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feat(pvforecast): add adaptive per-plane PV learning and diagnostics (#1422)

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PV arrays with different orientations and curtailment need separate plant calibration and weather correction. This opt-in provider publishes normal EOS AC/DC forecast keys at 15-minute resolution.

- Preserve daily per-plane energy/shape calibration and validated short-term local/trend selection. Curtailment-sensitive arrays require a fresh release flag or an explicitly configured learning guard.
-  Add switchable empirical solar-angle response relative to separate eligible reference arrays, removing their common weather error. This learns angular residuals, not physical angles or a measured geometric horizon.
-  Archive actually received weather model inputs, first local receipt and frozen per-member PV candidates. Returned past weather is excluded; unknown source issuance remains explicit. Learn model weights and forecast-regime bias separately at 2–6, 6–24 and 24–48 hours.
- New long-horizon candidates remain in shadow until their genuinely issued outputs improve equal-day MAE by the configured margin, win on at least 60% of validation days and pass a matched-support energy-bias guard. Insufficient evidence retains calibration. Daily yields still require complete local days, including DST.
- Expose fitted parameters, independent training/validation day counts, errors, selected methods and persistence in the existing status API, with additional normal prediction keys for Grafana. Retain physical DC through angular/weather corrections an  enforce individual inverter AC limits.

Co-authored-by: Normann <github@koldrack.com>
Co-authored-by: Christin <chrissi@von-perbandt.de>
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
Co-authored-by: YMT <yn276364@dal.ca>
Co-authored-by: sarankumar1325 <120080287+sarankumar1325@users.noreply.github.com>
This commit is contained in:
Andreas
2026-11-03 15:31:32 +01:00
committed by GitHub
co-authored by Normann Christin Claude Opus 5.5 YMT sarankumar1325
parent 298d46a527
commit f804d48072
24 changed files with 6063 additions and 13 deletions
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docs/_generated/configexample.md
+70
View File
@@ -328,6 +328,56 @@
"calibration_min_factor": 0.5,
"calibration_max_factor": 1.5
},
"adaptive": {
"backend": "local",
"resolution_minutes": 15,
"forecast_days": null,
"past_days": null,
"weather_models": [
"best_match",
"icon_d2"
],
"transposition_model": "perez",
"albedo": 0.25,
"inverter_efficiency": 0.96,
"temperature_coefficient": -0.36,
"apply_iam": true,
"shift_to_interval_start": true,
"calibration_enabled": false,
"calibration_days": 30,
"calibration_reference_days": 30,
"calibration_outage_filter_enabled": true,
"calibration_outage_threshold": 0.55,
"calibration_min_healthy_days": 3,
"calibration_azimuth_bin_degrees": 45,
"calibration_prior_kwh": 5.0,
"calibration_min_factor": 0.5,
"calibration_max_factor": 1.5,
"regional_weather_model": "icon_d2",
"regional_horizon_hours": 6,
"learning_days": 21,
"min_training_days": 5,
"max_ac_power_w": 0,
"nominal_total_peakpower_kw": null,
"diagnostics_days": 7,
"archive_retention_days": 60,
"nowcast_enabled": true,
"trend_enabled": true,
"solar_response_enabled": true,
"weather_response_enabled": true,
"correction_mode": "auto",
"validation_days": 7,
"min_validation_days": 3,
"min_validation_samples": 32,
"min_validation_improvement": 0.02,
"training_delay_minutes": 15,
"total_measurement_key": null,
"total_curtailment_sensitive": false,
"total_curtailment_key": null,
"total_learning_guard_key": null,
"total_learning_guard_max": null,
"total_learning_guard_max_gap_seconds": 600
},
"pvforecastimport": {
"import_file_path": null,
"import_json": null
@@ -360,6 +410,16 @@
},
"planes": [
{
"name": null,
"measurement_key": null,
"nowcast_reference": false,
"curtailment_sensitive": false,
"parameters_estimated": false,
"correction_group": null,
"curtailment_key": null,
"learning_guard_key": null,
"learning_guard_max": null,
"learning_guard_max_gap_seconds": 600,
"surface_tilt": 10.0,
"surface_azimuth": 180.0,
"userhorizon": [
@@ -382,6 +442,16 @@
"strings_per_inverter": 2
},
{
"name": null,
"measurement_key": null,
"nowcast_reference": false,
"curtailment_sensitive": false,
"parameters_estimated": false,
"correction_group": null,
"curtailment_key": null,
"learning_guard_key": null,
"learning_guard_max": null,
"learning_guard_max_gap_seconds": 600,
"surface_tilt": 20.0,
"surface_azimuth": 90.0,
"userhorizon": [
docs/_generated/configpvforecast.md
+282
View File
@@ -7,6 +7,7 @@
| Name | Environment Variable | Type | Read-Only | Default | Description |
| ---- | -------------------- | ---- | --------- | ------- | ----------- |
| adaptive | `EOS_PVFORECAST__ADAPTIVE` | `PVForecastAdaptiveAkkudoktorCommonSettings` | `rw` | `required` | Adaptive Akkudoktor: configure string measurements and independent reference flags on the planes. |
| akkudoktor | `EOS_PVFORECAST__AKKUDOKTOR` | `PVForecastAkkudoktorLocalCommonSettings` | `rw` | `required` | Akkudoktor forecast backend and local calibration settings |
| forecastsolar | `EOS_PVFORECAST__FORECASTSOLAR` | `PVForecastForecastSolarCommonSettings` | `rw` | `required` | ForecastSolar provider settings |
| homeassistant | `EOS_PVFORECAST__HOMEASSISTANT` | `PVForecastHomeAssistantCommonSettings` | `rw` | `required` | Home Assistant provider settings |
@@ -61,6 +62,56 @@
"calibration_min_factor": 0.5,
"calibration_max_factor": 1.5
},
"adaptive": {
"backend": "local",
"resolution_minutes": 15,
"forecast_days": null,
"past_days": null,
"weather_models": [
"best_match",
"icon_d2"
],
"transposition_model": "perez",
"albedo": 0.25,
"inverter_efficiency": 0.96,
"temperature_coefficient": -0.36,
"apply_iam": true,
"shift_to_interval_start": true,
"calibration_enabled": false,
"calibration_days": 30,
"calibration_reference_days": 30,
"calibration_outage_filter_enabled": true,
"calibration_outage_threshold": 0.55,
"calibration_min_healthy_days": 3,
"calibration_azimuth_bin_degrees": 45,
"calibration_prior_kwh": 5.0,
"calibration_min_factor": 0.5,
"calibration_max_factor": 1.5,
"regional_weather_model": "icon_d2",
"regional_horizon_hours": 6,
"learning_days": 21,
"min_training_days": 5,
"max_ac_power_w": 0,
"nominal_total_peakpower_kw": null,
"diagnostics_days": 7,
"archive_retention_days": 60,
"nowcast_enabled": true,
"trend_enabled": true,
"solar_response_enabled": true,
"weather_response_enabled": true,
"correction_mode": "auto",
"validation_days": 7,
"min_validation_days": 3,
"min_validation_samples": 32,
"min_validation_improvement": 0.02,
"training_delay_minutes": 15,
"total_measurement_key": null,
"total_curtailment_sensitive": false,
"total_curtailment_key": null,
"total_learning_guard_key": null,
"total_learning_guard_max": null,
"total_learning_guard_max_gap_seconds": 600
},
"pvforecastimport": {
"import_file_path": null,
"import_json": null
@@ -93,6 +144,16 @@
},
"planes": [
{
"name": null,
"measurement_key": null,
"nowcast_reference": false,
"curtailment_sensitive": false,
"parameters_estimated": false,
"correction_group": null,
"curtailment_key": null,
"learning_guard_key": null,
"learning_guard_max": null,
"learning_guard_max_gap_seconds": 600,
"surface_tilt": 10.0,
"surface_azimuth": 180.0,
"userhorizon": [
@@ -115,6 +176,16 @@
"strings_per_inverter": 2
},
{
"name": null,
"measurement_key": null,
"nowcast_reference": false,
"curtailment_sensitive": false,
"parameters_estimated": false,
"correction_group": null,
"curtailment_key": null,
"learning_guard_key": null,
"learning_guard_max": null,
"learning_guard_max_gap_seconds": 600,
"surface_tilt": 20.0,
"surface_azimuth": 90.0,
"userhorizon": [
@@ -177,6 +248,56 @@
"calibration_min_factor": 0.5,
"calibration_max_factor": 1.5
},
"adaptive": {
"backend": "local",
"resolution_minutes": 15,
"forecast_days": null,
"past_days": null,
"weather_models": [
"best_match",
"icon_d2"
],
"transposition_model": "perez",
"albedo": 0.25,
"inverter_efficiency": 0.96,
"temperature_coefficient": -0.36,
"apply_iam": true,
"shift_to_interval_start": true,
"calibration_enabled": false,
"calibration_days": 30,
"calibration_reference_days": 30,
"calibration_outage_filter_enabled": true,
"calibration_outage_threshold": 0.55,
"calibration_min_healthy_days": 3,
"calibration_azimuth_bin_degrees": 45,
"calibration_prior_kwh": 5.0,
"calibration_min_factor": 0.5,
"calibration_max_factor": 1.5,
"regional_weather_model": "icon_d2",
"regional_horizon_hours": 6,
"learning_days": 21,
"min_training_days": 5,
"max_ac_power_w": 0,
"nominal_total_peakpower_kw": null,
"diagnostics_days": 7,
"archive_retention_days": 60,
"nowcast_enabled": true,
"trend_enabled": true,
"solar_response_enabled": true,
"weather_response_enabled": true,
"correction_mode": "auto",
"validation_days": 7,
"min_validation_days": 3,
"min_validation_samples": 32,
"min_validation_improvement": 0.02,
"training_delay_minutes": 15,
"total_measurement_key": null,
"total_curtailment_sensitive": false,
"total_curtailment_key": null,
"total_learning_guard_key": null,
"total_learning_guard_max": null,
"total_learning_guard_max_gap_seconds": 600
},
"pvforecastimport": {
"import_file_path": null,
"import_json": null
@@ -209,6 +330,16 @@
},
"planes": [
{
"name": null,
"measurement_key": null,
"nowcast_reference": false,
"curtailment_sensitive": false,
"parameters_estimated": false,
"correction_group": null,
"curtailment_key": null,
"learning_guard_key": null,
"learning_guard_max": null,
"learning_guard_max_gap_seconds": 600,
"surface_tilt": 10.0,
"surface_azimuth": 180.0,
"userhorizon": [
@@ -231,6 +362,16 @@
"strings_per_inverter": 2
},
{
"name": null,
"measurement_key": null,
"nowcast_reference": false,
"curtailment_sensitive": false,
"parameters_estimated": false,
"correction_group": null,
"curtailment_key": null,
"learning_guard_key": null,
"learning_guard_max": null,
"learning_guard_max_gap_seconds": 600,
"surface_tilt": 20.0,
"surface_azimuth": 90.0,
"userhorizon": [
@@ -256,6 +397,7 @@
"max_planes": 1,
"providers": [
"PVForecastAkkudoktor",
"PVForecastAdaptiveAkkudoktor",
"PVForecastForecastSolar",
"PVForecastHomeAssistant",
"PVForecastImport",
@@ -459,14 +601,24 @@
| Name | Type | Read-Only | Default | Description |
| ---- | ---- | --------- | ------- | ----------- |
| albedo | `Optional[float]` | `rw` | `0.2` | Proportion of the light hitting the ground that it reflects back. |
| correction_group | `Optional[str]` | `rw` | `None` | Optional group of planes sharing short-term validation. Unset independent references share one group; other planes are separate. |
| curtailment_key | `Optional[str]` | `rw` | `None` | Optional measured flag: 0 explicitly unrestricted, 1 curtailed. Missing or stale flags never authorize learning a curtailment-sensitive plane. |
| curtailment_sensitive | `bool` | `rw` | `False` | Battery-full curtailment possible; exclude from unrestricted weather learning. |
| inverter_model | `Optional[str]` | `rw` | `None` | Model of the inverter of this plane. |
| inverter_paco | `Optional[int]` | `rw` | `None` | AC power rating of the inverter [W]. |
| learning_guard_key | `Optional[str]` | `rw` | `None` | Optional site-specific positive learning guard, e.g. battery SoC, as an alternative to a binary curtailment flag. |
| learning_guard_max | `Optional[float]` | `rw` | `None` | Learn only while every supported guard value is below this bound. This is an explicit site assumption, not automatic curtailment detection. |
| learning_guard_max_gap_seconds | `int` | `rw` | `600` | None |
| loss | `Optional[float]` | `rw` | `14.0` | Sum of PV system losses in percent |
| measurement_key | `Optional[str]` | `rw` | `None` | Matching EOS power measurement key for adaptive learning. |
| module_model | `Optional[str]` | `rw` | `None` | Model of the PV modules of this plane. |
| modules_per_string | `Optional[int]` | `rw` | `None` | Number of the PV modules of the strings of this plane. |
| mountingplace | `Optional[str]` | `rw` | `building` | Type of mounting for PV system. Options are 'free' for free-standing and 'building' for building-integrated. |
| name | `Optional[str]` | `rw` | `None` | Readable PV string name. |
| nowcast_reference | `bool` | `rw` | `False` | Use this independent array as a local weather reference. |
| optimal_surface_tilt | `Optional[bool]` | `rw` | `False` | Calculate the optimum tilt angle. Ignored for two-axis tracking. |
| optimalangles | `Optional[bool]` | `rw` | `False` | Calculate the optimum tilt and azimuth angles. Ignored for two-axis tracking. |
| parameters_estimated | `bool` | `rw` | `False` | Geometry and peak power are effective fitted model estimates, not verified nameplate data. |
| peakpower | `Optional[float]` | `rw` | `None` | Nominal power of PV system in kW. |
| pvtechchoice | `Optional[str]` | `rw` | `crystSi` | PV technology. One of 'crystSi', 'CIS', 'CdTe', 'Unknown'. |
| strings_per_inverter | `Optional[int]` | `rw` | `None` | Number of the strings of the inverter of this plane. |
@@ -487,6 +639,16 @@
"pvforecast": {
"planes": [
{
"name": null,
"measurement_key": null,
"nowcast_reference": false,
"curtailment_sensitive": false,
"parameters_estimated": false,
"correction_group": null,
"curtailment_key": null,
"learning_guard_key": null,
"learning_guard_max": null,
"learning_guard_max_gap_seconds": 600,
"surface_tilt": 10.0,
"surface_azimuth": 180.0,
"userhorizon": [
@@ -654,3 +816,123 @@
}
```
<!-- pyml enable line-length -->
### Automatic defaults; configure measurements and reference flags on each plane
<!-- pyml disable line-length -->
:::{table} pvforecast::adaptive
:widths: 10 10 5 5 30
:align: left
| Name | Type | Read-Only | Default | Description |
| ---- | ---- | --------- | ------- | ----------- |
| albedo | `float` | `rw` | `0.25` | Ground albedo used for planes that do not set their own. |
| apply_iam | `bool` | `rw` | `True` | Apply the ASHRAE incidence-angle modifier to the beam component. |
| archive_retention_days | `int` | `rw` | `60` | None |
| backend | `Literal['remote', 'local']` | `rw` | `local` | None |
| calibration_azimuth_bin_degrees | `int` | `rw` | `45` | Width of the solar-azimuth bins for the correction. 0 fits a single global factor only. |
| calibration_days | `int` | `rw` | `30` | Length of the measurement window used to fit the correction. |
| calibration_enabled | `bool` | `rw` | `False` | None |
| calibration_max_factor | `float` | `rw` | `1.5` | Upper clamp on any fitted correction factor. |
| calibration_min_factor | `float` | `rw` | `0.5` | Lower clamp on any fitted correction factor. |
| calibration_min_healthy_days | `int` | `rw` | `3` | Minimum number of healthy days used for a fit. Older healthy days from the reference window are added when the recent window contains fewer. |
| calibration_outage_filter_enabled | `bool` | `rw` | `True` | Exclude days whose measured production is far below the recent healthy plant level. This prevents inverter, battery and curtailment events from being learned as permanent PV model losses. |
| calibration_outage_threshold | `float` | `rw` | `0.55` | A day is treated as unavailable when its measured/modelled energy ratio is below this fraction of the robust healthy reference ratio. |
| calibration_prior_kwh | `float` | `rw` | `5.0` | Shrinkage strength: a bin needs this much modelled energy before its own factor outweighs the global one. Higher is more conservative. |
| calibration_reference_days | `int` | `rw` | `30` | Lookback used to distinguish healthy production from outages or curtailment. If the calibration window contains too few healthy days, the most recent healthy days from this reference window are used. |
| correction_mode | `Literal['auto', 'calibrated', 'local', 'trend']` | `rw` | `auto` | Choose validated short-term candidates per group/lead, or explicitly fix a correction stage. |
| diagnostics_days | `int` | `rw` | `7` | None |
| forecast_days | `Optional[int]` | `rw` | `None` | Forecast horizon in days (1-16). Leave empty to derive it from `prediction.hours`, which is what keeps the optimizer's tail horizon fed. |
| inverter_efficiency | `float` | `rw` | `0.96` | Nominal inverter efficiency (PVWatts eta_inv_nom). |
| learning_days | `int` | `rw` | `21` | None |
| max_ac_power_w | `float` | `rw` | `0` | None |
| min_training_days | `int` | `rw` | `5` | None |
| min_validation_days | `int` | `rw` | `3` | None |
| min_validation_improvement | `float` | `rw` | `0.02` | None |
| min_validation_samples | `int` | `rw` | `32` | None |
| nominal_total_peakpower_kw | `Optional[float]` | `rw` | `None` | Known installed nameplate total; fitted plane powers may be effective estimates. |
| nowcast_enabled | `bool` | `rw` | `True` | None |
| past_days | `Optional[int]` | `rw` | `None` | Days of past data to request (0-92). Leave empty to derive it from `prediction.historic_hours`. |
| regional_horizon_hours | `float` | `rw` | `6` | None |
| regional_weather_model | `Optional[str]` | `rw` | `icon_d2` | None |
| resolution_minutes | `int` | `rw` | `15` | Forecast resolution in minutes. 15 requests Open-Meteo's `minutely_15` block (natively resolved over Central Europe and North America, interpolated from hourly elsewhere); 60 requests the `hourly` block. |
| shift_to_interval_start | `bool` | `rw` | `True` | Open-Meteo stamps an interval mean with the interval END. EOS labels an interval by its START, so records are shifted back by one interval. Disable only to compare like-for-like against a provider that does not. |
| solar_response_enabled | `bool` | `rw` | `True` | Learn empirical angular response relative to separate, eligible reference arrays; activate only after issued-candidate validation. |
| temperature_coefficient | `float` | `rw` | `-0.36` | Module power temperature coefficient in %/degC (negative). Matches the `cellCoEff` the akkudoktor.net forecast uses. |
| total_curtailment_key | `Optional[str]` | `rw` | `None` | None |
| total_curtailment_sensitive | `bool` | `rw` | `False` | None |
| total_learning_guard_key | `Optional[str]` | `rw` | `None` | None |
| total_learning_guard_max | `Optional[float]` | `rw` | `None` | None |
| total_learning_guard_max_gap_seconds | `int` | `rw` | `600` | None |
| total_measurement_key | `Optional[str]` | `rw` | `None` | Optional whole-plant power channel when individual plane measurements are unavailable. |
| training_delay_minutes | `int` | `rw` | `15` | None |
| transposition_model | `str` | `rw` | `perez` | pvlib sky-diffuse transposition model: isotropic, klucher, haydavies, reindl, king or perez. |
| trend_enabled | `bool` | `rw` | `True` | None |
| validation_days | `int` | `rw` | `7` | None |
| weather_models | `list[str]` | `rw` | `['best_match']` | Open-Meteo weather models to request. Listing more than one turns the input into a poor-man's ensemble: the members are averaged per variable, which is the cheapest reliable way to cut irradiance forecast error. Costs no extra API calls. |
| weather_response_enabled | `bool` | `rw` | `True` | Archive received model weather and learn horizon/regime corrections; activate only after issued-candidate validation. |
:::
<!-- pyml enable line-length -->
<!-- pyml disable no-emphasis-as-heading -->
**Example Input/Output**
<!-- pyml enable no-emphasis-as-heading -->
<!-- pyml disable line-length -->
```json
{
"pvforecast": {
"adaptive": {
"backend": "local",
"resolution_minutes": 15,
"forecast_days": null,
"past_days": null,
"weather_models": [
"best_match",
"icon_d2"
],
"transposition_model": "perez",
"albedo": 0.25,
"inverter_efficiency": 0.96,
"temperature_coefficient": -0.36,
"apply_iam": true,
"shift_to_interval_start": true,
"calibration_enabled": false,
"calibration_days": 30,
"calibration_reference_days": 30,
"calibration_outage_filter_enabled": true,
"calibration_outage_threshold": 0.55,
"calibration_min_healthy_days": 3,
"calibration_azimuth_bin_degrees": 45,
"calibration_prior_kwh": 5.0,
"calibration_min_factor": 0.5,
"calibration_max_factor": 1.5,
"regional_weather_model": "icon_d2",
"regional_horizon_hours": 6.0,
"learning_days": 21,
"min_training_days": 5,
"max_ac_power_w": 0.0,
"nominal_total_peakpower_kw": null,
"diagnostics_days": 7,
"archive_retention_days": 60,
"nowcast_enabled": true,
"trend_enabled": true,
"solar_response_enabled": true,
"weather_response_enabled": true,
"correction_mode": "auto",
"validation_days": 7,
"min_validation_days": 3,
"min_validation_samples": 32,
"min_validation_improvement": 0.02,
"training_delay_minutes": 15,
"total_measurement_key": null,
"total_curtailment_sensitive": false,
"total_curtailment_key": null,
"total_learning_guard_key": null,
"total_learning_guard_max": null,
"total_learning_guard_max_gap_seconds": 600
}
}
}
```
<!-- pyml enable line-length -->
docs/_generated/openapi.md
+112 -1
View File
@@ -1,6 +1,6 @@
# Akkudoktor-EOS
**Version**: `v0.3.0.dev2610301534325811`
**Version**: `v0.3.0.dev2611011668106370`
<!-- pyml disable line-length -->
**Description**: This project provides a comprehensive solution for simulating and optimizing an energy system based on renewable energy sources. With a focus on photovoltaic (PV) systems, battery storage (batteries), load management (consumer requirements), heat pumps, electric vehicles, and consideration of electricity price data, this system enables forecasting and optimization of energy flow and costs over a specified period.
@@ -1699,6 +1699,117 @@ Args:
---
## GET /v1/prediction/pv-adaptive/comparisons
<!-- pyml disable line-length -->
**Links**: [local](http://localhost:8503/docs#/default/comparisons_v1_prediction_pv-adaptive_comparisons_get), [eos](https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/Akkudoktor-EOS/EOS/refs/heads/main/openapi.json#/default/comparisons_v1_prediction_pv-adaptive_comparisons_get)
<!-- pyml enable line-length -->
Comparisons
<!-- pyml disable line-length -->
```python
"""
Export genuinely issued forecasts with later measured truth, issuance and lead.
"""
```
<!-- pyml enable line-length -->
**Parameters**:
- `days` (query, optional): No description provided.
- `limit` (query, optional): No description provided.
- `since_ms` (query, optional): No description provided.
**Responses**:
- **200**: Successful Response
- **422**: Validation Error
---
## GET /v1/prediction/pv-adaptive/latest
<!-- pyml disable line-length -->
**Links**: [local](http://localhost:8503/docs#/default/latest_v1_prediction_pv-adaptive_latest_get), [eos](https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/Akkudoktor-EOS/EOS/refs/heads/main/openapi.json#/default/latest_v1_prediction_pv-adaptive_latest_get)
<!-- pyml enable line-length -->
Latest
<!-- pyml disable line-length -->
```python
"""
Latest complete output curve; validation uses the separate immutable archive.
"""
```
<!-- pyml enable line-length -->
**Responses**:
- **200**: Successful Response
---
## PUT /v1/prediction/pv-adaptive/samples
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**Links**: [local](http://localhost:8503/docs#/default/samples_v1_prediction_pv-adaptive_samples_put), [eos](https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/Akkudoktor-EOS/EOS/refs/heads/main/openapi.json#/default/samples_v1_prediction_pv-adaptive_samples_put)
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Samples
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```python
"""
Store original power readings in the bounded adaptive archive, without zero filling.
"""
```
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**Request Body**:
- `application/json`: {
"items": {
"$ref": "#/components/schemas/MeasurementSample"
},
"type": "array",
"maxItems": 10000,
"title": "Values"
}
**Responses**:
- **200**: Successful Response
- **422**: Validation Error
---
## GET /v1/prediction/pv-adaptive/status
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**Links**: [local](http://localhost:8503/docs#/default/status_v1_prediction_pv-adaptive_status_get), [eos](https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/Akkudoktor-EOS/EOS/refs/heads/main/openapi.json#/default/status_v1_prediction_pv-adaptive_status_get)
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Status
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```python
"""
Status contains honest cold-start counts and matched per-lead error metrics.
"""
```
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**Responses**:
- **200**: Successful Response
---
## GET /v1/prediction/pvforecast/pvlib/inverters
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