mirror of https://github.com/Akkudoktor-EOS/EOS.git synced 2025-04-19 08:55:15 +00:00

Add documentation that covers:

- configuration
- prediction

Add Python scripts that support automatic documentation generation for
configuration data defined with pydantic.

Adapt EOS configuration to provide more methods for REST API and
automatic documentation generation.

Adapt REST API to allow for EOS configuration file load and save.
Sort REST API on generation of openapi markdown for docs.

Move logutil to core/logging to allow configuration of logging by standard config.

Make Akkudoktor predictions always start extraction of prediction data at start of day.
Previously extraction started at actual hour. This is to support the code that assumes
prediction data to start at start of day.

Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>

2025-01-05 14:41:07 +01:00

3.5 KiB

Raw Blame History

% SPDX-License-Identifier: Apache-2.0

Measurements

Measurements are utilized to refine predictions using real data from your system, thereby enhancing accuracy.

Household Load Measurement
Grid Export Measurement
Grid Import Measurement

Storing Measurements

EOS stores measurements in a key-value store, where the term measurement key refers to the unique identifier used to store and retrieve specific measurement data. Note that the key-value store is memory-based, meaning that all stored data will be lost upon restarting the EOS REST server.

:::{admonition} Todo :class: note Ensure that measurement data persists across server restarts. :::

Several endpoints of the EOS REST server allow for the management and retrieval of these measurements.

The measurement data must be or is provided in one of the following formats:

1. DateTimeData

A dictionary with the following structure:

    {
        "start_datetime": "2024-01-01 00:00:00",
        "interval": "1 Hour",
        "<measurement key>": [value, value, ...],
        "<measurement key>": [value, value, ...],
        ...
    }

2. DateTimeDataFrame

A JSON string created from a pandas dataframe with a DatetimeIndex. Use pandas.DataFrame.to_json(orient="index"). The column name of the data must be the same as the names of the measurement keys.

3. DateTimeSeries

A JSON string created from a pandas series with a DatetimeIndex. Use pandas.Series.to_json(orient="index").

Load Measurement

The EOS measurement store provides for storing meter readings of loads. There are currently five loads foreseen. The associated measurement keys are:

measurement_load0_mr: Load0 meter reading [kWh]
measurement_load1_mr: Load1 meter reading [kWh]
measurement_load2_mr: Load2 meter reading [kWh]
measurement_load3_mr: Load3 meter reading [kWh]
measurement_load4_mr: Load4 meter reading [kWh]

For ease of use, you can assign descriptive names to the measurement keys to represent your system's load sources. Use the following configuration options to set these names (e.g., 'Dish Washer', 'Heat Pump'):

measurement_load0_name: Name of the load0 source
measurement_load1_name: Name of the load1 source
measurement_load2_name: Name of the load2 source
measurement_load3_name: Name of the load3 source
measurement_load4_name: Name of the load4 source

Load measurements can be stored for any datetime. The values between different meter readings are linearly approximated. Since optimization occurs on the hour, storing values between hours is generally not useful.

The EOS measurement store automatically sums all given loads to create a total load value series for specified intervals, usually one hour. This aggregated data can be used for load predictions.

Grid Export/ Import Measurement

The EOS measurement store also allows for the storage of meter readings for grid import and export. The associated measurement keys are:

measurement_grid_export_mr: Export to grid meter reading [kWh]
measurement_grid_import_mr: Import from grid meter reading [kWh]

:::{admonition} Todo :class: note Currently not used. Integrate grid meter readings into the respective predictions. :::

3.5 KiB Raw Blame History