Faberwork's use of TensorFlow AI boosts smart building efficiency.
By integrating TensorFlow, we transformed manual processes into automated solutions, resulting in more effective sustainable building management.
Client
The client is a smart building energy management company seeking an advanced IoT management system to minimize cost and control environmental impacts.
Challenges
Faberwork customers relied heavily on experience gained with hands-on reviews of their system’s past performance. This manual procedure proved inadequate as the network grew and different types of controllers were added.
The growing scale and complexity of the business led to increased costs and additional operational risk for the firm.
Solution
Faberwork, working with its client, created a new artificial intelligence solution to replace the manual processes. TensorFlow, an open-source library of AI models, was employed to meet the need for innovation. The TensorFlow model carefully mined through the customer’s past data and collected information on the network's endpoint statuses and controller types.
Once the neural network was trained on the customer’s historical data, the AI tool could accurately predict the system's behavior over time. The client’s monitoring of its smart building and energy network improved significantly.
The model swiftly navigated through large data lakes and provided decision-oriented messages. Essentially, the use of TensorFlow-assisted modeling increased operational agility in Energy Management.
Results
The implementation of AI modeling by Faberwork yielded important outcomes:
- Greater Efficiency: The client upgraded its operational effectiveness as manual examination of the network was transitioned to automation. Deploying AI tools to facilitate endpoint reputation identification enabled teams to concentrate on strategic assignments and systematic enhancements to the model.
- Better Precision: Due to its state-of-the-art algorithms, the TensorFlow-assisted AI model could identify false negatives and minimize false positives for specific endpoint types.
- Scalability Assurance: The AI model is scalable and adaptable despite continued growth and change within the customer’s community. This has guaranteed the client’s ability to manage network monitoring as new types of controllers are introduced.
- Cost Reduction: Faberwork’s client experienced considerable cost savings as it minimized its dependence on manual analysis and optimized operational processes. Machine learning software increased the client's return on investments (ROI).
By embracing AI-driven solutions, Faberwork's client has transformed its approach to endpoint status identification, paving the way for enhanced efficiency, precision, and scalability in Energy Management.
"With our AI model, the client’s building management became more energy efficient and environmentally favorable while improving profitability."
—Madhusudhan Jangid, Senior Software Engineer, Faberwork
