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fix: load prediction adjustment with measurement in kwh (#826)

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Use load energy meter reading in kWh for load prediction adjustment.
Before the reading was falsely regarded to be in Wh.

Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
This commit is contained in:
Bobby Noelte
2026-01-01 12:26:29 +01:00
committed by GitHub
parent 4434b7109e
commit 39973bf836
8 changed files with 33 additions and 30 deletions
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2
docs/_generated/configexample.md
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@@ -136,7 +136,7 @@
}
},
"general": {
"version": "0.2.0.dev70048701",
"version": "0.2.0.dev81043823",
"data_folder_path": null,
"data_output_subpath": "output",
"latitude": 52.52,

6
docs/_generated/configgeneral.md
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@@ -16,7 +16,7 @@
| latitude | `EOS_GENERAL__LATITUDE` | `Optional[float]` | `rw` | `52.52` | Latitude in decimal degrees between -90 and 90. North is positive (ISO 19115) (°) |
| longitude | `EOS_GENERAL__LONGITUDE` | `Optional[float]` | `rw` | `13.405` | Longitude in decimal degrees within -180 to 180 (°) |
| timezone | | `Optional[str]` | `ro` | `N/A` | Computed timezone based on latitude and longitude. |
| version | `EOS_GENERAL__VERSION` | `str` | `rw` | `0.2.0.dev70048701` | Configuration file version. Used to check compatibility. |
| version | `EOS_GENERAL__VERSION` | `str` | `rw` | `0.2.0.dev81043823` | Configuration file version. Used to check compatibility. |
:::
<!-- pyml enable line-length -->
@@ -28,7 +28,7 @@
```json
{
"general": {
"version": "0.2.0.dev70048701",
"version": "0.2.0.dev81043823",
"data_folder_path": null,
"data_output_subpath": "output",
"latitude": 52.52,
@@ -46,7 +46,7 @@
```json
{
"general": {
"version": "0.2.0.dev70048701",
"version": "0.2.0.dev81043823",
"data_folder_path": null,
"data_output_subpath": "output",
"latitude": 52.52,

2
docs/_generated/openapi.md
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@@ -1,6 +1,6 @@
# Akkudoktor-EOS
**Version**: `v0.2.0.dev70048701`
**Version**: `v0.2.0.dev81043823`
<!-- 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.