Predictive Insights for the Manufacturing Industry

In the manufacturing sector, early detection of energy usage anomalies is key to preventing downtime and optimizing maintenance. Together with SenseNode, we developed an AI-based solution that analyzes time series data to detect unusual patterns – and in the next step, predict anomalies before they occur.

By training the model to understand its own normal behavior, we can identify both sudden anomalies and gradual changes in energy usage. This opens new opportunities for proactive monitoring and more efficient energy management across industrial operations.

What we did

• Analyzed energy consumption data from manufacturing processes

• Built an AI model that detects and classifies anomalies in time series data

• Laid the groundwork for predictive maintenance based on energy patterns

Business value

• Prevents production disruptions and unexpected downtime

• Provides insights to optimize grid operation and capacity planning

• Reduces energy waste and costs through quicker response to inefficiencies

This project shows how AI and time series data can unlock real business value – and take industrial energy management to the next level.

We code the future – by spotting problems before they happen.