As we shift from fossil fuels to renewable energy sources, having access to up-to-date, real-world data is more important than ever. This data contributes to energy planning, academic research and energy policy analysis, as well as consumers’ ability to participate in energy matters.
Databases like RateAcuity store relevant, accurate information that helps people understand what has happened in the past, specifically regarding electric rates. But these vast amounts of data are useless unless they’re analyzed and used to predict what might happen in the future. That’s why predictive analytics is so important.
More and more organizations are using predictive analytics to increase their revenue, reduce risk and stand out in a competitive market. This kind of forward-thinking helps a variety of industries, from financial services to medical providers to energy production. In the energy field, predictive analytics helps grid operators, systems engineers, controllers and other plant personnel take advantage of massive amounts of data and use it to make real-time decisions that have a positive impact on equipment reliability and maintenance. It can also help power utilities monitor assets to identify, diagnose and prioritize equipment issues in real time.
Here’s just one example: Duke Energy used predictive asset analytics software to detect an early warning in one of their steam turbines. Since they were able to identify and remedy the issue early on, they saved more than $4.1 million by preventing additional damage to the equipment and extended loss of power generation.
There’s a major opportunity here for more utilities to bring their data to life, delivering energy-saving advice to consumers and businesses everywhere so they can spend less and operate more efficiently. Today consumer’s already want more insight into their energy usage, and it’s a need and desire that will only increase over time. The more informed we are, the better we can become—consuming less energy, saving more money and helping our environment in the long haul.