"Energy Load Forecasting for Smart Grid"

1. Inspiration

Buildings contribute to nearly one-third (1/3) of global energy consumption, making them one of the largest sources of carbon emissions. With the rising need for sustainable energy and the unpredictability of renewables, we were inspired to build an AI solution that can predict energy demand, detect anomalies, and help reduce carbon footprints.

1. What it does

Our system uses AI-powered time series forecasting to predict short-term energy loads for buildings and industries. It integrates real-time weather data to align renewable energy supply with demand. Additionally, it has an anomaly detection module that identifies unusual consumption patterns — like sudden spikes, equipment faults, or wastage — and alerts users via a dashboard.

1. How we built it

Collected open-source energy datasets and combined them with weather APIs.

Preprocessed data (cleaning, scaling, handling missing values).

Trained LSTM/Transformer models for forecasting.

Implemented anomaly detection using autoencoders and Isolation Forest.

Built an interactive Streamlit dashboard for visualization.

Deployed the prototype using Docker + Google Cloud AI Platform for scalability.

1. Challenges we ran into

Handling incomplete and noisy datasets.

Limited cloud compute credits for training deep models.

Balancing between generalizable forecasting models and building-specific fine-tuning.

Designing an easy-to-use interface within hackathon time limits.

1. Accomplishments that we're proud of

Successfully integrated forecasting + anomaly detection in one system.

Built a working dashboard prototype to visualize predictions in real-time.

Learned to optimize models under tight resource and time constraints.

Created a solution that directly contributes to green energy adoption.

1. What we learned

How to apply AI time series models (LSTM, Transformers) to real-world data.

The importance of feature engineering and incorporating external data (like weather).

Building scalable prototypes with cloud + containerization.

Collaboration and rapid prototyping under hackathon pressure.

1. What's next for Untitled

Scaling to city-wide smart grids with IoT sensor integration.

Adding predictive maintenance alerts for equipment based on anomalies.

Partnering with energy providers for pilot testing.

Expanding the system into a mobile app for households to monitor and reduce energy use

Built With

• actions
• aws-redshift
• azure-ml-#databases:-postgresql
• cloud-functions
• dash
• github
• grafana
• kubernetes
• lambda-#apis:-openweathermap-api
• languages:-python
• mongodb-#cloud-services:-bigquery
• power-bi-#deployment:-docker
• prophet-#platforms:-google-cloud-ai-platform
• pytorch
• rest-apis-#visualization:-streamlit
• sagemaker
• scikit-learn
• smart-meter-apis
• sql-#frameworks:-tensorflow
• statsmodels