Rhino Energy Prediction Plugin

Overview

Rhino Energy Prediction Plugin is designed to support architects in making energy-informed design decisions early in the building process. The plugin enables users to create or modify building models and receive predictions for heating and cooling loads using a machine learning (ML) model. Architects can gauge building energy performance early (concept stage) using the Rhino Energy Prediction Plugin. The plugin embeds a self-contained ONNX runtime directly in Grasshopper.

Features

• Model Initialization
  Reads an ONNX model file path and sets up an InferenceSession that exposes each input tensor’s name, datatype, and shape.

• Real-Time Inference
  Packs Grasshopper inputs into dense tensors, executes the ONNX model, and returns the first element of the output array as an energy load estimate.

• Automatic Feature Extraction
  Companion Python script reads 3D building geometry and computes features such as roof area, window-to-wall ratio, floor area, and number of stories.

• Pure C# Runtime
  Runs entirely in .NET via Microsoft’s ONNX Runtime—no Python interpreter required at inference time.

Architecture

1. Component Initialization
   The plugin reads the ONNX model path from the Grasshopper input.
   It then creates an InferenceSession and retrieves input tensor metadata.

2. Input Packing
   Grasshopper values are loaded into dense tensors that match the ONNX input shapes.

3. Model Inference
   The plugin runs the ONNX model with the packed inputs and receives an output array.
   The first element of that array is sent to the Grasshopper output.

4. Feature Extraction Script
   A Python helper extracts building features automatically by classifying layers named Wall, Slab, Window, and Roof.

Installation

1. Download the VIP_Energy_Plugin folder.
2. Copy it to your Grasshopper Libraries folder:

   C:\Users\YourUserName\AppData\Roaming\Grasshopper\Libraries
   

3. Launch Rhino and open Grasshopper.
4. Drag the VIPPlugin component from the Params tab onto the canvas.
5. Provide the ONNX model file path to the component input.
6. View the energy load prediction on the second output parameter.

Workflow

1. Open Rhino and start Grasshopper.
2. Place the VIPPlugin component and connect the ONNX model path.
3. Sketch or import a building mass in Rhino.
4. Run the feature extraction script to compute geometry parameters.
5. Grasshopper packs the inputs and runs the ONNX model.
6. Inspect the real-time energy load estimate.

Requirements

• Rhino 7+ – Plugin host environment
• Windows OS – .NET and Rhino SDK compatibility
• .NET Framework 4.8+ – ONNX Runtime support
• Python 3.8+ – Feature extraction and model conversion scripts
• ONNX model file – Trained energy prediction model

Tech Stack

• Rhino SDK (C#) – Core plugin development and geometry handling
• Grasshopper (C#) – Dynamic component architecture
• Microsoft ONNX Runtime – High-performance model inference
• Python – Building feature extraction and .joblib → .onnx conversion
• scikit-learn / sklearn-onnx – Model training and conversion

Deploy via Docker locally

• act -j build -P windows-latest=-self-hosted

Roadmap

• Real-Time EUI Feedback
  Provide energy use intensity updates as users modify height, WWR, and story count.

• Flexible Model Inputs
  Detect parameter names and types automatically to support multiple climates and typologies.

• Multi-Format Support
  Add seamless handling of both .onnx and .joblib models with built-in feature mapping.

• Map Integration
  Link with an energy prediction map to import existing building geometry and simulate retrofits.

• Custom Tab
  Give PlugIn standalone VIPEnergy tab so that future energy related plugins that work in the Rhino/GH environment may be added to this parent group.

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