We partnered with a leading infrastructure client to build an advanced Energy Monitoring and Management System tailored for their high-rise building with over 90 floors. The system integrates directly with the building’s existing Building Management System (BMS) infrastructure and ingests over one million data points per minute from thousands of IoT devices and sensors.

Our team designed a robust ETL pipeline and data architecture to process, normalize, and store massive volumes of real-time and historical energy data. This data is then analyzed for real-time monitoring, trend analysis, and anomaly detection, enabling early identification of faulty devices and unusual energy spikes.

We developed a centralized dashboard that provides a unified view of energy consumption across all floors, systems, and time ranges. Using machine learning algorithms, we implemented models for energy forecasting, anomaly detection, and cluster-based optimization.

Using machine learning algorithms, we implemented models for energy forecasting, anomaly detection, and cluster-based optimization. The solution also supports HVAC system optimization by predicting demand and adjusting operations accordingly.

Our analytics engine compares historical and real-time data to uncover patterns, inefficiencies, and optimization opportunities. The system continues to evolve using feedback loops from real-world experimentation to improve prediction accuracy and decision-making.