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darkgreybox-0.3.1
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مشاهده بیشترتوضیحات
DarkGreyBox: An Open-Source Data-Driven Python Building Thermal Model Inspired By Genetic Algorithms and Machine Learning
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | darkgreybox-0.3.1 |
| نام | darkgreybox |
| نسخه کتابخانه | 0.3.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | csaba zagoni |
| ایمیل نویسنده | czagoni@greenpeace.org |
| آدرس صفحه اصلی | https://github.com/czagoni/darkgreybox |
| آدرس اینترنتی | https://pypi.org/project/darkgreybox/ |
| مجوز | - |
# Dark Grey Box
[](https://www.gnu.org/licenses/gpl-3.0)
[](https://circleci.com/gh/czagoni/darkgreybox)
[](https://badge.fury.io/py/darkgreybox)
## DarkGreyBox: An open-source data-driven python building thermal model inspired by Genetic Algorithms and Machine Learning
### Read the medium article here: https://medium.com/analytics-vidhya/data-driven-thermal-models-for-buildings-15385f744fc5
Constructing simple, accurate and easy-to-interpret thermal models for existing buildings is essential in reducing the environmental impact of our built environment. DarkGreyBox provides a data-driven approach to constructing and fitting RC-equivalent grey box thermal models for buildings, within the classic Machine Learning (ML) framework for straightforward model performance evaluation.
A large number of competing models can be set up in easy-to-configure pipelines and the best performing models are selected based on principles inspired by Genetic Algorithms (GA). This approach also addresses the main disadvanatages of classical grey-box thermal modelling techniques by not requiring initial condition values for the thermal parameters to be pre-calculated and also not requiring an excitation signal to be injected into the building for successful model convergence and evaluation.
The massive advantages of using a DarkGreyBoxModel over a black-box (i.e. Machine Learning) model - e.g. a deep sequence-to-sequence model - are that it is easily interpreted by humans and that it slots easily into other modelling frameworks. E.g. to model the behaviour of a building with its connected heating system, simply construct a heat source model in a MILP framework and the grey-box building thermal model just slots in as a set of linear differential equations with a handful of parameters. Doing this with a deep ML model would be quite tricky.
The easiest way to get familiar with DarkGreyBox is to look at the [tutorials](docs/tutorials/).
## Installation
### Dependencies
DarkGreyBox requires:
- Python (>= 3.6)
- lmfit
- pandas
- joblib
Note: these are only the core dependencies and you will most likely want to install either the optional dependencies or your preferred custom alternatives to them.
### User installation from PyPi package (latest release)
Install DarkGreyBox via `pip`:
```bash
pip install darkgreybox
```
#### Optional Dependencies
This gives you a headstart for using DarkGreyBox in anger.
- scikit-learn
- numdifftools
- statsmodels
- matplotlib
- jupyter
- notebook
You can install these additional dependencies via pip:
```bash
pip install darkgreybox[dev]
```
### User installation from source repository
You can check the latest sources with the command::
```bash
git clone https://github.com/czagoni/darkgreybox.git
```
You can install the dev dependencies (from the root of the repository):
```bash
pip install -e .'[dev]'
```
## Documentation
To access the tutorials you need to have cloned DarkGreyBox from the source repository (see above).
### Tutorials
The easiest way to get into the details of how DarkGreyBox works is through following the tutorials:
* [Demo Notebook 01 - Ti Model Direct Fit](docs/tutorials/darkgrey_poc_demo_01.ipynb): This notebook demonstrates the direct usage of the DarkGreyBox models via a simple fitting example for a Ti model.
* [Demo Notebook 02 - TiTe Model Direct Fit FAIL](docs/tutorials/darkgrey_poc_demo_02.ipynb): This notebook demonstrates the direct usage of the DarkGreyBox models via a simple fitting example for a TiTe model. In this case a local minimum is found during the fitting process and the model heavily oscillates making it unusable.
* [Demo Notebook 03 - TiTe Model Wrapper Fit PASS](docs/tutorials/darkgrey_poc_demo_03.ipynb): This notebook demonstrates the usage of the DarkGreyBox models via fitting them with a wrapper function for a TiTe model.
* [Demo Notebook 04 - DarkGreyFit](docs/tutorials/darkgrey_poc_demo_04.ipynb): This notebook demonstrates the usage of the DarkGreyBox models via fitting them with DarkGreyFit, setting up and evaluating multiple pipelines at once.
Launch a new jupyter notebook from the root of the repository:
```bash
jupyter notebook
```
## Development
We welcome new contributors of all experience levels.
### Source code
You can check the latest sources with the command::
```bash
git clone https://github.com/czagoni/darkgreybox.git
```
You can install the dev and test dependencies (from the root of the repository):
```bash
pip install -e .'[dev,test]'
```
### Testing
After installation, you can launch the test suite from the repo root
directory (you will need to have `pytest` installed):
```bash
pytest
```
You can check linting from the repo root directory (you will need to have `flake8` installed):
```bash
flake8
```
نیازمندی
| مقدار | نام |
|---|---|
| ~=1.1.0 | lmfit |
| ~=1.5.2 | pandas |
| ~=1.2.0 | joblib |
| ~=0.13.1 | statsmodels |
| ~=0.9.39 | numdifftools |
| ~=1.2.0 | scikit-learn |
| ~=3.6.3 | matplotlib |
| ~=1.0.0 | jupyter |
| ~=6.5.2 | notebook |
| ~=2.0.1 | autopep8 |
| ~=5.11.4 | isort |
| ~=6.0.0 | flake8 |
| ~=7.2.1 | pytest |
| ~=3.10.0 | pytest-mock |
| ~=4.0.0 | pytest-cov |
نحوه نصب
نصب پکیج whl darkgreybox-0.3.1:
pip install darkgreybox-0.3.1.whl
نصب پکیج tar.gz darkgreybox-0.3.1:
pip install darkgreybox-0.3.1.tar.gz