FlatworldEdge developed a centralized Big Data repository for a US-based HVAC manufacturer, enabling the storage and analysis of sensor data. The system also provided automated models for power optimization in the HVAC systems, improving efficiency.

The solution enhanced performance via fault detection and resource optimization, and automated operational controls. As a result, energy consumption and costs were significantly reduced, positioning the manufacturer as a leader in energy efficiency.

Story of the Customer 

The customer, a $15 billion HVAC manufacturer in the U.S., faced challenges in storing sensor data from numerous installations and automating power consumption optimization.

In search of a solution, they needed a system for central data storage, predicting necessary power adjustments, and providing recommendations for global energy optimization.

The Challenge 

  • Storing and analyzing massive volumes of sensor data from multiple HVAC systems for power optimization presented a significant challenge.
  • The task of developing models for automated analysis and real-time decision-making for power optimization was daunting.
  • Optimizing ongoing processes, enhancing performance through fault detection & resource optimization, and automating operational controls were complex tasks.

The Solution 

  • A centralized Big Data repository was developed to store and analyze sensor data, enabling automated predictions for adjustments at sensor, blower, and property levels for global energy optimization.
  • The solution allowed for optimizing ongoing processes through monitoring and verification, enhancing overall performance via fault detection and resource optimization.
  • Operational controls were automated, facilitating real-time decision-making that led to lower energy consumption and costs.

The Result 

  • The implementation of a centralized Big Data repository facilitated automated predictions, optimizing energy consumption.
  • The solution enhanced overall performance with fault detection and resource optimization capabilities.
  • Operational controls were automated, enabling real-time decision making and lowering energy costs.