EPlus-LLM
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EPlus-LLMv2

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EPlus-LLM commited on Mar 22, 2025

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----
-base_model:
-- google/flan-t5-xxl
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
-## Model Details
-### Model Description
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-## Uses
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
-### Training Data
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-#### Summary
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
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+# EPlus-LLM
+**EPlus-LLM series, natural language for auto-building energy modeling via LLM**
+<div align="center">
+  <img src="/figs/graphic.png" alt="Illustration of EPlus-LLMv2 for Auto-building energy modeling" width="700"/>
+</div>
+## 🎉 News
+- ⚡️ [2025/01/01] (update #2): We release EPlus-LLMv2, successfully addressing the challenge of auto-building energy modeling (ABEM) in complex scenarios. The new version of the platform supports a wide range of modeling scenarios encountered in real-world building applications, significantly enhancing its breadth and flexibility. Based on comprehensive datasets and a large-scale LLM, we integrate techniques such as LoRA, mixed precision training, and model quantification to reduce computational burden and achieve efficient fine-tuning (without compensating performance).
+[Paper coming soon](https://doi.org/10.1016/j.apenergy.2024.123431).
+- 🔥 [2024/05/016] (update #1): 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 complex EnergyPlus models, including varying geometries, materials, thermal zones, hourly schedules, and more.
+- Accuracy & Efficiency: Achieves 100% modeling accuracy while reducing manual modeling time by over 98%.
+- Interaction & Automation: A user-friendly human-AI interface for seamless model creation and customization.
+<div align="center">
+  <img src="/figs/v2_paltform.png" alt="Description" width="600"/>
+  <p><em>A user-friendly human-AI interface for EPlus-LLMv2.</em></p>
+</div>
+- Flexible Design Scenarios:
+  ✅ Geometry: square, L-, T-, U-, and hollow-square-shaped buildings
+  ✅ Roof types: flat, gable, hip – customizable attic/ridge height
+  ✅ Orientation & windows: custom WWR, window placement, facade-specific controls
+  ✅ Walls & materials: thermal properties, insulation types
+  ✅ Internal loads: lighting, equipment, occupancy, infiltration/ventilation, schedules, heating/cooling setpoints
+  ✅ Thermal zoning: configurable multi-zone layouts with core & perimeter zones
+<div align="center">
+  <img src="/figs/v2_prompt-model.png" alt="Prompt-Model Description" width="600"/>
+  <p><em>The relationship between the prompt and the model.</em></p>
+</div>
+## 🏗️ 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.
+<div align="center">
+  <img src="/figs/v2_example1.png" alt="Examples of EPlus-LLMv2" width="600"/>
+  <p><em>EXample scenarios of EPlus-LLMv2.</em></p>
+</div>
+## 🚀 Quick Start
+This repository contains v2 and v1 of EPlus-LLM, along with implementation details for the ABEM reference.
+📂 Repository Structure
+```
+    ── README.md                           # Project documentation
+    ── v2                                  # V2 model for complex ABEM scenarios in real-world
+    ── v1                                  # V1 model for simple ABEM scenarios
+    ── requirements.txt                    # Dependencies for this project
+```
+🔧 Installation
+- Clone the repository:
+```
+    git clone https://github.com/Gangjiang1/EPlus-LLM.git
+    cd EPlus-LLM
+```
+- Install required dependencies:
+```
+    pip install -r requirements.txt
+```
+▶️ Running Auto-Building Energy Modeling via EPlus-LLM
+```
+    cd v2
+    python EPlus-LLM/v2/Inference.py
+```