Smart grid company
Preventive maintenance
Problem
The client's Network Operations Center received diverse inputs from their end clients, including ambient temperature, humidity, and machine readings. They required a system to assist in decision-making to optimize power utilization efficiently.
Solution
An augmented intelligence system was developed, incorporating a proprietary analytics framework to monitor data elements. The solution featured a machine learning model to predict load balancing requirements across different clients, optimizing energy harvesting. Additionally, a rule engine was implemented to ensure conditions were maintained without compromise.
Outcome
The system significantly enhanced the performance of the Network Operations Center, enabling smarter decision-making and improving operational efficiency. This resulted in substantial cost savings while maintaining reliability and meeting client-specific energy requirements.
Optimizing Network Operations and Energy Utilization with Augmented Intelligence
The client’s Network Operations Center (NOC) received diverse data inputs from their end clients, primarily breweries, including ambient temperature, humidity, and machine readings. An augmented intelligence system was required to assist in decision-making, optimize power utilization, and balance the load on the grid. A rule engine was developed to ensure that critical conditions specific to the breweries were maintained. Additionally, a proprietary analytics framework was used to monitor data elements, and a machine learning model was built to predict the load balancing required across clients for optimized energy harvesting.
The implementation of the augmented intelligence system resulted in over a 20% improvement in NOC performance. This enhancement is expected to generate annual savings of over 200K Euros, significantly reducing operational costs while optimizing power utilization across the network.
Augmented Boost, Big Savings
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