(np.mean(GradientCalAmount_w3sigma_mode_Icing), np.mean(GradientCalAmount_w3sigma_mode_StorageOnly), np.mean(GradientCalAmount_w3sigma_mode_Parallel), np.mean(GradientCalAmount_w3sigma_mode_ChillerOnly))
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#print(np.std(GradientCalAmount_w3sigma_mode_Icing), np.std(GradientCalAmount_w3sigma_mode_StorageOnly), np.std(GradientCalAmount_w3sigma_mode_Parallel), np.std(GradientCalAmount_w3sigma_mode_ChillerOnly))
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#print(np.std(GradientCalAmount_w3sigma_mode_Icing), np.std(GradientCalAmount_w3sigma_mode_StorageOnly), np.std(GradientCalAmount_w3sigma_mode_Parallel), np.std(GradientCalAmount_w3sigma_mode_ChillerOnly))
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@@ -1067,9 +1078,9 @@ if __name__ == "__main__" :
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elif inputX[i]==ChillerOnly:
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elif inputX[i]==ChillerOnly:
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if inputX_REF1[i] + inputX_REF2[i]==2:
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if inputX_REF1[i] + inputX_REF2[i]==2:
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- RecommendedCalAmount.append(RecommendedCalAmount[-1]+np.mean(GradientCalAmount_w3sigma_mode_ChillerOnly))
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+ RecommendedCalAmount.append(RecommendedCalAmount[-1]+np.mean(GradientCalAmount_w3sigma_mode_ChillerOnly_2))
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elif inputX_REF1[i] + inputX_REF2[i]==1:
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elif inputX_REF1[i] + inputX_REF2[i]==1:
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- RecommendedCalAmount.append(RecommendedCalAmount[-1]+np.mean(GradientCalAmount_w3sigma_mode_ChillerOnly)/2)
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+ RecommendedCalAmount.append(RecommendedCalAmount[-1]+np.mean(GradientCalAmount_w3sigma_mode_ChillerOnly_1))
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else:
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else:
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RecommendedCalAmount.append(RecommendedCalAmount[-1])
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RecommendedCalAmount.append(RecommendedCalAmount[-1])
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@@ -1464,8 +1475,11 @@ if __name__ == "__main__" :
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SimulCalAmount.append(SimulCalAmount[-1]+np.mean(GradientCalAmount_w3sigma_mode_Parallel))
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SimulCalAmount.append(SimulCalAmount[-1]+np.mean(GradientCalAmount_w3sigma_mode_Parallel))
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## 냉단운전은 냉동기 두대로 운영되었으므로 축열량은 그대로
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## 냉단운전은 냉동기 두대로 운영되었으므로 축열량은 그대로
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elif CustomizedStatus[i] == ChillerOnly:
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elif CustomizedStatus[i] == ChillerOnly:
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- if len(GradientCalAmount_w3sigma_mode_ChillerOnly) == 0:
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- print('[Warning] There is no enough data (Chiller Only)')
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+ if len(GradientCalAmount_w3sigma_mode_ChillerOnly_1) == 0:
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+ print('[Warning] There is no enough data (Chiller Only_1)')
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+ SimulCalAmount.append(SimulCalAmount[-1])
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+ elif len(GradientCalAmount_w3sigma_mode_ChillerOnly_2) == 0:
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+ print('[Warning] There is no enough data (Chiller Only_2)')
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SimulCalAmount.append(SimulCalAmount[-1])
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SimulCalAmount.append(SimulCalAmount[-1])
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else:
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else:
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SimulCalAmount.append(SimulCalAmount[-1])
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SimulCalAmount.append(SimulCalAmount[-1])
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