Merge pull request #695 from davidusb-geek/davidusb-geek/fix/hot_fix_influxdb_credentials

Hot fix for InfluxDB not passed credentials

Author: davidusb-geek

CHANGELOG.md

@@ -1,5 +1,11 @@
 # Changelog
 
+## 0.16.1 - 2026-01-23
+### Fix
+- Hot fix for InfluxDB not passed credentials
+- Fixed optimization window size mismatch issue
+- Fixed optim_status key not found error when falling back to LP problem
+
 ## 0.16.0 - 2026-01-21
 
 ### 🚀 Major Optimization Engine Overhaul

conda/meta.yaml

@@ -1,5 +1,5 @@
 {% set name = "emhass" %}
-{% set version = "0.16.0" %}
+{% set version = "0.16.1" %}
 
 package:
   name: {{ name|lower }}

docs/conf.py

@@ -23,7 +23,7 @@
 author = "David HERNANDEZ TORRES"
 
 # The full version, including alpha/beta/rc tags
-release = "0.16.0"
+release = "0.16.1"
 
 # -- General configuration ---------------------------------------------------
 

docs/publish_data.md

@@ -49,11 +49,24 @@ By default, running an optimization in EMHASS will output the results into the C
 ```bash
 # RUN dayahead
 curl -i -H 'Content-Type:application/json' -X POST -d {} http://localhost:5000/action/dayahead-optim
-# Then publish teh results of dayahead
+# Then publish the results of dayahead
 curl -i -H 'Content-Type:application/json' -X POST -d {} http://localhost:5000/action/publish-data
 ```
 *Note, the published entities from the publish-data action will not automatically update the entities' current state (current state being used to check when to turn on and off appliances via Home Assistant automations). To update the EMHASS entities state, another publish would have to be re-run later when the current time matches the next value's timestamp (e.g. every 30 minutes). See examples below for methods to automate the publish-action.*
 
+```{note}
+
+If your are using the thermal model, then for this manual publish implementation you need to indicate which of your deferrable loads are thermal.
+We do this with the `"def_load_config"` entry.
+
+If you have just one deferrable load which is a thermal load then the payload of `curl` publish command should look like:
+`{"def_load_config": [{"thermal_config": {}}]}`
+
+Similarly if you have three deferrable loads where the second load is a thermal load the payload would have been:
+`{"def_load_config": [{},{"thermal_config": {}},{}]}`
+
+```
+
 ### Continual_publish *(EMHASS Automation)*
 Setting `continual_publish` to `true` in the configuration saves the output of the optimization into the `data_path/entities` folder *(a .json file for each sensor/entity)*. A constant loop (in `optimization_time_step` minutes) will run, observe the .json files in that folder, and publish the saved files periodically (updating the current state of the entity by comparing date.now with the saved data value timestamps). 
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "emhass"
-version = "0.16.0"
+version = "0.16.1"
 description = "An Energy Management System for Home Assistant"
 readme = "README.md"
 requires-python = ">=3.10, <3.13"

src/emhass/optimization.py

@@ -75,7 +75,6 @@ def __init__(
         self.freq = self.retrieve_hass_conf["optimization_time_step"]
         self.time_zone = self.retrieve_hass_conf["time_zone"]
         self.time_step = self.freq.seconds / 3600  # in hours
-        self.time_delta = pd.to_timedelta(opt_time_delta, "hours")  # The period of optimization
         self.var_pv = self.retrieve_hass_conf["sensor_power_photovoltaics"]
         self.var_load = self.retrieve_hass_conf["sensor_power_load_no_var_loads"]
         self.var_load_new = self.var_load + "_positive"
@@ -86,6 +85,19 @@ def __init__(
         self.var_prod_price = var_prod_price
         self.optim_status = None
 
+        # Prioritize config value over default arg
+        if "delta_forecast_daily" in self.optim_conf:
+            # If configured in days (int/float), convert to timedelta
+            val = self.optim_conf["delta_forecast_daily"]
+            if isinstance(val, int) or isinstance(val, float):
+                self.time_delta = pd.to_timedelta(val, "days")
+            else:
+                # Assume it is already a timedelta or compatible
+                self.time_delta = pd.to_timedelta(val)
+        else:
+            # Fallback to the argument (default 24h)
+            self.time_delta = pd.to_timedelta(opt_time_delta, "hours")
+
         # Configuration for Solver
         if "num_threads" in optim_conf.keys():
             if optim_conf["num_threads"] == 0:
@@ -1715,18 +1727,21 @@ def pad_list(input_list, target_len, fill=0):
         self.optim_status = status_raw.title() if status_raw else "Failure"
 
         # Helper: Ensure we return "Optimal" for tests if it was "Optimal (Relaxed)" or "Optimal_Inaccurate"
-        if "Optimal" in self.optim_status:
-            pass  # Keep it as is (e.g. "Optimal (Relaxed)")
-
-        self.logger.info("Status: " + self.optim_status)
-
         if self.prob.value is None or self.prob.status not in [
             cp.OPTIMAL,
             cp.OPTIMAL_INACCURATE,
             "Optimal (Relaxed)",
         ]:
             self.logger.warning("Cost function cannot be evaluated or Infeasible/Unbounded")
-            return pd.DataFrame()
+
+            # Create a DataFrame with the correct index (timestamps)
+            opt_tp = pd.DataFrame(index=data_opt.index)
+
+            # explicitely set the status column so downstream functions (like get_injection_dict)
+            # don't crash when trying to access or drop it.
+            opt_tp["optim_status"] = self.optim_status
+
+            return opt_tp
         else:
             self.logger.info(
                 "Total value of the Cost function = %.02f",
@@ -1785,7 +1800,7 @@ def perform_perfect_forecast_optim(
             day_end = day + self.time_delta - self.freq
             if day_start.tzinfo != day_end.tzinfo:
                 self.logger.warning(
-                    f"Skipping day {day} as days have ddifferent timezone, probably because of DST."
+                    f"Skipping day {day} as days have different timezone, probably because of DST."
                 )
                 continue  # Skip this day and move to the next iteration
             else:

src/emhass/utils.py

@@ -2104,6 +2104,12 @@ async def build_params(
     params["retrieve_hass_conf"]["Latitude"] = params_secrets.get("Latitude")
     params["retrieve_hass_conf"]["Longitude"] = params_secrets.get("Longitude")
     params["retrieve_hass_conf"]["Altitude"] = params_secrets.get("Altitude")
+    if params_secrets.get("influxdb_username") is not None:
+        params["retrieve_hass_conf"]["influxdb_username"] = params_secrets.get("influxdb_username")
+        params["params_secrets"]["influxdb_username"] = params_secrets.get("influxdb_username")
+    if params_secrets.get("influxdb_password") is not None:
+        params["retrieve_hass_conf"]["influxdb_password"] = params_secrets.get("influxdb_password")
+        params["params_secrets"]["influxdb_password"] = params_secrets.get("influxdb_password")
     # Update optional param secrets
     if params["optim_conf"].get("weather_forecast_method", None) is not None:
         if params["optim_conf"]["weather_forecast_method"] == "solcast":