Inspiration
We were inspired by the urgent need to reduce energy waste in commercial and residential buildings. Buildings account for almost one-third of global energy use, and small inefficiencies like faulty HVAC systems or unoptimized scheduling can add up to massive energy losses. By predicting short-term load patterns and spotting anomalies early, we saw an opportunity to help facility managers make smarter, greener decisions.
What It Does
SmartLoad predicts short-term building energy load, detects anomalies, and translates forecasts into actionable optimization insights.
Key features:
- Forecasting energy load for the next hours/days
- Detecting anomalies like unusual spikes or faulty equipment
- Providing optimization recommendations (shift loads to renewable hours, detect wastage, estimate fossil fuel reduction)
- Offering an interactive dashboard with explainability and actionable metrics
How We Built It
We followed a structured workflow:
Data Preparation
- Collected time-series data (energy meter readings, weather data, occupancy metadata).
- Standardized timezone, units (kW vs kWh), and ensured sensor calibration.
- Applied normalization strategies (per-meter scaling, rolling quantiles).
- Collected time-series data (energy meter readings, weather data, occupancy metadata).
Modeling Approach
- Built baseline models first (Persistence, Seasonal Naive, ARIMA).
- Added feature-based models (XGBoost, LightGBM) with lag and time-of-day features.
- Integrated deep learning models (LSTM, Temporal CNN).
- Benchmarked with Time Series Foundation Models (Chronos, TimesFM, Moirai).
- Performed backtesting with rolling windows, defined retrain frequency, and measured MAE/RMSE.
- Built baseline models first (Persistence, Seasonal Naive, ARIMA).
Anomaly Detection
- Used residual-based anomaly detection on forecast errors.
- Designed a triage process to validate anomalies with facility managers and reduce false positives.
- Used residual-based anomaly detection on forecast errors.
Explainability Layer
- Integrated SHAP values and attention heatmaps to explain why consumption spiked.
- Enhanced trust by making the model interpretable for critical decisions.
- Integrated SHAP values and attention heatmaps to explain why consumption spiked.
Interactive Dashboard
- Built using Streamlit / Dash / Power BI-style layout.
- Dashboard features:
- Forecast vs actual load visualization
- Real-time anomaly alerts
- CO₂ emission reduction estimates
- Actionable optimization insights
- Forecast vs actual load visualization
- Built using Streamlit / Dash / Power BI-style layout.
Resource Considerations
- Managed GPU requirements for TSFMs.
- Balanced accuracy vs latency for production readiness.
- Managed GPU requirements for TSFMs.
Privacy & Security
- Ensured proper handling of occupancy and sensor data.
- Applied governance for sensitive metadata.
- Ensured proper handling of occupancy and sensor data.
Challenges We Ran Into
- Handling data quality issues: missing values, misaligned timestamps, inconsistent units.
- Choosing the right normalization strategy for generalizing across different building types.
- Managing model complexity vs resource constraints — TSFMs are powerful but GPU-heavy.
- Designing anomaly validation loops with facility managers to reduce false alarms.
- Maintaining balance between explainability and accuracy.
Accomplishments We Are Proud Of
- Built a pipeline that goes from raw data → forecasts → actionable insights.
- Successfully benchmarked TSFMs against classical ML models.
- Added explainability (SHAP, heatmaps) to make energy decisions more trustworthy.
- Designed a dashboard that is intuitive, fast, and production-ready.
- Created a system that can directly contribute to sustainability goals.
What We Learned
- The importance of baseline models before moving to complex deep learning.
- Why MAPE fails near zero loads and why MAE/RMSE are more robust.
- How to calibrate probabilistic forecasts for reliability.
- The power of ensembling tree-based and deep models for robustness.
- The need for close collaboration with domain experts (facility managers) to validate anomalies.
What’s Next for SmartLoad
- Scale SmartLoad to thousands of buildings across different regions.
- Integrate real-time weather and renewable generation forecasts.
- Automate anomaly feedback loops with facility managers.
- Extend optimization to include dynamic pricing and demand-response programs.
- Open-source a lightweight version for universities and small businesses.
