76_SolarForecast - Informationen/Ideen zu Weiterentwicklung und Support

DS_Starter · 11 Januar 2026, 22:21:49

Ich habe an den AI Semantiken weitergearbeitet, die sich in den Profilen

v1_common_active_pv
v1_heatpump_active_pv

positiv auswirken sollten.

Das contrib ist upgedated.

stefanru · 12 Januar 2026, 02:54:39

Irgendwie hat es mir jetzt nach dem Update meine Vorhersage Grafik zerhagelt.
Oder liegt das am laufenden Training?
aiControl aiConActivate=1 aiConProfile=v1_heatpump_active_pv

flowGraphicControl size=475 animate=1 showconsumer=1 showconsumerdummy=1 showconsumerpower=1 showconsumerremaintime=0
graphicBeam1Color 3C14FF
graphicBeam1Content pvForecast
graphicBeam2Color 19FF29
graphicBeam2Content pvReal
graphicBeam3Content consumptionForecast
graphicBeam4Color FF1F78
graphicBeam4Content consumption
graphicControl hourCount=16 headerDetail=all layoutType=double beamHeightlevel=1:100,2:100
graphicHistoryHour 8
graphicSelect both
graphicShowNight 0

Ach so zur Grafik es ist kurz vor 03:00.

Danke und Gruß,
Stefan

DS_Starter · 12 Januar 2026, 08:23:52

Moin Stefan,

nein, gibt keine Einflußnahme auf die Grafik und läuft auch alles einwandfrei.
Deine Grafik sieht mir eher nach Browser/JS -Thema aus.

Grüße,
Heiko

TheTrumpeter · 12 Januar 2026, 08:50:19

Zitat von: 300P am 09 Januar 2026, 16:16:26
Zitat von: TheTrumpeter am 09 Januar 2026, 13:00:31Hab' das grad durchgeschaut, finde aber nirgends einen Hinweis was/wie ich die WP konfigurieren/bekanntmachen muss.

Zusatzinstallierung notwendig:
Zuerst versuch es mal so in der Reihenfolge - bei meinem RPI4 hat es so geklappt.

Code Auswählen Erweitern
sudo su apt-get update && apt-get upgrade sudo apt-get install gcc sudo apt-get install libfann-dev sudo cpan AI::FANN
Bei mir lief es dann so sauber durch.

Dann Contrib - Version laden / 1 x restart FHEM

Dies (o.ä.) zum "attr Forecast aiControl" hinzufügen
Code Auswählen Erweitern
aiConActivate=1\ aiConAlpha=1\ aiConTrainStart=1:2\ aiConActFunc=SIGMOID\ aiConHiddenLayers=80-40-20\ aiConLearnRate=0.005\ aiConMomentum=0.8\ aiConShuffleMode=2\ aiConSteepness=0.5\ aiConTrainAlgo=INCREMENTAL
Jetzt noch bei "attr Forecast ctrlDebug"
Code Auswählen Erweitern
aiProcessanklicken damit du was siehst.

und dann hiermit deine Wärmepumpe (ab V2.0.0.) an SF bekanntgeben

EDIT:
Ach ganz vergessen - dann must du erstmalig ein langandauerndes AI-Training anwerfen ehe du etwas siehst...
Code Auswählen Erweitern
set <SF> aiDecTree runConTrain

Super Danke...

Installiert, parametriert, und läuft mal...

Code Auswählen

2026.01.12 08:48:14 1: mySolarForecast DEBUG> AI FANN Training for Consumption Forecast BlockingCall PID "18554" with Timeout 86400 s started
2026.01.12 08:48:15 1: mySolarForecast DEBUG> AI FANN - There are 3837 Records skipped due to incomplete or invalid data. Index: 
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2025020317, 2025020408, 2025020409, 2025033003, 2025041613, 2025042314, 2025042618, 2025051610, 2025051620, 2025061211, 2025062615, 2025062714, 2025072409, 2025073009, 2025073010, 2025073011, 2025073012, 2025090814, 2025112810
2026.01.12 08:48:15 1: mySolarForecast DEBUG> AI FANN - Target-Norm: raw_max=5012, p99=3569, p99.5=4923, targmaxval=6400
2026.01.12 08:48:15 1: mySolarForecast DEBUG> AI FANN - True Outliers above p99.5 (4923): 5012
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8003]: lag1=0.589 hppf=0.203 lag24=0.345 d1p=0.000 d1n=0.328 rollstd=0.552 up=0 down=1 upS=0.000 downS=0.328 vol=1 pvX=0 break=1
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8003]: tmplag1=0.463 tmplag3=0.450 tmplag24=0.475 tmpd1p=0.012 tmpd1n=0.000 tmpd3p=0.025 tmpd3n=0.000 tmpTrdp=0.019 tmpTrdn=0.000
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8004]: lag1=0.391 hppf=0.203 lag24=0.320 d1p=0.039 d1n=0.000 rollstd=0.463 up=1 down=0 upS=0.039 downS=0.000 vol=1 pvX=0 break=1
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8004]: tmplag1=0.475 tmplag3=0.450 tmplag24=0.475 tmpd1p=0.000 tmpd1n=0.000 tmpd3p=0.025 tmpd3n=0.000 tmpTrdp=0.012 tmpTrdn=0.000
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8005]: lag1=0.418 hppf=0.203 lag24=0.320 d1p=0.000 d1n=0.142 rollstd=0.297 up=0 down=1 upS=0.000 downS=0.142 vol=1 pvX=0 break=1
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8005]: tmplag1=0.475 tmplag3=0.463 tmplag24=0.463 tmpd1p=0.000 tmpd1n=0.000 tmpd3p=0.012 tmpd3n=0.000 tmpTrdp=0.006 tmpTrdn=0.000
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8006]: lag1=0.332 hppf=0.203 lag24=0.318 d1p=0.000 d1n=0.005 rollstd=0.291 up=0 down=1 upS=0.000 downS=0.005 vol=1 pvX=0 break=0
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8006]: tmplag1=0.475 tmplag3=0.475 tmplag24=0.450 tmpd1p=0.000 tmpd1n=0.000 tmpd3p=0.000 tmpd3n=0.000 tmpTrdp=0.000 tmpTrdn=0.000
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8007]: lag1=0.328 hppf=0.217 lag24=0.310 d1p=0.000 d1n=0.034 rollstd=0.285 up=0 down=1 upS=0.000 downS=0.034 vol=1 pvX=0 break=0
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8007]: tmplag1=0.475 tmplag3=0.475 tmplag24=0.450 tmpd1p=0.000 tmpd1n=0.012 tmpd3p=0.000 tmpd3n=0.012 tmpTrdp=0.000 tmpTrdn=0.012
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8008]: lag1=0.308 hppf=0.231 lag24=0.334 d1p=0.000 d1n=0.267 rollstd=0.307 up=0 down=1 upS=0.000 downS=0.267 vol=1 pvX=0 break=0
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8008]: tmplag1=0.463 tmplag3=0.475 tmplag24=0.438 tmpd1p=0.000 tmpd1n=0.013 tmpd3p=0.000 tmpd3n=0.025 tmpTrdp=0.000 tmpTrdn=0.019
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8009]: lag1=0.146 hppf=0.231 lag24=0.249 d1p=0.000 d1n=0.061 rollstd=0.280 up=0 down=1 upS=0.000 downS=0.061 vol=1 pvX=0 break=0
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8009]: tmplag1=0.450 tmplag3=0.475 tmplag24=0.438 tmpd1p=0.000 tmpd1n=0.000 tmpd3p=0.000 tmpd3n=0.025 tmpTrdp=0.000 tmpTrdn=0.012
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8010]: lag1=0.109 hppf=0.231 lag24=0.053 d1p=0.000 d1n=0.019 rollstd=0.353 up=0 down=1 upS=0.000 downS=0.019 vol=1 pvX=0 break=0
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8010]: tmplag1=0.450 tmplag3=0.463 tmplag24=0.425 tmpd1p=0.000 tmpd1n=0.000 tmpd3p=0.000 tmpd3n=0.013 tmpTrdp=0.000 tmpTrdn=0.006
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8011]: lag1=0.098 hppf=0.231 lag24=0.062 d1p=0.017 d1n=0.000 rollstd=0.335 up=1 down=0 upS=0.017 downS=0.000 vol=1 pvX=0 break=0
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8011]: tmplag1=0.450 tmplag3=0.450 tmplag24=0.425 tmpd1p=0.000 tmpd1n=0.000 tmpd3p=0.000 tmpd3n=0.000 tmpTrdp=0.000 tmpTrdn=0.000
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8012]: lag1=0.109 hppf=0.245 lag24=0.088 d1p=0.000 d1n=0.037 rollstd=0.313 up=0 down=1 upS=0.000 downS=0.037 vol=1 pvX=0 break=0
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8012]: tmplag1=0.450 tmplag3=0.450 tmplag24=0.425 tmpd1p=0.000 tmpd1n=0.013 tmpd3p=0.000 tmpd3n=0.013 tmpTrdp=0.000 tmpTrdn=0.013
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8013]: lag1=0.087 hppf=0.245 lag24=0.082 d1p=0.000 d1n=0.086 rollstd=0.245 up=0 down=1 upS=0.000 downS=0.086 vol=1 pvX=0 break=0
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8013]: tmplag1=0.438 tmplag3=0.450 tmplag24=0.412 tmpd1p=0.000 tmpd1n=0.000 tmpd3p=0.000 tmpd3n=0.013 tmpTrdp=0.000 tmpTrdn=0.006
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8014]: lag1=0.035 hppf=0.260 lag24=0.081 d1p=0.000 d1n=0.009 rollstd=0.107 up=0 down=1 upS=0.000 downS=0.009 vol=1 pvX=0 break=0
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8014]: tmplag1=0.438 tmplag3=0.450 tmplag24=0.412 tmpd1p=0.000 tmpd1n=0.013 tmpd3p=0.000 tmpd3n=0.025 tmpTrdp=0.000 tmpTrdn=0.019
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8015]: lag1=0.029 hppf=0.090 lag24=0.084 d1p=0.006 d1n=0.000 rollstd=0.107 up=1 down=0 upS=0.006 downS=0.000 vol=1 pvX=0 break=0
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8015]: tmplag1=0.425 tmplag3=0.438 tmplag24=0.600 tmpd1p=0.175 tmpd1n=0.000 tmpd3p=0.162 tmpd3n=0.000 tmpTrdp=0.169 tmpTrdn=0.000
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8016]: lag1=0.033 hppf=0.275 lag24=0.088 d1p=0.000 d1n=0.002 rollstd=0.107 up=0 down=0 upS=0.000 downS=0.002 vol=1 pvX=0 break=0
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8016]: tmplag1=0.600 tmplag3=0.438 tmplag24=0.425 tmpd1p=0.000 tmpd1n=0.188 tmpd3p=0.000 tmpd3n=0.025 tmpTrdp=0.000 tmpTrdn=0.106
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8017]: lag1=0.032 hppf=0.275 lag24=0.087 d1p=0.000 d1n=0.004 rollstd=0.102 up=0 down=0 upS=0.000 downS=0.004 vol=1 pvX=0 break=0
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8017]: tmplag1=0.412 tmplag3=0.425 tmplag24=0.412 tmpd1p=0.000 tmpd1n=0.000 tmpd3p=0.000 tmpd3n=0.013 tmpTrdp=0.000 tmpTrdn=0.006
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8018]: lag1=0.030 hppf=0.275 lag24=0.083 d1p=0.000 d1n=0.000 rollstd=0.066 up=0 down=0 upS=0.000 downS=0.000 vol=1 pvX=0 break=0
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8018]: tmplag1=0.412 tmplag3=0.600 tmplag24=0.412 tmpd1p=0.000 tmpd1n=0.000 tmpd3p=0.000 tmpd3n=0.188 tmpTrdp=0.000 tmpTrdn=0.094
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8019]: lag1=0.030 hppf=0.290 lag24=0.081 d1p=0.045 d1n=0.000 rollstd=0.005 up=1 down=0 upS=0.045 downS=0.000 vol=0 pvX=0 break=0
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8019]: tmplag1=0.412 tmplag3=0.412 tmplag24=0.412 tmpd1p=0.000 tmpd1n=0.012 tmpd3p=0.000 tmpd3n=0.012 tmpTrdp=0.000 tmpTrdn=0.012
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8020]: lag1=0.061 hppf=0.290 lag24=0.082 d1p=0.031 d1n=0.000 rollstd=0.035 up=1 down=0 upS=0.031 downS=0.000 vol=0 pvX=0 break=0
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8020]: tmplag1=0.400 tmplag3=0.412 tmplag24=0.425 tmpd1p=0.000 tmpd1n=0.000 tmpd3p=0.000 tmpd3n=0.012 tmpTrdp=0.000 tmpTrdn=0.006
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8021]: lag1=0.083 hppf=0.290 lag24=0.085 d1p=0.046 d1n=0.000 rollstd=0.064 up=1 down=0 upS=0.046 downS=0.000 vol=1 pvX=0 break=0
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8021]: tmplag1=0.400 tmplag3=0.412 tmplag24=0.425 tmpd1p=0.000 tmpd1n=0.000 tmpd3p=0.000 tmpd3n=0.012 tmpTrdp=0.000 tmpTrdn=0.006
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8022]: lag1=0.114 hppf=0.290 lag24=0.102 d1p=0.165 d1n=0.000 rollstd=0.101 up=1 down=0 upS=0.165 downS=0.000 vol=1 pvX=0 break=0
2026.01.12 08:48:17 1: mySolarForecast - DBG F[8022]: tmplag1=0.400 tmplag3=0.400 tmplag24=0.438 tmpd1p=0.000 tmpd1n=0.000 tmpd3p=0.000 tmpd3n=0.000 tmpTrdp=0.000 tmpTrdn=0.000
2026.01.12 08:48:17 1: mySolarForecast DEBUG> First attempt 0 with Seed=300872
2026.01.12 08:48:18 1: mySolarForecast DEBUG> AI FANN Training started with Params:
input datasets=8017, 
Registry version=v1_heatpump_pv, 
training algo=FANN_TRAIN_INCREMENTAL, 
output AF=LINEAR, 
hidden AF=SIGMOID, 
hidden Neurons=80-40-20, 
hidden steepness=0.5, 
Epoches=15000, 
mse_error=0.001, 
learning rate=0.00500, 
learning momentum=0.8, 
Data sharing=split after shuffle of training data and use AI internal shuffle (Train=6413, Test=1603), 
Data shuffle=2 (period=10)
2026.01.12 08:48:18 1: mySolarForecast DEBUG> Epoche 1: Train MSE=0.014347, Val MSE=0.024200, Val MAE=0.106411, Val MedAE=0.052485, Bit_Fail=60 -> Snap metric improved
2026.01.12 08:48:23 1: mySolarForecast DEBUG> Epoche 10: Train MSE=0.013907, Val MSE=0.024962, Val MAE=0.103846, Val MedAE=0.045119, Bit_Fail=65 -> Snap weighted rmse improved
2026.01.12 08:48:32 1: mySolarForecast DEBUG> Epoche 30: Train MSE=0.013929, Val MSE=0.024963, Val MAE=0.103840, Val MedAE=0.045105, Bit_Fail=65 -> Snap weighted rmse improved
2026.01.12 08:49:02 1: mySolarForecast DEBUG> Epoche 90: Train MSE=0.013952, Val MSE=0.026763, Val MAE=0.099310, Val MedAE=0.031040, Bit_Fail=85 -> Snap weighted rmse improved
2026.01.12 08:49:07 1: mySolarForecast DEBUG> Epoche 100: Train MSE=0.013851, Val MSE=0.023228, Val MAE=0.110755, Val MedAE=0.064122, Bit_Fail=56

In 2-3 Stunden kann ich mit einem ersten Ergebnis rechnen, oder? (RPi 4B)

DS_Starter · 12 Januar 2026, 08:56:37

Könnte passen. Wenn attempt 0 durch ist, kann man die Zeit für einen Durchlauf besser abschätzen. Mehr als 3 passieren üblicherweise nicht.

TheTrumpeter · 12 Januar 2026, 09:18:42

Wenn ich das richtig interpretiere, ist ein Durchgang schon fertig?

Code Auswählen

2026.01.12 09:10:55 1: mySolarForecast DEBUG> Early stopping bei Epoche 2690 (no improvement since 1000 epochs)
2026.01.12 09:10:55 1: === Snapshot-Statistik ===
2026.01.12 09:10:55 1: Metric-Improvement Snapshots:              92 (letzte Epoche: 1681)
2026.01.12 09:10:55 1: Weighted-RMSE-Proxy-Improvement Snapshots: 55 (letzte Epoche: 1690)
2026.01.12 09:10:55 1: Bit-Improvement Snapshots:                 0 (letzte Epoche: 0)
2026.01.12 09:10:55 1: Bit-Tradeoff Snapshots:                    2 (letzte Epoche: 528)
2026.01.12 09:10:55 1: mySolarForecast DEBUG> Best Snapshot reloaded from Epoche 1690: Train MSE=0.001587, Val MSE=0.002532, Val MAE=0.027589, Val MedAE=0.008648, Bit_Fail=0,
2026.01.12 09:10:55 1: mySolarForecast DEBUG> Run Validation Test with 20% of Input data ...
2026.01.12 09:10:55 1: mySolarForecast DEBUG> Validation finished - Best Training MSE=0.001587, Validation MSE=0.002532, Validation Bit_Fail=0
2026.01.12 09:10:55 1: mySolarForecast DEBUG> Retrain check -> 
-- In Normalization Space: -- 
Train MSE=0.001587 
Val MSE=0.002532 
Val Mean=0.0025705388 
VAL/TRAIN MSE Ratio=1.595718 (limit=2.5) 
Diff=0.000945 (limit=0.005) 
ValStd=0.0000881450 (limit=0.000642634699763387) 
-- At Original Scale: -- 
MAE=176.564062253893 
RMSE/MAE=1.4876 (limit=1.5) 
Slope=0.835558 (limit=0.7 .. 1.3) 
Bias=78.80 (limit=+-264.846093380839) 
R2=0.88 
P95=806.8953 (limit=706.25624901557) 
P99=1229.4735 (limit=1412.51249803114) 
-- Robustness Indicators: -- 
RMSE relative=85 (limit=20) 
BitFail=0 (limit=5) 
BitFailRate=0.0000 (limit=0.1) 
Forecast Quality Score=37 
-> Retrain decision=Retrain
2026.01.12 09:10:55 1: mySolarForecast DEBUG> Retry attempt 1 with Seed=11959720
2026.01.12 09:10:55 1: mySolarForecast DEBUG> AI FANN Training started with Params:
input datasets=8017, 
Registry version=v1_heatpump_pv, 
training algo=FANN_TRAIN_INCREMENTAL, 
output AF=LINEAR, 
hidden AF=SIGMOID, 
hidden Neurons=80-40-20, 
hidden steepness=0.5, 
Epoches=15000, 
mse_error=0.001, 
learning rate=0.00500, 
learning momentum=0.8, 
Data sharing=split after shuffle of training data and use AI internal shuffle (Train=6413, Test=1603), 
Data shuffle=2 (period=10)

DS_Starter · 12 Januar 2026, 09:28:52

Ja, genau. Das Momntum learning momentum=0.8 ist etwas zu hoch eingestellt, besser learning momentum=0.4/0.5.
Aber mal abwarten was das Ergebnis ist. Ich hatte gestern viel am Profil v1_common_active_pv, v1_heatpump_active_pv gemacht, was du aber aktuell nicht ausgewählt hast.

TheTrumpeter · 12 Januar 2026, 09:42:23

Zitat von: DS_Starter am 12 Januar 2026, 09:28:52Ich hatte gestern viel am Profil v1_common_active_pv, v1_heatpump_active_pv gemacht, was du aber aktuell nicht ausgewählt hast.

Oh, das habe ich nicht genau genug gelesen, dachte "v1_heatpump_pv" passt eh.

Ich lass' es trotzdem mal durchlaufen (2. Durchgang ist auch schon fertig) und stell' dann auf "v1_heatpump_active_pv" um.

Code Auswählen

2026.01.12 09:39:23 1: mySolarForecast DEBUG> Early stopping bei Epoche 3391 (no improvement since 1000 epochs)
2026.01.12 09:39:23 1: === Snapshot-Statistik ===
2026.01.12 09:39:23 1: Metric-Improvement Snapshots:              95 (letzte Epoche: 2391)
2026.01.12 09:39:23 1: Weighted-RMSE-Proxy-Improvement Snapshots: 55 (letzte Epoche: 2263)
2026.01.12 09:39:23 1: Bit-Improvement Snapshots:                 0 (letzte Epoche: 0)
2026.01.12 09:39:23 1: Bit-Tradeoff Snapshots:                    3 (letzte Epoche: 482)
2026.01.12 09:39:23 1: mySolarForecast DEBUG> Best Snapshot reloaded from Epoche 2391: Train MSE=0.001436, Val MSE=0.002543, Val MAE=0.027361, Val MedAE=0.008083, Bit_Fail=0,
2026.01.12 09:39:23 1: mySolarForecast DEBUG> Run Validation Test with 20% of Input data ...
2026.01.12 09:39:23 1: mySolarForecast DEBUG> Validation finished - Best Training MSE=0.001436, Validation MSE=0.002543, Validation Bit_Fail=0
2026.01.12 09:39:23 1: mySolarForecast DEBUG> Retrain check -> 
-- In Normalization Space: -- 
Train MSE=0.001436 
Val MSE=0.002543 
Val Mean=0.0027083563 
VAL/TRAIN MSE Ratio=1.770873 (limit=2.5) 
Diff=0.001107 (limit=0.005) 
ValStd=0.0001516924 (limit=0.000677089072847024) 
-- At Original Scale: -- 
MAE=175.107371397212 
RMSE/MAE=1.4903 (limit=1.5) 
Slope=0.839147 (limit=0.7 .. 1.3) 
Bias=71.16 (limit=+-262.661057095819) 
R2=0.88 
P95=799.7077 (limit=700.42948558885) 
P99=1229.5355 (limit=1400.8589711777) 
-- Robustness Indicators: -- 
RMSE relative=84 (limit=20) 
BitFail=0 (limit=5) 
BitFailRate=0.0000 (limit=0.1) 
Forecast Quality Score=38 
-> Retrain decision=Retrain
2026.01.12 09:39:23 1: mySolarForecast DEBUG> Retry attempt 2 with Seed=11993500
2026.01.12 09:39:23 1: mySolarForecast DEBUG> AI FANN Training started with Params:
input datasets=8017, 
Registry version=v1_heatpump_pv, 
training algo=FANN_TRAIN_INCREMENTAL, 
output AF=LINEAR, 
hidden AF=SIGMOID, 
hidden Neurons=80-40-20, 
hidden steepness=0.5, 
Epoches=15000, 
mse_error=0.001, 
learning rate=0.00500, 
learning momentum=0.8, 
Data sharing=split after shuffle of training data and use AI internal shuffle (Train=6413, Test=1603), 
Data shuffle=2 (period=10)
2026.01.12 09:39:23 1: mySolarForecast DEBUG> Epoche 1: Train MSE=0.014301, Val MSE=0.024170, Val MAE=0.106522, Val MedAE=0.052795, Bit_Fail=60 -> Snap metric improved

TheTrumpeter · 12 Januar 2026, 11:08:41

So, habe fertig:

Code Auswählen

2026.01.12 11:00:42 1: === Snapshot-Statistik ===
2026.01.12 11:00:42 1: Metric-Improvement Snapshots:              128 (letzte Epoche: 3241)
2026.01.12 11:00:42 1: Weighted-RMSE-Proxy-Improvement Snapshots: 58 (letzte Epoche: 3253)
2026.01.12 11:00:42 1: Bit-Improvement Snapshots:                 0 (letzte Epoche: 0)
2026.01.12 11:00:42 1: Bit-Tradeoff Snapshots:                    3 (letzte Epoche: 479)
2026.01.12 11:00:42 1: mySolarForecast DEBUG> Best Snapshot reloaded from Epoche 3253: Train MSE=0.001276, Val MSE=0.002697, Val MAE=0.027604, Val MedAE=0.008233, Bit_Fail=0,
2026.01.12 11:00:42 1: mySolarForecast DEBUG> Run Validation Test with 20% of Input data ...
2026.01.12 11:00:42 1: mySolarForecast DEBUG> Validation finished - Best Training MSE=0.001276, Validation MSE=0.002697, Validation Bit_Fail=0
2026.01.12 11:00:42 1: mySolarForecast DEBUG> Retrain check -> 
-- In Normalization Space: -- 
Train MSE=0.001276 
Val MSE=0.002697 
Val Mean=0.0026454248 
VAL/TRAIN MSE Ratio=2.114081 (limit=2.5) 
Diff=0.001421 (limit=0.005) 
ValStd=0.0001606879 (limit=0.000661356190187747) 
-- At Original Scale: -- 
MAE=176.66372875335 
RMSE/MAE=1.4822 (limit=1.5) 
Slope=0.832313 (limit=0.7 .. 1.3) 
Bias=67.15 (limit=+-264.995593130024) 
R2=0.88 
P95=813.5449 (limit=706.654915013398) 
P99=1321.0065 (limit=1413.3098300268) 
-- Robustness Indicators: -- 
RMSE relative=84 (limit=20) 
BitFail=0 (limit=5) 
BitFailRate=0.0000 (limit=0.1) 
Forecast Quality Score=37 
-> Retrain decision=Retrain
2026.01.12 11:00:42 1: mySolarForecast DEBUG> Best model after retries comes from Attempt=1 with: 
Seed=11959720, 
Model Score=38, 
Model Slope=0.84, 
Model Bias=71.16, 
VAL MedAE=51.73, 
VAL MAE=175.11, 
VAL weighted RMSE=260.96, 
VAL weighted RMSE relative=84 %, 
VAL weighted RMSE_Rating=very bad, 
VAL R2=0.88, 
Val MSE=0.002543
2026.01.12 11:00:42 1: mySolarForecast DEBUG> AI FANN training data successfuly written to file: ./FHEM/FhemUtils/NeuralNet_SolarForecast_mySolarForecast
2026.01.12 11:00:45 1: mySolarForecast DEBUG> AI FANN con Training BlockingCall PID '18554' finished

stefanru · 12 Januar 2026, 11:19:39

Hi Heiko,
danke das hat schon mit den graphicShowNight 0.
Irgendwie ist mir das vorher nie so aufgefallen.
Ich beobachte es.

Kurze Frage zur KI.
Ich habe nun nur dein Modell eingestellt
aiControl aiConActivate=1 aiConProfile=v1_heatpump_active_pv

und bekomme das und ich glaube das sieht nicht gut aus:

Informationen zum neuronalen Netz der Verbrauchsvorhersage

letztes KI-Training: 12.01.2026 03:27:44 / Laufzeit in Sekunden: 3733
KI Abfragestatus: ok
letzte KI-Ergebnis Generierungsdauer: 84.84 ms
Verbrauchernummer Wärmepumpe: 01

=== Modellparameter ===

Normierungsgrenzen: PV=27280 Wh, Hausverbrauch: Min=0 Wh / Max=11860 Wh
Trainingsdaten: 8199 Datensätze (Training=6559, Validierung=1640)
Architektur: Inputs=112, Hidden Layers=80-40-20, Outputs=1
Hyperparameter: Learning Rate=0.005, Momentum=0.5, BitFail-Limit=0.35
Aktivierungen: Hidden=SIGMOID, Steilheit=0.9, Output=LINEAR
Trainingsalgorithmus: INCREMENTAL, Registry Version=v1_heatpump_active_pv
Zufallsgenerator: Mode=2, Periode=10

=== Trainingsmetriken ===

bestes Modell bei Epoche: 1366 (von max. 15000)
Training MSE: 0.001336
Validation MSE: 0.008961
Validation MSE Average: 0.010217
Validation MSE Standard Deviation: 0.000501
Validation Bit_Fail: 6
Model Bias: 993 Wh
Model Slope: 0.7
Trainingsbewertung: Retrain

=== Fehlermaße der Prognosen ===

MAE: 811.81 Wh
MedAE: 546.69 Wh
RMSE: 918.20 Wh
RMSE relative: 31 %
RMSE Rating: weak
MAPE: 37.97 %
MdAPE: 20.71 %
R²: 0.37

Gruß und Danke,
Stefan

DS_Starter · 12 Januar 2026, 11:35:09

Wenn ihr (TheTrumpeter, Stefan) eure Trainingslogs und einen Screenshot der Balkengrafik mit der Verbrauchsprognose der nächsten Stunden (soviel wie es geht) an eure Posts anhängt, kann ich das Ergebnis von der KI analysieren lassen und ggf. Verbesserungen im Profil einbauen.

Auch wenn die Werte formal schlecht aussehen, können sie von z.B. Ausreißern verursacht sein, die nicht zu vermeiden sind und es kann sich trotzdem eine solide Prognose ergeben (und umgekehrt).

Hier mal zur Verdeutlichung die Kennzahlen und Prognose von mir (ohne WP).
Wie zu sehen gibt es heute zwei deutliche Peak-Abweichungen, die durch Wama und Trockner veruracht sind. Da kann keine KI etwas dagegen tun weil es einfach durch nichts vorhergesehen werden kann. Lediglich in den Folgestunden 08 und 11 wird durch die Trendfolgelogik ein gewisser Ausgleich geschaffen der die Tagesgesamtverbrauchsprognose wieder in einen realen Rahmen rückt.

Code Auswählen

=== Modellparameter ===

Normierungsgrenzen: PV=8503 Wh, Hausverbrauch: Min=0 Wh / Max=8938 Wh
Trainingsdaten: 8176 Datensätze (Training=6540, Validierung=1636)
Architektur: Inputs=65, Hidden Layers=80-40-20, Outputs=1
Hyperparameter: Learning Rate=0.005, Momentum=0.4, BitFail-Limit=0.35
Aktivierungen: Hidden=SIGMOID, Steilheit=1.3, Output=LINEAR
Trainingsalgorithmus: INCREMENTAL, Registry Version=v1_common_active_pv
Zufallsgenerator: Mode=2, Periode=10

=== Trainingsmetriken ===

bestes Modell bei Epoche: 692 (von max. 15000)
Training MSE: 0.000185
Validation MSE: 0.000257
Validation MSE Average: 0.000266
Validation MSE Standard Deviation: 0.000017
Validation Bit_Fail: 0
Model Bias: 108 Wh
Model Slope: 0.9
Trainingsbewertung: Borderline

=== Fehlermaße der Prognosen ===

MAE: 97.76 Wh
MedAE: 65.56 Wh
RMSE: 115.47 Wh
RMSE relative: 18 %
RMSE Rating: acceptable
MAPE: 16.76 %
MdAPE: 9.80 %
R²: 0.91

=== Drift-Kennzahlen ===

Drift Score: 2.13
Drift RMSE relative: 42.51
Drift Bias: 311.22
Drift Slope: 0.522
Drift Bewertung: moderate

stefanru · 12 Januar 2026, 11:59:36

Ah ok,
ja klar schicke ich.
Seltsamer weise habe ich keine Drift zahlen.
Aber eigentlich sieht die Vorhersage gut aus!

Code Auswählen

Informationen zum neuronalen Netz der Verbrauchsvorhersage

letztes KI-Training: 12.01.2026 03:27:44 / Laufzeit in Sekunden: 3733
KI Abfragestatus: ok
letzte KI-Ergebnis Generierungsdauer: 86.64 ms
Verbrauchernummer Wärmepumpe:  01

=== Modellparameter ===

Normierungsgrenzen: PV=27280 Wh, Hausverbrauch: Min=0 Wh / Max=11860 Wh
Trainingsdaten: 8199 Datensätze (Training=6559, Validierung=1640)
Architektur: Inputs=112, Hidden Layers=80-40-20, Outputs=1
Hyperparameter: Learning Rate=0.005, Momentum=0.5, BitFail-Limit=0.35
Aktivierungen: Hidden=SIGMOID, Steilheit=0.9, Output=LINEAR
Trainingsalgorithmus: INCREMENTAL, Registry Version=v1_heatpump_active_pv
Zufallsgenerator: Mode=2, Periode=10

=== Trainingsmetriken ===

bestes Modell bei Epoche: 1366 (von max. 15000)
Training MSE: 0.001336
Validation MSE: 0.008961
Validation MSE Average: 0.010217
Validation MSE Standard Deviation: 0.000501
Validation Bit_Fail: 6
Model Bias: 993 Wh
Model Slope: 0.7
Trainingsbewertung: Retrain

=== Fehlermaße der Prognosen ===

MAE: 811.81 Wh
MedAE: 546.69 Wh
RMSE: 918.20 Wh
RMSE relative: 31 %
RMSE Rating: weak
MAPE: 37.97 %
MdAPE: 20.71 %
R²: 0.37

=== Drift-Kennzahlen ===

Drift Score: -
Drift RMSE relative: -
Drift Bias: -
Drift Slope: -
Drift Bewertung: -

DS_Starter · 12 Januar 2026, 12:37:22

ZitatSeltsamer weise habe ich keine Drift zahlen.
Aber eigentlich sieht die Vorhersage gut aus!

Drift wird in der Nacht gerechnet, wenn das Modell läuft. Also erst morgen.
Und ja, sieht so schlecht nicht aus... und wenn du noch das komplette Trainingslog anhängen würdest, könnte ich die KI füttern und bewerten lassen.

TheTrumpeter · 12 Januar 2026, 13:28:32

Zitat von: DS_Starter am 12 Januar 2026, 11:35:09Wenn ihr (TheTrumpeter, Stefan) eure Trainingslogs und einen Screenshot der Balkengrafik mit der Verbrauchsprognose der nächsten Stunden (soviel wie es geht) an eure Posts anhängt, kann ich das Ergebnis von der KI analysieren lassen und ggf. Verbesserungen im Profil einbauen.

Ich habe dann nach dem obigen Ergebnis auf "v1_heatpump_active_pv" umgestellt und das Training neu gestartet... läuft immer noch. Aktueller Zwischenstand:

Code Auswählen

2026.01.12 13:21:49 1: mySolarForecast DEBUG> Early stopping bei Epoche 4370 (no improvement since 1000 epochs)
2026.01.12 13:21:49 1: === Snapshot-Statistik ===
2026.01.12 13:21:49 1: Metric-Improvement Snapshots:              66 (letzte Epoche: 3370)
2026.01.12 13:21:49 1: Weighted-RMSE-Proxy-Improvement Snapshots: 59 (letzte Epoche: 3302)
2026.01.12 13:21:49 1: Bit-Improvement Snapshots:                 0 (letzte Epoche: 0)
2026.01.12 13:21:49 1: Bit-Tradeoff Snapshots:                    0 (letzte Epoche: 0)
2026.01.12 13:21:49 1: mySolarForecast DEBUG> Best Snapshot reloaded from Epoche 3370: Train MSE=0.001108, Val MSE=0.002496, Val MAE=0.025909, Val MedAE=0.007147, Bit_Fail=0,
2026.01.12 13:21:49 1: mySolarForecast DEBUG> Run Validation Test with 20% of Input data ...
2026.01.12 13:21:49 1: mySolarForecast DEBUG> Validation finished - Best Training MSE=0.001108, Validation MSE=0.002496, Validation Bit_Fail=0
2026.01.12 13:21:49 1: mySolarForecast DEBUG> Retrain check -> 
-- In Normalization Space: -- 
Train MSE=0.001108 
Val MSE=0.002496 
Val Mean=0.0023810494 
VAL/TRAIN MSE Ratio=2.253010 (limit=2.5) 
Diff=0.001388 (limit=0.005) 
ValStd=0.0000926900 (limit=0.000595262357462899) 
-- At Original Scale: -- 
MAE=165.81584353369 
RMSE/MAE=1.4742 (limit=1.5) 
Slope=0.841794 (limit=0.7 .. 1.3) 
Bias=59.55 (limit=+-248.723765300536) 
R2=0.89 
P95=800.6679 (limit=663.263374134762) 
P99=1287.0716 (limit=1326.52674826952) 
-- Robustness Indicators: -- 
RMSE relative=79 (limit=20) 
BitFail=0 (limit=5) 
BitFailRate=0.0000 (limit=0.1) 
Forecast Quality Score=39 
-> Retrain decision=Retrain
2026.01.12 13:21:49 1: mySolarForecast DEBUG> Retry attempt 3 with Seed=6829023
2026.01.12 13:21:49 1: mySolarForecast DEBUG> AI FANN Training started with Params:
input datasets=8020, 
Registry version=v1_heatpump_active_pv, 
training algo=FANN_TRAIN_INCREMENTAL, 
output AF=LINEAR, 
hidden AF=SIGMOID, 
hidden Neurons=80-40-20, 
hidden steepness=0.5, 
Epoches=15000, 
mse_error=0.001, 
learning rate=0.00500, 
learning momentum=0.8, 
Data sharing=split after shuffle of training data and use AI internal shuffle (Train=6416, Test=1603), 
Data shuffle=2 (period=10)

Am "learning momentum=0.8" habe ich erstmal nichts verändert.

Wolle02 · 12 Januar 2026, 13:35:48

Dann will ich auch mal meine Trainingsdaten teilen:

Code Auswählen

=== Modellparameter ===

Normierungsgrenzen: PV=8888 Wh, Hausverbrauch: Min=0 Wh / Max=12741 Wh
Trainingsdaten: 6210 Datensätze (Training=4968, Validierung=1242)
Architektur: Inputs=39, Hidden Layers=80-40-20, Outputs=1
Hyperparameter: Learning Rate=0.005, Momentum=0.5, BitFail-Limit=0.35
Aktivierungen: Hidden=SIGMOID, Steilheit=0.9, Output=LINEAR
Trainingsalgorithmus: INCREMENTAL, Registry Version=v1_common
Zufallsgenerator: Mode=2, Periode=10

=== Trainingsmetriken ===

bestes Modell bei Epoche: 1069 (von max. 15000)
Training MSE: 0.000461
Validation MSE: 0.000289
Validation MSE Average: 0.000518
Validation MSE Standard Deviation: 0.000072
Validation Bit_Fail: 0
Model Bias: 83 Wh
Model Slope: 0.8
Trainingsbewertung: Retrain

=== Fehlermaße der Prognosen ===

MAE: 136.48 Wh
MedAE: 86.30 Wh
RMSE: 176.37 Wh
RMSE relative: 31 %
RMSE Rating: weak
MAPE: 18.53 %
MdAPE: 15.28 %
R²: 0.90

=== Drift-Kennzahlen ===

Drift Score: 2.09
Drift RMSE relative: 93.71
Drift Bias: 60.92
Drift Slope: 0.134
Drift Bewertung: severe

Bis auf den RSME relative Wert finde ich eigentlich, dass die Werte gar nicht schlecht aussehen. Eine KI Analyse fände ich auch interessant.
Anbei noch ein Screenshot. Der Peak ist Mittagessen kochen. Wärmepumpe habe ich zwar; findet aber für die Prognose keine Verwendung, weil anderer Stromkreis. MIt dem EV habe ich die KI nicht nicht bekannt gemacht, muss aber wahrscheinlich morgen mal laden.