How Self Service Analytics Impact Energy Management Efficiency

How Self Service Analytics Impact Energy Management Efficiency

Energy CIO Insights | Thursday, November 26, 2020

Self-service analytics tools allow energy firms to accurately analyze, monitor, and predict process and asset performance accurately, helping them meet the organizational carbon-footprint objectives.

FREMONT, CA: The energy sector is confronted with high-impacting measures due to decarbonization programs. This fuels the change toward renewable energy but also asks for measures to develop carbon-neutral power. Part of the solution is reducing the energy required in power generation plants and making those plants more efficient. Improving overall equipment effectiveness and reducing the energy in utilities required for generating energy are two major areas. With this, it has become clear that companies need to take their efforts to the next level by monitoring and optimizing energy use in real-time and leveraging industrial internet of things (IIoT)-generated data. One of the best ways to leverage innovations is to apply advanced industrial analytics to sensors-created production data. Every data provides unprecedented opportunities for improving energy efficiency.

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Process engineers are in charge of the design, implementation, control, and optimization of industrial processes. They are involved in analyzing, upgrading, modifying, and optimizing equipment and production processes. If the production process is under-performing, they should figure out why, but also in the shortest time possible to eliminate production losses, maintain product quality, and elude high maintenance costs. Implementing self-service analytics allows engineers to get more robust and faster insights into their operational production data. It lets them identify new areas for performance optimization with advanced analysis capabilities, monitor production to prevent abnormal situations, and even predict future evolutions of batch runs, transitions, or equipment startups.

Because the process contextualizes asset performance, it functions in, and best performance windows need to be subtracted from actual process behavior rather than theoretical data. Historical data can be created to monitor behavior, optionally with an energy consumption focus. Live operational performance data can be leveraged for predictive analytics for performance downstream caused by behavior upstream. Early warnings can also be created and designed to trigger actual problem situations, eliminating false positive alarms triggered by measurement noise or spikes in the data.

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