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.  
  
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Gangjiang1/EPlus-LLM/blob/main/v1/EPlus-LLM_inference.ipynb)

```python
# ⚠️ Please make sure you have adequate GPU memory.
# ⚠️ Please make sure your EnergyPlus version is 9.6 for successful running.

import torch
from transformers import (
    AutoModelForSeq2SeqLM, 
    AutoTokenizer,
)

# Load the rest port of IDF file.
file_path = "v1_nextpart.idf"
output_path = "v1_final.idf"

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

# Generation config
generation_config = model.generation_config
generation_config.max_new_tokens = 2000
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

# Please provide your input here — a description of the desired building
# For more details, please refer to the paper: https://doi.org/10.1016/j.apenergy.2024.123431
input="Simulate a building that is 30.00 meters long, 15.00 meters wide, and 3.50 meters high. The window-to-wall ratio is 0.28. The occupancy rate is 8.00 m2/people, the lighting level is 6.00 W/m2, and the equipment power consumption is 8.80 W/m2."
input_ids = tokenizer(input, return_tensors="pt", truncation=False)
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 = generated_output.replace("_", " ")
generated_output = generated_output.replace("|", "\n")

with open(file_path, 'r', encoding='utf-8') as file:
    nextpart = file.read()
final_text = nextpart + "\n\n" + generated_output
with open(output_path, 'w', encoding='utf-8') as f:
    f.write(final_text)
    
# Output the building energy model in IDF file
print(f"Building Energy Model Auto-Generated: {output_path}")
```

## 📝 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     = {EPlus-LLM: A large language model-based computing platform for 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}}

@article{jiang2025EPlus-LLMv2,
  author    = {Gang Jiang and Jianli Chen},
  title     = {Efficient fine-tuning of large language models for automated building energy modeling in complex cases},
  journal   = {Automation in Construction},
  volume    = {175},
  pages     = {106223},
  year      = {2025},
  month     = {July},
  doi       = {https://doi.org/10.1016/j.autcon.2025.106223}}
```