Renewable energy firms should boost spending on data analytics-based asset management to come up with potential cost reductions and revenue improvements.

FREMONT, CA: Aging infrastructure, the growing cost of power generation, and the need to stay in tandem with the evolving technologies are all intensifying the pressure on renewable energy companies to invest in maintaining, growing, and modernizing their service delivery assets. The asset management program needs to achieve the necessary capability, and analytics is a perfect aid for this.

As the costs of remote-monitoring and analytical software come down, there are promising opportunities for renewable companies to make their asset management more productive. Advanced analytics leveraging performance data and predictive algorithms, can help firms prevent asset failures, focus on asset management efforts, avoid excess maintenance work or premature asset replacements, and utilize the valuable experience of in-house experts. Analytics allows asset operators to gain visibility and control over operational risks and asset management practices and to use insights to have a more constructive conversation with regulators.

For renewable energy, real-time analytics enable operators to avoid using assets needlessly and rest assets faster, resulting in more power output due to increased usability. Automated analytics results in optimum asset usage with a consistent, automatic review of event data to drive decisions. The use of analytics makes monitoring the performance of renewable energy assets more efficient, and operators and engineers are freed from regular tasks and can focus on activities that increase asset performance and uptime.

Today, renewable energy firms can manage assets more efficiently, thanks to improvements in technology. Having the analytical tools for processing, interpreting, and responding to data from equipment makes it possible for renewable companies to retire the old model of relying on specialists to set maintenance schedules based on experience in favor of flexible, streamlined, and analytically rigorous approach that uses real-time performance data and predictive models to guide asset management decisions.

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