README.md

---
language:
- en
license: cc-by-nc-4.0
base_model:
- google/flan-t5-large
---

# EPlus-LLM

**Natural Language Interface for Automated Building Energy Modeling via LLMs**  
*A prototype project exploring the use of fine-tuned large language models to automate building energy modeling from natural language input.*

<div align="center">
  <img src="https://huggingface.co/EPlus-LLM/EPlus-LLMv1/resolve/main/EPlus-LLM_graphic.png" alt="Illustration of EPlus-LLMv2 for Auto-building energy modeling" width="700"/>
</div>


## 🎉 News
- ⚡️ [2025/01/01]: A prompting-based method for auto-building energy modeling has been released.
[Paper here](https://doi.org/10.1016/j.energy.2025.134548).
- 🔥 [2024/05/016]: We first successfully implement natural language-based auto-building modeling by fine-tuning a large language model (LLM).
[Paper here](https://doi.org/10.1016/j.apenergy.2024.123431).

## 🚀 Key Features
- Scalability: Auto-generates EnergyPlus models, including varying geometry sizes and internal loads.
- Accuracy & Efficiency: Achieves 100% modeling accuracy while reducing manual modeling time by over 95%.
- Interaction & Automation: A user-friendly human-AI interface for seamless model creation and customization.

## 🏗️ Target Users
This current platform is designed for engineers, architects, and researchers working in building performance, sustainability, and resilience. It is especially useful during early-stage conceptual design when modeling decisions have the greatest impact.

## 🚀 Quick Start

Here provides a code snippet to show you how to load the EPlus-LLM and auto-generate building energy models.

```python
import torch
from transformers import (
    AutoModelForSeq2SeqLM,
    AutoTokenizer,
)

tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-large")
model = AutoModelForSeq2SeqLM.from_pretrained("EPlus-LLM/EPlus-LLMv1")

generation_config = model.generation_config
generation_config.max_new_tokens = 1300
generation_config.temperature = 0.1
generation_config.top_p = 0.1
generation_config.num_return_sequences = 1
generation_config.pad_token_id = tokenizer.eos_token_id
generation_config.eos_token_id = tokenizer.eos_token_id

input="<Your input, description of the desired building.>"
input_ids = tokenizer(input, return_tensors="pt", truncation=False).to(device)
generated_ids = model.generate(input_ids = input_ids.input_ids,
                           attention_mask = input_ids.attention_mask,
                           generation_config = generation_config)

generated_output = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
generated_output = new_tokens.replace("_", " ")
generated_ooutput = new_tokens.replace("|", "\n")

print(generated_output)

```

## 📝 Citation

If you find our work helpful, feel free to give us a cite.

```
@article{jiang2025EPlus-LLM,
  author    = {Gang Jiang and Zhihao Ma and Liang Zhang and Jianli Chen},
  title     = {Prompt engineering to inform large language models in automated building energy modeling},
  journal   = {Applied Energy},
  volume    = {367},
  pages     = {123431},
  year      = {2024},
  month     = {Aug},
  doi       = {https://doi.org/10.1016/j.apenergy.2024.123431}
}

@article{jiang2025prompting,
  author    = {Gang Jiang and Zhihao Ma and Liang Zhang and Jianli Chen},
  title     = {Prompt engineering to inform large language models in automated building energy modeling},
  journal   = {Energy},
  volume    = {316},
  pages     = {134548},
  year      = {2025},
  month     = {Feb},
  doi       = {https://doi.org/10.1016/j.energy.2025.134548}
}
```