A next-generation power plant, for example, can employ machine learning to account for substantially more inputs, allowing for more exact modeling of essential plant operational functions than previously thought conceivable.
FREMONT, CA: Even before COVID-19, renewable energy sources, low gas prices, and ambitious decarbonization targets, all of which are shifting customer preferences, were causing substantial disruption to fossil-fuel power plants. As a result, adopting the newest digital and advanced analytics technology has become crucial as the power-generation industry moves to the next normal.
Many power firms started their digital transitions with data models, which help optimize set points, improve dispatch decisions, and support maintenance plans and operating-mode selection. However, forward-thinking businesses have recently begun to use visualization tools to monitor real-time generation performance and digital control software to send predicted data to control rooms. Nevertheless, because these improvements are based on tangibly enhancing plant operations, they represent only a small component of a digitally enabled, next-generation power plant.
The data of a company is one of its most valuable assets. Developing a fact-based, data-driven culture and discovering how current developments in analytics may translate data into meaningful insights are the first stages in every company's path. Along with revolutionary technologies like artificial intelligence and machine learning, the next generation of digital and sophisticated analytics solutions has arisen. In terms of identifying hidden patterns and complicated interdependencies, such approaches aim to go beyond typical multivariate regression analysis methods.
A next-generation power plant, for example, can employ machine learning to account for substantially more inputs, allowing for more exact modeling of essential plant operational functions than previously thought conceivable. For example, performance-improvement models based on thermodynamic models and OEM set points were once thought to be enough for optimizing a plant's heat rate—the amount of energy required to produce a single kilowatt-hour (kWh)—just a few years ago. Machine learning can now optimize heat rates more efficiently and in a quarter of the time.
To remain ambitious in the global energy supply in the future decade, power plants must enhance their unit efficiency and strengthen the resilience of their operations. Advanced analytics models and solutions have changed the art of power production, and adopting digital solutions in power generation is more important today than it has ever been.
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