Merge pull request #209 from fboundy/patch

Patch

README.md

@@ -1,4 +1,4 @@
-# PV Opt: Home Assistant Solar/Battery Optimiser v3.14.1
+# PV Opt: Home Assistant Solar/Battery Optimiser v3.14.2
 
 Solar / Battery Charging Optimisation for Home Assistant. This appDaemon application attempts to optimise charging and discharging of a home solar/battery system to minimise cost electricity cost on a daily basis using freely available solar forecast data from SolCast. This is particularly beneficial for Octopus Agile but is also benefeficial for other time-of-use tariffs such as Octopus Flux or simple Economy 7.
 

apps/pv_opt/config/config.yaml

@@ -81,7 +81,7 @@ pv_opt:
   # Octopus account parameters
   # ========================================
 
-  octopus_auto: False # Read tariffs from the Octopus Energy integration. If successful this over-rides the following parameters
+  # octopus_auto: False # Read tariffs from the Octopus Energy integration. If successful this over-rides the following parameters
 
   # octopus_account: !secret octopus_account
   # octopus_api_key: !secret octopus_api_key
@@ -98,8 +98,8 @@ pv_opt:
   # octopus_import_tariff_code: E-1R-FLUX-IMPORT-23-02-14-G
   # octopus_export_tariff_code: E-1R-FLUX-EXPORT-23-02-14-G
 
-  octopus_import_tariff_code: E-1R-GO-VAR-22-10-14-N
-  octopus_export_tariff_code: E-1R-OUTGOING-LITE-FIX-12M-23-09-12-N
+  # octopus_import_tariff_code: E-1R-GO-VAR-22-10-14-N
+  # octopus_export_tariff_code: E-1R-OUTGOING-LITE-FIX-12M-23-09-12-N
 
   # ===============================================================================================================
   # Brand / Integration Specific Config: SOLIS_SOLAX_MODBUS: https://github.com/wills106/homeassistant-solax-modbus

apps/pv_opt/pv_opt.py

@@ -12,7 +12,7 @@
 from numpy import nan
 import re
 
-VERSION = "3.14.1"
+VERSION = "3.14.2"
 
 OCTOPUS_PRODUCT_URL = r"https://api.octopus.energy/v1/products/"
 
@@ -2273,6 +2273,7 @@ def _get_hass_power_from_daily_kwh(self, entity_id, start=None, end=None, days=N
             df.index = pd.to_datetime(df.index)
             x = df.diff().clip(0).fillna(0).cumsum() + df.iloc[0]
             x.index = x.index.round("1s")
+            x = x[~x.index.duplicated()]
             y = -pd.concat([x.resample("1s").interpolate().resample("30min").asfreq(), x.iloc[-1:]]).diff(-1)
             dt = y.index.diff().total_seconds() / pd.Timedelta("60min").total_seconds() / 1000
             df = y[1:-1] / dt[2:]