NEWS

Problem gefunden:
In den sogenanntem Label des Alerts müssen Variablen eingetragen werden: zb. Router = Asus
Diese Variablen müssen dann in den Benachrichtigungsrichtlinien unter "New nested policy" ebenfalls eingetragen werden.

Unbenannt1.jpg
Unbenannt.jpg

Trotzdem herzlichen Dank!

@domm

Also ich hab jetzt noch zweimal drübergelesen, aber ich hab immernoch nicht wirklich verstanden wo dein Problem liegt.
Vielleicht solltest du mal ein Bild vom Grafana Dashboard zeigen und was du genau machen möchtest.

Wenn du schreibst du hast alle DP vorliegen und schreibst diese auch in die InfluxDB, dann mußt du die doch nur noch abfragen, bzw eine vorhandene Abfrage anpassen.
Dann in Grafana das Dashboard aufrufen und eine neue Visualisierung hinzufügen.

@andi_grafa sagte in Grafana, Summierung der letzten Tageswerte:

Wie du schon richtig selbst festgestellt hast, summierst du stumpf ALLE Werte. Stattdessen willst du ja nur den jeweilig letzten Wert des Tages nehmen und den im Monat summieren. Das bedeutet, dass du noch ein zusätzliches "aggregateWindow" benötigt, mit dem du den Tages"letzt"wert ermittelst. Versuchs mal so:

import "timezone" import "date" option location = timezone.location(name: "Europe/Berlin") from(bucket: "iobroker") |> range(start: date.truncate(t: date.add(d: -6mo, to: today()), unit: 1mo), stop: date.truncate(t: today(), unit: 1mo)) |> filter(fn: (r) => r["_measurement"] == "Regen heute") |> filter(fn: (r) => r["_field"] == "value") |> aggregateWindow(every: 1d, fn: last, createEmpty: true, timeSrc: "_start") |> aggregateWindow(every: 1mo, fn: sum, createEmpty: true, timeSrc: "_start")

@meister-mopper Danke 👍

@crunchip vielen dank, das hätte ich nie gefunden.

@mere13 sagte in Tagesverbrauch - Ich komm nicht drauf:

|> aggregateWindow(every: 24h, fn: last, timeSrc: "_start", createEmpty: false)

Würde stattdessen mal every: 1d testen. Sonst werden wahrscheinlich immer nur 24 Stunden genommen und die Tagesgrenze ignoriert.

Ich würde vor dem aggregateWindow die Differenz bilden und dann sum aller Differenzen anwenden.

import "timezone" option location = timezone.location(name: "Europe/Berlin") from(bucket: "homebucket") |> range(start: -10d) |> filter(fn: (r) => r["_measurement"] == "mystrom energy since boot - WLAN" and r["_field"] == "value") |> difference(nonNegative: true, columns: ["_value"]) |> aggregateWindow(every: 1d, fn: sum, timeSrc: "_start", createEmpty: false) |> map(fn: (r) => ({r with _value: r._value / 3600.0})) |> yield(name: "last")

siehe hier: https://forum.iobroker.net/post/1166370

@udbruegg
Ich hab es !
Entschuldigt diese halbgare Aktion. Ich war echt etwas in Panik, weil ich da echt schon einige Tage Arbeit drin habe.
Es war das einfache einloggen. Ich hatte nur was mit den Passwörtern "damals" nicht eindeutig dokumentiert und war nun total auf dem irrem Holzweg.
Sorry...
Ist mir echt etwas peinlich, dass ich bei so einer einfachen Sache, so panisch in die falsche Richtung gerannt bin.

Danke für die Unterstützung, da bei dem Datenpunkt keine ständigen Daten kommen, geht das so. Hab bei einem anderen, der ständig die Wertewechsel, da geht das so nicht. Daher erst mal Dankeschön, hier war es genau das richtig, was Ihr mir gesagt habt.

@luckyheiko

c3f6fee3-33b6-432b-95d3-cd097e8949ef-image.png

Fertig! Naja, der grobe Entwurf.
Wichtig ist, dass die Werte mit >Influx1.8 vorliegen. mit folgender Syntax kannst du die Daten umbiegen:

from(bucket: "Solar")
|> range(start: 12h, stop: 36h)
|> timeShift (duration: -48h)
|> filter(fn: (r) => r["_measurement"] == "pvforecast.0.plants.westseiteEigen.power")
|> filter(fn: (r) => r["_field"] == "value")
|> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)
|> yield(name: "mean")

Das wäre der Befehl für die Werte morgen (Graue Linie)

from(bucket: "Solar")
|> range(start: -12h, stop: 12h)
|> timeShift (duration: -24h)
|> filter(fn: (r) => r["_measurement"] == "pvforecast.0.plants.westseiteEigen.power")
|> filter(fn: (r) => r["_field"] == "value")
|> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)
|> yield(name: "mean")

Das wäre der Befehl für die Werte heute (Farbige Fläche)

Die nervöse Linie ist der aktuelle Ertrag der PV Anlaga

@fk757 hier mal ein Dashboard zum einfügen in Grafana, deinen Datenpunkt Gesamt musst du entsprechend einfügen.

Spoiler

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Wasserverbrauch", "transparent": true, "type": "timeseries" } ], "refresh": "", "schemaVersion": 38, "style": "dark", "tags": [ "Hausenergie", "Wasser" ], "templating": { "list": [] }, "time": { "from": "now-24h", "to": "now" }, "timepicker": {}, "timezone": "", "title": "Wasser", "uid": "nZwcX6d4k", "version": 14, "weekStart": "" }

@marc-berg said in Grafana: täglichen Solar-Ertrag summieren:

timeSrc: "_start"

Super!! Das war die Lösung.

@dede2403 sagte in Grafana Datum stimmt nicht:

Danke kann geschlossen werden.

Das ist aber deine Aufgabe, indem du deinen ersten Post editierst und "gelöst" mit in den Betreff setzt. 🙂

Aaahrg, ich habe übersehen, dass ich ein "Transform" eingebaut hatte, dessen Graph dann die Linie immer übermalt hat. Jetzt klappts 🙂

5a595d5e-88a9-4a08-8efb-766f06b4ff4f-image.png

@astrakid nachtrag: du musst die werte regelmäßig in deine influxdb schreiben, je nachdem wie granular das haben willst. ich schreibe alle 10s einen wert in die DBen und aggregiere später. für die tagesanzeige gehe ich auf minuten-intervalle, für wochen und monats ansichten auf 6h intervalle.

@base 👍 genau das was ich auch gesucht habe Danke

@thecad

jetzt verstanden 😉
Hat der Akku keine App die dir was anzeigt ?

Die Entnahme aus dem Akku kannst du aber perfekt bestimmen, zumindest in der Zeit, wo die Sonne untergegangen ist. Da hast du ja nur Akku Versorgung. Dann müsstest du mit Blockly einfach den Wert beim Sonnen Untergang + Aufgang tracken . Und die Differenz ist das, was du über die Nacht eingespeißt hast.

Tagsüber ist es in der Tat schwierig. Ich hab kein Akku, gibt hier sicher aber welche, die da weiterhelfen können 😉

@markus-7 Bezüglich der Legende hat ja @Negalein schon alles gezeigt.Ich wollte noch anmerken, dass der Code in dieser Version komplett simpel ist.
pivot, rename und drop sind nicht nötig, wird alles durch Transform data erledigt.

from(bucket: "iobroker") |> range(start: v.timeRangeStart, stop: v.timeRangeStop) |> filter(fn: (r) => r["_measurement"] == "iob.temp.badezimmer" or r["_measurement"] == "iob.temp.esskueche") |> filter(fn: (r) => r["_field"] == "value") |> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)

sollte reichen.
oder einfach

from(bucket: "iobroker") |> range(start: v.timeRangeStart, stop: v.timeRangeStop) |> filter(fn: (r) => r["_measurement"] =~ /(.*\.temp\..*) |> filter(fn: (r) => r["_field"] == "value") |> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)

Wenn du alle ?.temp.? datenpunkte haben willst.