aiControl aiConActivate=1 aiConProfile=v1_heatpump_pv aiConHiddenLayers=64-32Information about the neural network for consumption forecasting
last AI training: 2026-05-11 02:01:56 / Runtime in seconds: 1361
AI query status: ok
last AI result generation time: 42.23 ms
Alpha: 1
Consumer number Heat pump: 01
=== Model Parameters ===
Standardization Limits: PV=16687 Wh, Household Consumption: Min=0 Wh / Max=14430 Wh
Training Data: 2896 Data Records (Training=2316, Validation=580)
Architecture: Inputs=94, Hidden Layers=64-32, Outputs=1
Hyperparameters: Learning Rate=0.005, Momentum=0.5, BitFail-Limit=0.35
Activations: Hidden=SIGMOID, Steepness=0.9, Output=LINEAR
Training Algorithm: INCREMENTAL, Registry Version=v1_heatpump_pv
Random Generator: Mode=2, Period=10
Model Age: 7 h
=== Training Metrics ===
best model at Epoche: 3249 (max. 15000)
Training MSE: 0.000533
Validation MSE: 0.001664
Validation MSE Average: 0.001864
Validation MSE Standard Deviation: 0.000146
Validation Bit_Fail: 1
Model Bias: 364 Wh
Model Slope: 0.7
Training evaluation: Retrain
=== Forecast Error Measures ===
MAE: 322.41 Wh
MedAE: 213.62 Wh
RMSE: 363.79 Wh
RMSE relative: 52 %
RMSE Rating: acceptable
MAPE: 43.21 %
MdAPE: 22.46 %
R²: 0.77
=== Noise ===
Noise Rating: borderline
Recommendation for Bit_Fail: 0.34 (Setting of aiControl->aiConBitFailLimit)
=== Drift Indicators ===
Drift Score: -
Drift RMSE ratio: -
Drift Slope: 1
Drift Bias: 0
Drift Bias Live: -
Drift Index: -
Drift Rating: fresh_model
Slope recalibrated: 0.7
Bias recalibrated: 364
last recalibration: -###############################################################################
#
# 73_DepartureBnT.pm
# Departure Bus and Train
# Reads the departure data from transport.stefan-biermann.de for a given
# station
# Written and best viewed with Notepad++; Language Markup: Perl; Tabstop: 4
#
# Copyright (c) 2026
# Author : Matthias Deeke
# e-mail : matthias.deeke(AT)deeke(PUNKT)eu
# Fhem commandref : https://fhem.de/commandref_DE.html#DepartureBnT
# Fhem Forum : https://forum.fhem.de/index.php?topic=143906.0
# Fhem Wiki : Not implemented
#
# This file is part of fhem.
#
# Fhem is free software: You can redistribute it and/or modify it under the
# terms of the GNU General Public License as published by the Free Software
# Foundation, either version 2 of the License, or any later version.
#
# Statement on Authorship and AI Usage:
# 1. The contributor warrants that the provided code represents a
# substantial creative work of their own authorship.
# 2. AI-generated portions have been significantly rewritten, extended,
# and/or modified by the contributor to ensure creative control and
# copyright responsibility.
# 3. Unaltered or only marginally modified AI-generated code fragments
# are explicitly labeled at the relevant sections within the source code
# via comments such as:
# "Begin - AI-generated segment - source: [AI NAME] - Begin"
# "End - AI-generated segment - source: [AI NAME] - End"
#
# Fhem is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with fhem. If not, see <http://www.gnu.org/licenses/>.
#
# Example:
# define myBusStation DepartureBnT
#
########################################################################################################################
Informationen zum neuronalen Netz der Verbrauchsvorhersage
letztes KI-Training: 09.05.2026 05:23:56 / Laufzeit in Sekunden: 7389
KI Abfragestatus: ok
letzte KI-Ergebnis Generierungsdauer: 132.96 ms
Alpha: 0.7
Verbrauchernummer Wärmepumpe: -
=== Modellparameter ===
Normierungsgrenzen: PV=11000 Wh, Hausverbrauch: Min=0 Wh / Max=5866 Wh
Trainingsdaten: 10188 Datensätze (Training=8150, Validation=2038)
Architektur: Inputs=69, Hidden Layers=50-25, Outputs=1
Hyperparameter: Learning Rate=0.005, Momentum=0.5, BitFail-Limit=0.34
Aktivierungen: Hidden=SIGMOID, Steepness=1.1, Output=LINEAR
Trainingsalgorithmus: INCREMENTAL, Registry Version=v1_common_active_pv
Zufallsgenerator: Mode=2, Period=10
Modellalter: 50 h
=== Trainingsmetriken ===
bestes Modell bei Epoche: 76 (max. 15000)
Training MSE: 0.000458
Validation MSE: 0.000981
Validation MSE Average: 0.001226
Validation MSE Standard Deviation: 0.000048
Validation Bit_Fail: 2
Model Bias: 96 Wh
Model Slope: 0.8
Trainingsbewertung: Borderline
=== Fehlermaße der Prognosen ===
MAE: 80.63 Wh
MedAE: 34.81 Wh
RMSE: 96.94 Wh
RMSE relative: 23 %
RMSE Rating: good
MAPE: 14.80 %
MdAPE: 7.97 %
R²: 0.85
=== Rauschen ===
Rauschen Bewertung: low
Empfehlung für Bit_Fail: 0.28 (Einstellung von aiControl->aiConBitFailLimit)
=== Drift-Kennzahlen ===
Drift Score: 3.95
Drift RMSE ratio: 6.87
Drift Slope: 0.093
Drift Bias: 448.84
Drift Bias Live: 545.01
Drift Index: 2.90
Drift Bewertung: recalibration blocked: rmse_anomaly
Slope recalibrated: 0.8
Bias recalibrated: 96
letzte Rekalibrierung: -Zitat von: Sebastian84 am 11 Mai 2026, 08:40:23Hab meinen Fehler bemerkt....es gäbe da schon noch ein paar andere Kleinigkeiten...


2026.05.11 08:00:19 1: Forecast DEBUG> DRIFT [con]: Flag=moderate | Block=rmse_anomaly | SlopeLive=0.851 | DriftSlope=0.850 | BiasLive=603.91 | DriftBias=610.53 | RMSErelLive=127.8 | RMSErelRatio=31.95 | BiasVarNorm=2.28 | DriftIndex=2.33 | DriftScore=15.77 | Zone3Hours=10 | Zone3Reset=0 | Hist=[moderate,moderate,moderate,moderate,moderate,moderate,moderate,moderate,moderate,moderate]
Zitat von: rabehd am 07 Mai 2026, 13:19:09Das wird nichts helfen, wir reden hier ja über den DNS-Ausfall der de-Adressen am Dienstag.Zitat2026.05.05 23:57:20.010 1: PERL WARNING: Use of uninitialized value $StationID in concatenation (.) or string at ./FHEM/73_DepartureBnT.pm line 832, <$fh> line 4737.
2026.05.05 23:57:20.010 3: SBahn : DepartureBnT_UpdateStationDetails - StationID does not exist.
2026.05.05 23:57:20.010 1: PERL WARNING: Use of uninitialized value $StationID in concatenation (.) or string at ./FHEM/73_DepartureBnT.pm line 570, <$fh> line 4737.
2026.05.05 23:57:20.010 3: SBahn : DepartureBnT_Download - StationID does not exist.
2026.05.05 23:57:20.010 1: SBahn : DepartureBnT_Attr - ShowDetails : Departure
2026.05.05 23:57:20.010 1: PERL WARNING: Use of uninitialized value $StationID in concatenation (.) or string at ./FHEM/73_DepartureBnT.pm line 570, <$fh> line 4741.
2026.05.05 23:57:20.010 3: SBahn : DepartureBnT_Download - StationID does not exist.
2026.05.05 23:57:20.093 1: PERL WARNING: Use of uninitialized value $data in concatenation (.) or string at ./FHEM/73_DepartureBnT.pm line 871, <$fh> line 4742.
ZitatKann ich irgendwie herausfinden ob die Datenbank selbst blockiert, oder nur etwas in perl/fhem?Du kannst z.B. mit dem CLI versuchen einen Datensatz einzufügen und zu löschen, einen bestehenden löschen oder updaten.
ZitatKönnen die doppelten Schreibvorgänge der Caches und die dadurch fehlenden Daten ein Indiz sein wo etwas schief läuft?Ich weiß nicht wirklich was du meinst, aber wenn es das sein sollte:
2026-05-09_14:55:06 logdb lastCachefile: ./log/cache_logdb_2025-05-18_07-14-14 (0 cache rows exported)
2026-05-09_14:55:06 logdb CacheUsage: 1
2026-05-09_14:55:06 logdb CacheUsage: 0
2026-05-09_14:55:06 logdb CacheUsage: 1
2026-05-09_14:55:06 logdb Cache exported to "lastCachefile" due to Cache overflow
2026-05-09_14:55:06 logdb CacheUsage: 2
2026-05-09_14:55:06 logdb Another operation is in progress - resync at NextSync
2026-05-09_14:55:06 logdb CacheUsage: 3
2026-05-09_14:56:06 logdb CacheOverflowLastNum: 0
2026-05-09_14:56:06 logdb CacheOverflowLastState: normal
2026-05-09_14:56:06 logdb CacheUsage: 1069
2026-05-09_15:17:08 logdb CacheOverflowLastNum: 645
2026-05-09_15:17:08 logdb CacheOverflowLastState: exceeded