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Maintenance and energy efficiency: two sides of the same coin

Maintenance and energy efficiency: two sides of the same coin

The link between bad maintenance and increased energy consumption of electrical assets is an important one to understand, as it can have a significant impact on the cost and efficiency of running a facility or business.

When electrical assets are not properly maintained, they tend to operate at less than optimal levels. This can cause the equipment to work harder and use more energy than necessary, resulting in higher energy consumption. For example, a poorly maintained air conditioning unit may run at a higher duty-cycle to cool a room, causing it to consume more energy than a well-maintained unit.

Another issue that can arise from bad maintenance is equipment failure. When electrical assets are not properly maintained, they are more likely to break down and require repairs or replacement. This can result in costly downtime for a business, as well as increased energy costs as the equipment struggles to function.

Improving maintenance of your electrical assets will not only save you energy costs, but it will also increase the performance of your electrical assets, which can lead to increased productivity and profitability.

Ecocentric Energy’s Numen AI asset monitoring system

The Numen system has been specifically developed by Ecocentric Energy to be a cost-effective, non-intrusive and easy to deploy, data acquisition and analysis platform that makes AI-based condition monitoring of electrical assets a reality in many commercial and industrial environments.

Numen continuously monitors electrical assets for performance degradation and increased energy consumption. This allows operators to vastly improve maintenance efficiencies by implementing predictive maintenance and in so doing also make significant improvements to the energy consumption of electrical assets.

Numen makes detailed electrical data available to users via a modern web interface and application programming interface (API) which further drives energy efficiency and data driven decision making.