modnn 3.0.5

Physics-Informed Modularized Neural Network for Building Energy Modeling

ModNN

ModNN is a Modularized Physics-Informed Neural Network for building energy modeling.

It incorporates with physics-informed model structure, loss function, and model constraints.

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🚀 Installation

You can install the package using pip:

pip install modnn

🧠 Example

Please find the online Jupyter notebook for a step-by-step instruction: https://colab.research.google.com/drive/1A2jt1q53RtxGuaoym6N1PmlKELDPpYFX?usp=sharing

🧠 Update log

🧠 [2.0.0] 2025 May 9

To further improve physical consistency,

I replaced heat transfer module by set of energy balance equations,

Start from version 2.0.0

🧠 [2.0.1] 2025 May 10

Add another parameter: "envelop_mdl",

Allow user to use the new physics based module or previous data driven module.

🧠 [2.0.2] 2025 May 10

Fix bug due to parameter: "envelop_mdl",

Vectorize calculation,speed improved by ~6 times.

🧠 [3.0.0] 2025 June 11

Update datadriven modnn RC based envelop_mdl really hard to tune on new dataset

🧠 [3.0.1] 2025 June 11

Add a step function for one step ahead prediction

🧠 [3.0.2] 2025 June 11

Fix bug for step function

🧠 [3.0.3] 2025 June 11

Fix bug for step function

🧠 [3.0.4] 2025 Sept 10

Didn't work on it for 3 months, just update the latest version Will use it for BESTOpt building dynamic model

🧠 [3.0.5] 2025 Sept 15

3.0.4 CAN-NOT work at all, I mistakenly comment one line and add a new line of code 3.0.5 is GOOD, will use it for BESTOpt dynamic modeling

🧪 Requirements

Python 3.7+

PyTorch

NumPy

Pandas

Matplotlib

Seaborn

scikit-learn

tqdm

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📬 License

MIT License

🙋‍♂️ Author

Zixin Jiang: [e-mail]