Smart Building Energy Forecasting with Anomaly Alerts

🌟 Inspiration

With rising energy demand, buildings are becoming major contributors to electricity usage and carbon emissions. We were inspired to create a solution that not only predicts future energy needs but also detects unusual consumption patterns. This ensures cost savings, equipment safety, and supports sustainability.

⚡ What it does

Our system forecasts hourly and daily energy consumption using AI models and flags anomalies such as unexpected spikes, drops, or abnormal seasonal usage. A real-time dashboard displays predictions, anomalies, and sends automated alerts to facility managers, enabling smarter and more efficient building operations.

🛠️ How we built it

Data Collection & Preprocessing: Gathered historical load data, weather details, and occupancy info. Cleaned missing values and engineered features like lagged data and moving averages.

Model Development: Built forecasting models using TSFM, ARIMA, Prophet, LSTM, and Transformer networks to capture short-term and seasonal patterns.

Anomaly Detection: Compared predicted vs. actual usage, flagged deviations beyond set thresholds, and categorized them as spikes, drops, or seasonal irregularities.

Visualization: Designed an interactive dashboard using Streamlit/Plotly to show predictions, anomalies, and send real-time alerts.

Continuous Learning: Enabled periodic retraining to adapt to new data and changing usage patterns.

🚧 Challenges we ran into

Dealing with incomplete and noisy datasets.

Achieving a balance between forecasting accuracy and computational efficiency.

Designing a flexible anomaly detection mechanism that adapts across multiple building types.

Integrating real-time data sources like weather APIs.

🏆 Accomplishments that we're proud of

Developed a scalable forecasting and anomaly detection pipeline.

Built a user-friendly dashboard for managers to take quick actions.

Reduced prediction error significantly with deep learning models.

Contributed to energy savings and sustainability goals.

📚 What we learned

How to apply time series forecasting with advanced models like Transformers.

The impact of feature engineering on prediction accuracy.

Importance of real-time visualization and alerts for practical usability.

How smart energy solutions can directly help reduce carbon emissions.

🔮 What's next for Smart Building Energy Forecasting with Anomaly Alerts

Scaling the solution to support city-level smart grids.

Enhancing anomaly detection with unsupervised ML and autoencoders.

Adding predictive maintenance insights for building equipment.

Integrating renewable energy forecasting for better demand-supply matching.

Built With

• dash
• jupyter-notebook
• matplotlib
• numpy
• openweathermap
• pandas
• plotly
• prophet
• python
• pytorch
• scikit-learn
• seaborn
• statsmodels
• streamlit
• tensorflow