The client is Europe's energy producer with operations spanning across several hubs in the region. They have been at the forefront of the industry, providing innovative and sustainable solutions to meet the world's energy needs.
Their focus on digital transformation and clean energy has positioned them as a key player in driving the transition to a low-carbon future.
The business challenges faced by the client were quite complex and technical. They needed to maintain, expand, and refine their predictive anomaly detection models across several hubs. However, they were struggling to reduce the time and effort required for model building and deployment while maintaining high-quality development and accuracy.
Additionally, they had to deal with large volumes of historical data, which needed to be pre-processed and retrained to improve model accuracy. Furthermore, the client had to modify their historical data access through Azure-based cloud services to enable efficient model deployment across all hubs.
To expedite model building, the Codvo team received a large export of historical data from 2018 to present. After conducting an in-depth analysis of the data, the team decided that autoencoders would be the right fit for the client's use case.
The team made improvements to standard data pre processing and retraining, iterations on the reporting notebooks and grid search, and refined model acceptance criteria to streamline deployment. The team also modified historical data access through client IT systems on Azure and planned to expand to new hubs.
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The Codvo team used Python NumPy and Pandas, TensorFlow, PyTorch, Docker, Jupyter Notebook, Azure, and Bitbucket to build and deploy the predictive anomaly detection models.
-Achieved 30% reduction in model development and deployment time, accelerating anomaly detection capabilities
-Improved model accuracy and reliability by leveraging autoencoders and historical data optimization
-Enabled seamless integration with Azure-based infrastructure, streamlining deployment across multiple regional hubs
-Established a scalable, automated anomaly detection framework, reducing manual intervention and operational risk
-Expanded anomaly detection capabilities to new energy hubs, supporting the client’s growth and digital transformation goals
-Enhanced early detection of system inefficiencies and failures, leading to improved energy utilization and reduced downtime
