Version 2026.03.0.rc3 (#565)

* Fixed error with flex setting of "dc_to_bat max power" or "bat_to_dc max power" (reported by @dabit)

* Fixed error not planning heatpump in first interval when not in run-mode (reported by @f.welvering)

* Missing hour-values (solar-inverters) are filled up by zero's (suggested by @dabit)

* Fixed error when "-" is used in name of solar-devices (reported by @patrickvorgers and @Asclepius8)

* Versie 2026.03.0.rc2

Author: corneel27

dao/prog/da_base.py

@@ -526,7 +526,7 @@ def get_setting_state(
             else:  # setting_value is een string (entity?)
                 try:
                     state = self.get_state(setting_value).state
-                    return state
+                    return float(state)
                 except Exception:
                     logging.warning(f"No value found for {key}, {setting_value}")
                     return default
@@ -620,7 +620,7 @@ def calc_solar_predictions(
             interval_s = self.interval_s
         else:
             interval_s = 900 if interval == "15min" else 3600
-        solar_name = solar_option["name"].replace(" ", "_")
+        solar_name = solar_option["name"].replace(" ", "_").replace("-","_")
         if ml_prediction:
             solar_predictor = SolarPredictor()
             try:

dao/prog/da_report.py

@@ -1044,6 +1044,38 @@ def get_sensor_data(
             elif dim == "MWh":
                 df_raw[col_name] = df_raw[col_name] * 1000
         # Print the raw DataFrame
+
+        # fill 0 for missing records
+        if agg =="uur":
+            columns = list(df_raw.columns.values)
+            df_insert = pd.DataFrame(columns=columns)
+            """
+            # vanaf hier voor de test:
+            df_raw.reset_index(drop=True, inplace=True)
+            df_raw.drop([2, 3, 4], inplace=True)
+            df_raw.index = df_raw["tijd"]
+            # tot hier voor de test
+            """
+            prev_row = None
+            for row in df_raw.itertuples():
+                if prev_row is not None:
+                    new_tijd = prev_row.tijd + datetime.timedelta(hours=1)
+                    while new_tijd < row.tijd:
+                        new_row_values = [
+                            new_tijd.strftime("%Y-%m-%d %H:%M")[11:14]+"00",
+                            pd.to_datetime(new_tijd),
+                            pd.to_datetime(new_tijd),
+                            new_tijd.timestamp(),
+                            0,
+                            prev_row.dim
+                        ]
+                        df_insert.loc[len(df_insert.index)] = new_row_values
+                        new_tijd += datetime.timedelta(hours=1)
+                prev_row = row
+            if len(df_insert) > 0:
+                df_insert.index= df_insert["tijd"]
+                df_raw = pd.concat([df_raw, df_insert])
+                df_raw.sort_index(inplace=True)
         logging.debug(f"sensordata raw, sensor {sensor},\n {df_raw.to_string()}\n")
         return df_raw
 

dao/prog/day_ahead.py

@@ -53,8 +53,8 @@ def __init__(self, file_name=None):
     def calc_optimum(
         self, _start_dt: dt.datetime | None = None, _start_soc: float | None = None
     ):
-        # _start_dt = datetime.datetime(year=2026, month=1, day=13, hour=23, minute=0)
-        # _start_soc = 11.0
+        # _start_dt = datetime.datetime(year=2026, month=2, day=25, hour=14, minute=30)
+        # _start_soc = 78.0
         if _start_dt is not None or _start_soc is not None:
             self.debug = True
         logging.info(f"Debug = {self.debug}")
@@ -294,15 +294,15 @@ def calc_optimum(
             solar_prog = self.calc_solar_predictions(
                 self.solar[s], start_interval_dt, end, self.interval
             )
-            solar_name = self.solar[s]["name"].replace(" ", "_")
+            solar_name = self.solar[s]["name"].replace(" ", "_").replace("-","_")
             prog_data[solar_name] = solar_prog["prediction"]
         for b in range(B):
             for s in range(len(self.battery_options[b]["solar"])):
                 solar_option = self.battery_options[b]["solar"][s]
                 solar_prog = self.calc_solar_predictions(
                     solar_option, start_interval_dt, end, self.interval
                 )
-                solar_name = solar_option["name"].replace(" ", "_")
+                solar_name = solar_option["name"].replace(" ", "_").replace("-","_")
                 prog_data[solar_name] = solar_prog["prediction"]
 
         # prog_data = prog_data.reset_index()
@@ -326,7 +326,7 @@ def calc_optimum(
                 hour_fraction.append(self.interval_s / 3600)
                 interval_fraction.append(1)
             for s in range(solar_num):
-                solar_name = self.solar[s]["name"].replace(" ", "_")
+                solar_name = self.solar[s]["name"].replace(" ", "_").replace("-","_")
                 prod = max(0, getattr(row, solar_name)) * interval_fraction[-1]
                 solar_prod[s].append(prod)
                 pv_total += prod
@@ -338,7 +338,7 @@ def calc_optimum(
             for b in range(B):
                 for s in range(len(self.battery_options[b]["solar"])):
                     solar_option = self.battery_options[b]["solar"][s]
-                    solar_name = solar_option["name"].replace(" ", "_")
+                    solar_name = solar_option["name"].replace(" ", "_").replace("-","_")
                     prod = max(0, getattr(row, solar_name)) * interval_fraction[-1]
                     if pv_dc_num <= 9:
                         pv_dc_varcode.append("pv_dc_" + str(pv_dc_num))
@@ -981,9 +981,10 @@ def calc_optimum(
 
         """
         constraints reduced charging power at low or high soc
-        max_power[u] <= max_power_0 + helling x (soc[u] – soc_0)
-        max_power[u] <= max_power_0 + helling x soc[u] – helling x soc_0
-        max_power[u] - helling x soc[u] <= max_power_0 - helling x soc-0
+        max_power[u] x 1000 <= max_power_0 + helling x (soc[u] – soc_0)
+        max_power[u] x 1000 <= max_power_0 + helling x soc[u] – helling x soc_0
+        max_power[u] x 1000 - helling x soc[u] <= max_power_0 - helling x soc_0
+        
         """
         # low soc
         for b in range(B):
@@ -992,7 +993,7 @@ def calc_optimum(
                 helling = int(red_power[rpl]["helling"])
                 for u in range(U):
                     model += (
-                        dc_from_bat[b][u] - helling * soc[b][u]
+                        dc_from_bat[b][u] * 1000 - helling * soc[b][u]
                         <= red_power[rpl]["power"] - helling * red_power[rpl]["soc"]
                     )
         # high soc
@@ -1002,7 +1003,7 @@ def calc_optimum(
                 helling = int(red_power[rph]["helling"])
                 for u in range(U):
                     model += (
-                        dc_to_bat[b][u] - helling * soc[b][u]
+                        dc_to_bat[b][u] * 1000 - helling * soc[b][u]
                         <= red_power[rph]["power"] - helling * red_power[rph]["soc"]
                     )
 
@@ -2496,7 +2497,7 @@ def calc_optimum(
                 for j in range(blocks_num):
                     for u in range(U):
                         model += (u - (hp_start_index[j] + block_len[j] - 1)) <= (
-                            u * (1 - hp_bl_on[j][u])
+                            U * (1 - hp_bl_on[j][u])
                         )
 
                 # constraint 3

dao/prog/solar_predictor.py

@@ -934,7 +934,7 @@ def train_solar_option(
         self, weather_data: pd.DataFrame, solar_dict: Dict, start: dt.datetime
     ):
         name = self.config.get(["name"], solar_dict, "default")
-        self.solar_name = name.replace(" ", "_")
+        self.solar_name = name.replace(" ", "_").replace("-","_")
         self.tilt = self.get_property_from_dict("tilt", solar_dict, 45)
         self.azimut = self.get_property_from_dict("orientation", solar_dict, 0) + 180
         self.solar_capacity = self.get_property_from_dict("capacity", solar_dict, 5)
@@ -1034,7 +1034,7 @@ def check_prediction(prediction, irradiance):
             return result
 
         name = self.config.get(["name"], solar_dict, "default")
-        self.solar_name = name.replace(" ", "_")
+        self.solar_name = name.replace(" ", "_").replace("-","_")
         self.tilt = self.get_property_from_dict("tilt", solar_dict, 45)
         self.azimut = self.get_property_from_dict("orientation", solar_dict, 0) + 180
         self.solar_capacity = self.get_property_from_dict("capacity", solar_dict, 5)

release-testing/CHANGELOG.md

@@ -1,5 +1,12 @@
 # Changelog 刀 DAO
 # Day Ahead Optimizer
+# 2026.03.0.rc2
+
+- Fixed error with flex setting of "dc_to_bat max power" or "bat_to_dc max power" (reported by @DaBit)
+- Fixed error not planning heatpump in first interval when not in run-mode (reported by @f.welvering)
+- Missing hour-values (solar-inverters) are filled up by zero's (suggested by @DaBit)
+- Fixed error when "-" is used in name of solar-devices (reported by @patrickvorgers and @Asclepius8)
+
 # 2026.03.0.rc1
 - Made optional battery settings "bat_to_dc max power" and "dc_to_bat max power" flex-setting (feature request by @DaBit)
 - Reduce power during charging at high soc and during discharging at low soc (feature requests form @bartzzz and @arjenhiemstra)

release-testing/config.yaml

@@ -1,6 +1,6 @@
 ---
 name: 刀 Day Ahead Optimizer (TESTING)
-version: 2026.03.0.rc1
+version: 2026.03.0.rc2
 stage: experimental
 slug: day_ahead_opt-testing
 description: Beta version of DAO. Use only for testing!