“EnCoDaPy” – Energy Control and Data Preparation in Python

Overview

The Basic Service provides a system to
- read a configuration
- receive data
- start a calculation
- return the results
This interaction is possible with several interfaces, see examples/03_interfaces:
- FIWARE-API
- MQTT
- File
The controller has the functionality to read a configuration from JSON and ENV, validate it and return it as a model.
The framework provides components that can be used within a service. This is the recommended solution for running a functional service. For more information and code, see: encodapy/components/readme.md
Further documentation can be found here.
Examples and documentation for each part of the project are available under: examples

The configuration of the service must be provided via config.json and has several sections (see the documentation or the examples):
- name: Controller name - for documentation purposes only
- interfaces: Indicates which interfaces are active
- inputs: Configuration of the inputs to the controller
- outputs: Configuration of the outputs
- staticdata: Static data point configuration (Data that is not continuously updated)
- controller_components: Configuration of the controller components, see encodapy/components/readme.md or the documentation
- controller_settings: General settings about the controller
Environmental variables are required to configure the basic service and the interfaces. For more information, see encodapy/config/env_values or the documentation.

You could install the Package via PyPI:

pip install encodapy

There are two ways to use the Package:

To create your own custom service, you have to overwrite two functions of the ControllerBasicService:

prepare_start(): This is a synchronous function that prepares the start of the algorithm and specifies aspects of the service. This should not take long due to health issues in Docker containers. It only needs to be overwritten if other tasks are required after initialisation of the service.
calculation(): Asynchronous function to perform the main calculation in the service
calibration(): Asynchronous function to calibrate the service or coefficients and update StaticData in the service if only required

To start the service, you need to call

For more details, see the examples

You could use components to run them with the ComponentRunnerService
Frist, create a configuration, then start the service:
- as shown in examples/07_component_runner
- by running the service with the encodapy.service.service_main function.
If you need additional components, please see encodapy/components:
- You can use components from encodapy/components or create your own
- An easy way to build your own component is shown in examples/08_create_new_component

For different examples and documentation, how to use the tool - see examples.

The examples are intended to help you use the tool and understand how it works:

Inputs and outputs get information about the unit. The class DataUnits is used for this.
More units must be added manually.
Timeranges:
- Timeranges for data queries are different for calculation and calibration.
- The following timeranges are possible
  - ‘“minute”’
  - ‘“hour”’
  - ‘“day”’
  - ‘“month”’ (30 days for simple use)
Today, there ist no adjustment for different units. Its a TODO for the future

The recommended way to run the service is:

Create a Python environment using Poetry (see pyproject.toml).
Use a Docker container for production deployments (create a custom image using the dockerfile).

This project is licensed under the BSD License - see the LICENSE file for details.

2024-2025, TUD Dresden University of Technology, Chair of Building Energy Systems and Heat Supply

We gratefully acknowledge the financial support of the Federal Ministry for Economic Affairs and Energy.