# Predbat custom-inverter config for the EP Cube
# Drop into Predbat's apps.yaml under `pred_bat:` (replace the inverter block).
#
# Usage:
#   1. Set predbat_inverter_type to a unique name; "EP_CUBE" is fine.
#   2. After saving, restart the Predbat add-on and watch its logs to verify
#      it sees the entities and successfully calls our shim services.
#
# Entity IDs are stable across devIds — the integration uses a fixed
# `EP Cube` device name, so the slug is just `sensor.ep_cube_<key>` with
# no per-account substitution needed.
#
# References:
#   - https://springfall2008.github.io/batpred/inverter-setup/
#   - https://springfall2008.github.io/batpred/apps-yaml/

pred_bat:
  # ─── Inverter identification ──────────────────────────────────────────────
  inverter_type: 'EP_CUBE'
  num_inverters: 1
  ge_inverter: 0  # not GivEnergy

  # ─── Sensor entities (read) ───────────────────────────────────────────────
  soc_kw:
    - sensor.ep_cube_battery_energy
  soc_max:
    - sensor.ep_cube_battery_capacity
  soc_percent:
    - sensor.ep_cube_battery_soc

  battery_power:
    - sensor.ep_cube_battery_power
  pv_power:
    - sensor.ep_cube_solar_power
  load_power:
    - sensor.ep_cube_load_power
  grid_power:
    - sensor.ep_cube_grid_power

  reserve:
    - sensor.ep_cube_reserve_soc

  # ─── Battery physical specs ───────────────────────────────────────────────
  # Adjust to match your hardware.
  battery_rate_max_charge: 5.0     # kW
  battery_rate_max_discharge: 5.0  # kW
  battery_loss: 0.03               # 3% conversion loss (typical Li-ion + inverter)
  battery_loss_discharge: 0.03
  inverter_loss: 0.03

  # ─── Control surface (write) ──────────────────────────────────────────────
  # Predbat calls these services. Our shim translates rate+window → TOU rewrite.
  inverter_can_charge_during_export: True
  inverter_can_force_export: True

  # Each *_service is a dict (Predbat schema) — service: <domain>.<name>.
  # Our shim takes targets via service_data, not entity_id, so no entity_id here.

  # Charge controls
  charge_start_service:
    service: ep_cube.charge_start
  charge_stop_service:
    service: ep_cube.charge_stop
  charge_freeze_service:
    service: ep_cube.charge_freeze

  # Discharge controls
  discharge_start_service:
    service: ep_cube.discharge_start
  discharge_stop_service:
    service: ep_cube.discharge_stop
  discharge_freeze_service:
    service: ep_cube.discharge_freeze

  # Idle / restore baseline
  idle_service:
    service: ep_cube.idle

  # If you have multiple EP Cubes, uncomment and set the device_id explicitly:
  # service_data:
  #   device_id: <your-devId>  # the small-int devId from the HA integration config

  # ─── Tariff (auto-detected from BottlecapDave's Octopus HACS integration) ─
  # https://github.com/BottlecapDave/HomeAssistant-OctopusEnergy
  # Regex auto-matches whatever MPAN/serial the integration exposes, and
  # whatever tariff is currently active on the account (Flexible / Agile /
  # Cosy / etc.) — no apps.yaml change needed when switching tariffs.
  metric_octopus_import: 're:(event.octopus_energy_electricity_[0-9a-z_]+(?<!export)_current_day_rates)'
  # Only uncomment once you actually have an Octopus export tariff (Outgoing,
  # Export SEG, etc). Predbat 8.40.9+ treats a no-match regex here as a hard
  # validation error ("Demand (unhealthy)") rather than a benign disable.
  # metric_octopus_export: 're:(event.octopus_energy_electricity_[0-9a-z_]+_export_current_day_rates)'

  # Until then, hardcode test prices to validate Predbat → shim → cloud loop:
  # rates_import:
  #   -
  #     start: "00:00:00"
  #     end: "07:00:00"
  #     rate: 8.0   # p/kWh
  #   -
  #     start: "16:00:00"
  #     end: "19:00:00"
  #     rate: 35.0
  #   -
  #     start: "07:00:00"
  #     end: "16:00:00"
  #     rate: 22.0
  #   -
  #     start: "19:00:00"
  #     end: "00:00:00"
  #     rate: 22.0

  # ─── Forecast / consumption ──────────────────────────────────────────────
  # Once Octopus account live, point at the BottlecapDave integration:
  # load_forecast: 'sensor.octopus_energy_electricity_<MPAN>_<SERIAL>_previous_consumption'
  # For dev, Predbat will use historical HA data from `load_power` sensor.

  # ─── ML load prediction (https://springfall2008.github.io/batpred/load-ml/)
  # Neural-net 48-hour load forecast trained on your load history + PV
  # generation + outdoor temperature. Recommended.
  #
  # Shadow mode first — `load_ml_enable: true` starts training and publishes
  # `sensor.predbat_load_ml_forecast` but does NOT yet affect planning.
  # After 24-48h, compare the forecast to actuals. If sane, uncomment
  # `load_ml_source: true` so Predbat uses the ML predictions in the
  # optimiser. Needs 1+ day of history (7+ recommended).
  load_ml_enable: true
  # load_ml_source: true     # enable after shadow-mode verification
  temperature_enable: true   # outdoor temp from zone.home — recommended ML input

  # ─── Predbat behaviour ───────────────────────────────────────────────────
  forecast_hours: 48
  best_soc_min: 0.5     # kWh — never plan below this
  best_soc_keep: 1.0    # kWh — soft reserve target
  combine_charge_slots: True
  combine_discharge_slots: True