AI in Homes
NEO AI Approach
In our platform, we are solving a supervised regression and recommendation problem for residential energy optimization using demand-side management.
Goal:
to predict and recommend technical and financial actions for homeowners that will reduce energy consumption, optimize expenditures, and lower greenhouse gas emissions, based on the unique characteristics of each home.
Core Methods and Technologies:
• Supervised Learning Algorithms:
Our solution employs advanced supervised machine learning algorithms—including ensemble methods (such as Random Forests and Gradient Boosted Trees), deep neural networks (DNNs), and regression models (e.g., linear regression, regularized regression)—to model complex relationships between home characteristics and energy performance outcomes.
• Feature Engineering:
Significant domain-specific feature engineering is performed to extract meaningful variables from raw input data, such as calculating Heating Degree Days (HDD), quantifying building envelope quality, and normalizing energy consumption per square meter.
• Recommendation Systems:
The ML engine is integrated with a rules-based recommendation layer that combines the model’s predictions with up-to-date incentive, budget, and technology data to generate personalized upgrade pathways for each home.
• Technologies Used:
Python (scikit-learn, XGBoost, TensorFlow/Keras), pandas for data wrangling, and cloud-based deployment tools for scalable model hosting and inference.
Inputs:
• Home and climate characteristics:
o Heating Degree Days (T-HDD)
o Area of floors (TA-Floors)
o Measured energy consumption (ED-Energy)
o Building envelope properties (ET-Envelop)
o Appliance/system efficiency (EF-Efficiency)
o Required heating/cooling rates (RR-Req. Rates)
Outputs:
• Energy Intensity (EI): Predicted energy usage per unit area or per household.
• Savings: Estimated potential financial savings from recommended retrofits or operational changes.
• Expenditure: Predicted cost required to achieve those savings.
• Recommended actions: Specific technical and financial interventions prioritized based on homeowner’s budget, available incentives, and current technology.
Approach to Validation:
• Cross-Validation and Train/Test Splits:
The model is validated using k-fold cross-validation and independent test sets to ensure robust predictive performance.
• Benchmarking:
Model predictions are benchmarked against actual retrofit outcomes from both the NRCAN public dataset and subsequently from proprietary energy advisor datasets as the platform grows.
• Error Metrics:
Standard regression metrics such as RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), and R² (coefficient of determination) are used to assess prediction quality.
• Real-World Pilot Testing:
Recommendations are piloted with selected energy advisors and homeowners. Their outcomes and feedback are looped back into the model for ongoing improvement.
• Continuous Learning:
As more real-world data becomes available, the system supports periodic retraining and performance monitoring, ensuring continual adaptation and improvement.
Conclusion:
This approach ensures the platform remains accurate and actionable as it scales, while enabling data-driven, measurable impacts on home energy efficiency and cost reduction. The platform’s modular architecture also allows for the integration of new ML methods (e.g., reinforcement learning for dynamic energy management) and IoT data streams in future development phases.
