Energy Load Forecasting

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  Forecasting Energy. Detecting Anomalies. Driving Decarbonization.

Inspiration

Buildings consume nearly one-third of global energy and emissions. With rising demand and renewable integration, accurate forecasting is essential for reducing carbon footprints. Our inspiration was to create an intelligent, scalable solution that empowers sustainable energy management while detecting anomalies in real time.

What it does

Our system uses Time Series Foundation Models (TSFMs) to forecast short-term building energy consumption and detect anomalies such as unusual spikes or system faults. The results are visualized in a Streamlit dashboard, where users can interact with load curves, monitor anomalies, and plan energy usage effectively.

How we built it

1. Preprocessed datasets using Pandas & NumPy.

2. Trained and fine-tuned TSFMs (PyTorch, HuggingFace Transformers) for forecasting.

3. Implemented anomaly detection with statistical thresholds and residual analysis.

4. Developed a FastAPI backend to serve forecasts and anomaly results.

5. Built an interactive Streamlit dashboard with Seaborn & Matplotlib visualizations.

6. Deployed the solution on GitHub + Streamlit Cloud for easy access.

   Challenges we ran into

7. Handling missing/noisy energy data.

8. Balancing model generalization across both residential and commercial buildings.

9. Integrating TSFM inference into a real-time API with minimal latency.

10. Ensuring visual appeal while keeping the dashboard lightweigh

    Accomplishments that we're proud of

11. Building a complete end-to-end pipeline from raw data → forecasting → anomaly detection → visualization.

12. Successfully fine-tuning TSFMs for building-level energy data.

13. Creating an accessible, user-friendly interface for non-technical stakeholders.

    What we learned

14. How foundation models can generalize across domains like energy systems.

15. The importance of clean data pipelines in time-series forecasting.

16. Deployment strategies combining FastAPI and Streamlit effectively.

    What's next for Energy Load Forecasting

    Expanding datasets with real-time IoT sensor integration.

Improving anomaly detection with explainable AI methods.

Scaling deployment for smart cities and utility providers.

Adding forecasting support for renewable sources (solar, wind).

Built With

• fastapi
• github
• huggingface
• matplotlib
• numpy
• pandas
• python
• pytorch
• seaborn
• streamlit
• uvicorn