WattsIQ's platform integrates machine learning, real-time data processing, and predictive modeling to deliver intelligent energy management. Our technology stack is designed for scalability, reliability, and continuous improvement.
Our platform employs multiple neural network architectures and statistical models to forecast consumption, detect anomalies, and optimize operations.
LSTM architectures process sequential energy data to identify temporal patterns and predict future consumption. These recurrent neural networks excel at capturing seasonal trends, weekly cycles, and daily routines.
Ensemble methods combine multiple weak learners to produce accurate predictions. XGBoost and LightGBM models handle non-linear relationships between weather, occupancy, and energy demand.
Unsupervised learning algorithms identify unusual consumption patterns that deviate from normal behavior. Isolation forests detect equipment failures, energy theft, and system inefficiencies.
Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) agents learn optimal control strategies through trial and reward. These agents adjust HVAC settings, lighting, and equipment operation to minimize cost and emissions.
WattsIQ's architecture is built on modular, scalable components that integrate data acquisition, processing, modeling, and control.
Smart meters, IoT sensors, and building management systems stream real-time energy data via MQTT, Modbus, and BACnet protocols. Data is validated, normalized, and timestamped before storage.
Apache Kafka handles high-throughput data streams. Apache Spark performs distributed processing for feature engineering, aggregation, and transformation. Time-series databases (InfluxDB) store historical data for model training.
TensorFlow and PyTorch frameworks train deep learning models on GPU clusters. MLflow tracks experiments, model versions, and performance metrics. Models are deployed via TensorFlow Serving for low-latency inference.
RESTful APIs and MQTT brokers send control signals to smart devices and building systems. Rule engines combine AI recommendations with safety constraints and user preferences.
Web dashboards visualize real-time metrics, forecasts, and historical trends. Grafana and custom React applications provide interactive charts, heatmaps, and performance reports.
Effective energy prediction requires rich feature sets that capture temporal, environmental, and operational variables.
Hour of day, day of week, month, and season are encoded as cyclical features. Holiday indicators and business hours flags capture calendar effects.
Temperature, humidity, wind speed, and solar irradiance from Environment Canada APIs enrich models. Forecasted weather enables predictive adjustments.
Occupancy sensors, Wi-Fi client counts, and calendar data estimate building occupancy. Models adjust HVAC and lighting based on actual presence.
Operational state, runtime hours, and efficiency ratings for HVAC units, pumps, and motors inform maintenance predictions and anomaly detection.
Time-of-use rates, peak demand charges, and carbon intensity data enable cost optimization and emissions reduction strategies.
Rolling averages, standard deviations, and lag features capture short-term and long-term consumption trends for improved accuracy.
Rigorous training protocols ensure models generalize well to unseen data and maintain accuracy over time.
Models are trained on historical data spanning multiple years to capture seasonal variations. Cross-validation with time-series splits prevents data leakage and ensures temporal consistency.
Models are evaluated using Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R² scores. Forecasts are benchmarked against naive baselines and industry standards.
SHAP (SHapley Additive exPlanations) values explain feature contributions to predictions. Users receive transparent insights into why specific recommendations are made.
Models are retrained periodically with fresh data to adapt to changing patterns. A/B testing compares new model versions against production baselines before deployment.
WattsIQ implements robust security protocols and redundancy measures to protect data and ensure continuous operation.
All data is encrypted in transit (TLS 1.3) and at rest (AES-256). Access controls enforce role-based permissions and audit logging tracks all data access.
PIPEDA-compliant data handling ensures personal information is collected, used, and stored according to Canadian privacy law. Data retention policies align with regulatory requirements.
Distributed infrastructure across multiple availability zones ensures high availability. Automated failover mechanisms maintain service continuity during outages.
Prometheus and Grafana monitor system health, API latency, and model performance. Automated alerts notify engineers of anomalies or degraded performance.
Automated backups occur daily with point-in-time recovery capabilities. Disaster recovery plans are tested quarterly to ensure rapid restoration.
Continuous integration pipelines run unit tests, integration tests, and model validation before deployment. Canary releases minimize risk of production issues.
WattsIQ connects with existing building systems, smart devices, and third-party platforms through open APIs and standard protocols.
Native support for BACnet, Modbus, and LonWorks protocols enables integration with existing HVAC controllers, lighting systems, and access control.
Compatibility with Google Home, Amazon Alexa, and Apple HomeKit allows residential users to control energy settings via voice and mobile apps.
Connections to utility providers retrieve real-time pricing, carbon intensity, and demand response signals for dynamic optimization.
Integration with Environment Canada, NOAA, and commercial weather APIs provides hyperlocal forecasts for predictive modeling.
APIs for solar inverters (SolarEdge, Enphase) and battery storage (Tesla Powerwall, LG Chem) enable renewable energy optimization.
RESTful APIs and webhooks integrate with ERP systems, facility management platforms, and business intelligence tools for unified reporting.
Access detailed API specifications, integration guides, and model architecture documentation through our technical portal.