Wind Generation Forecast Monitor 🌬️

This project is a full-stack web application designed to help users intuitively understand the accuracy of UK national-level wind power generation forecasts. It compares actual generation data with forecasted data fetched from the BMRS API, adjusting for configurable forecast horizons.

Directory Structure

The repository is organized into three main sections:

• /analysis: Contains the Jupyter Notebook (forecast_analysis.ipynb) detailing the error characteristics of the forecast model and recommendations for reliable wind capacity based on first-principles reasoning.
• /backend: A Python FastAPI application responsible for interacting with the Elexon BMRS API (/datasets/FUELHH/stream and /datasets/WINDFOR/stream). It handles data fetching, time-zone standardization, horizon filtering, and merging the datasets.
• /frontend: A React.js application using Chart.js (react-chartjs-2) to provide an interactive dashboard. Users can select date ranges and use a slider to configure the forecast horizon.

🚀 How to Start the Application Locally

1. Backend Setup (FastAPI)

1. Navigate to the backend directory:

   cd backend
   

2. Create and activate a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   

3. Install the required dependencies:

   pip install fastapi uvicorn pandas numpy requests
   

4. Start the development server:

   uvicorn main:app --reload
   The backend will run on http://127.0.0.1:8000
   

2. Frontend Setup (React)

1. Open a new terminal and navigate to the frontend directory:

   cd frontend
   

2. Install the required Node modules:

   npm install
   

3. Start the React development server:

   npm start
   

4. The frontend will run on http://localhost:3000

🔗 Important Links

Live Application (Frontend): [(https://wind-forecast-app-sigma.vercel.app/)]

Live Backend API: [(https://wind-forecast-backend-32c2.onrender.com)]

Demo & Analysis Video: [(https://youtu.be/6VKA-zeY0AU)]

AI Tools Usage Disclaimer

As permitted by the challenge guidelines, AI tools (Google Gemini) were utilized for low-level assistance during development. Specifically, AI was used to help debug strict JSON compliance issues with Pandas NaN values, troubleshoot BMRS API parameter syntax (publishDateTimeFrom), and resolve Chart.js configuration for plotting non-continuous lines (spanGaps). All architectural decisions, analytical reasoning, and data interpretations were derived independently.