1.修改光伏物理模型

models_processing/model_predict/model_prediction_photovoltaic_physical.py

@@ -22,23 +22,30 @@ def forecast_data_distribution(pre_data, args):
     col_time = args['col_time']
     farm_id = args['farm_id']
     col_radiance = args['col_radiance']
-    radiance_max = float(args['radiance_max'])
+    pre_radiance_max = pre_data[col_radiance].max()
     cap = float(args['cap'])
     pre_data['farm_id'] = farm_id
+    if 'control_type' in args and args['control_type'] == 'cut':
+        radiance_max = args['radiance_max']
+    else:
+        radiance_max = max(float(args['radiance_max']), pre_radiance_max)
     pre_data['power_forecast'] = round(pre_data[col_radiance] * cap / radiance_max, 2)
+
     if 'sunrise_time' in args:
         sunrise_time = args['sunrise_time']
         pre_data.loc[pre_data[col_time].dt.time < sunrise_time, 'power_forecast'] = 0
     if 'sunset_time' in args:
         sunset_time = args['sunset_time']
-        pre_data[pre_data[col_time] > sunset_time, 'power_forecast'] = 0
+        pre_data.loc[pre_data[col_time] > sunset_time, 'power_forecast'] = 0
+    pre_data.loc[pre_data['power_forecast'] > cap, 'power_forecast'] = cap
+    pre_data.loc[pre_data['power_forecast'] < 0, 'power_forecast'] = 0
     return pre_data[['farm_id', 'date_time', 'power_forecast']]
 
 
 def model_prediction(df, args):
     # 新增日出、日落时间参数
     howLongAgo, farm_id, target, cap, col_radiance, radiance_max, model_name, col_time = int(args['howLongAgo']), args['farm_id'], \
-    args['target'], float(args['cap']), args['col_radiance'], args['radiance_max'], args['model_name'], args['col_time']
+    args['target'], float(args['cap']), args['col_radiance'], float(args['radiance_max']), args['model_name'], args['col_time']
     df['power_forecast'] = round(df[col_radiance]*cap/radiance_max, 2)
     df.loc[df['power_forecast'] < 0, 'power_forecast'] = 0
     if 'sunrise_time' in args:
@@ -50,6 +57,8 @@ def model_prediction(df, args):
     df['model'] = model_name
     df['howLongAgo'] = howLongAgo
     df['farm_id'] = farm_id
+    df.loc[df['power_forecast'] > cap, 'power_forecast'] = cap
+    df.loc[df['power_forecast'] < 0, 'power_forecast'] = 0
     print("model predict result  successfully!")
     return df[['dateTime', 'howLongAgo', 'model', 'farm_id', 'power_forecast', target]]