Explain each zone: contributions, forecast steps, coefficients

Each zone forecast entity now shows the current temperature, the
forecast at +15/+30/+60/+120 minutes (what the horizon covers), which
model layer is in use, and what drives the zone right now in °C/h:
envelope loss, boiler, bias, electrical heat, neighbours, ventilation
and recovery, plus each layer's coefficients with standard error,
confidence and whether it is identified. These attributes change every
poll and are not recorded.

Tests for inter-zone exchange on a two-room synthetic apartment (living
room and a colder bedroom, one boiler): with a true coupling of 0.01 the
learner finds a clearly positive value but, because the difference
between rooms is fairly steady and therefore collinear with each room's
bias, correctly reports it as not identified; with no coupling it stays
near zero.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

tests/ha/test_lifecycle.py

@@ -93,6 +93,10 @@ async def test_storage_restore_and_remove(hass, entry, inputs):
     forecast = hass.states.get(forecast_id)
     assert len(forecast.attributes["forecast_15min"]) == len(forecast.attributes["plan_15min"]) == 4
     assert forecast.attributes["forecast_15min"][3] == float(forecast.state)
+    assert forecast.attributes["forecast"]["60_min"] == float(forecast.state)
+    assert set(forecast.attributes["forecast"]) == {"15_min", "30_min", "60_min"}  # 60-minute horizon
+    assert set(forecast.attributes["contributions_c_per_h"]) == {"loss", "heat", "bias"}
+    assert forecast.attributes["model_in_use"] == "base"
     assert {"forecast_15min", "plan_15min"} <= forecast.state_info["unrecorded_attributes"]
     assert await hass.config_entries.async_remove(entry.entry_id)
     await hass.async_block_till_done()