Posted by Matt Sykes – 23.05.19
Today’s data analytics industry is enabling energy and utility companies to use data to make more consistent, informed and strategic decisions. Automated data analytics provides detailed, real-time information and the necessary details to effectively manage the growth of renewable energy generation.
Matt Schnugg, senior director of data and analytics at GE Power Digital believes that data is a real challenge for the future energy market. Data has the power to control and enhance energy assets that are critical for the transformation of the global energy market.
Data analytics now offer a wide range of solutions, from delivering new services, saving money for customers through to digital simulations to improve new systems before they are introduced. Energy professionals are now utilising high-quality data streams and applying this information to many parts of a business.
One significant benefit of data analytics is the considerable savings it offers companies in the form of predictive maintenance. For example, new data streams have saved Duke Energy over $120 millionbe effectively predicting equipment failures before they occur. The growth of interconnected systems and available information through the cloud are enabling a series of new data analytics applications to be developed.
According to a recent study by energy research company Zpryme and ABB, over 90% of energy and utility leaders believe data analytics is essential to the future success of their business, but only just over 20% are making investment decisions based on predictive analytics. The study indicates the existing imbalance between the considerable growth of available data and the actual capabilities of assimilating and utilising this data effectively.
Some energy and utility businesses have started to progress their business activities by developing uses for data that requires more complex and detailed analytics. Exelon Utilities has created a number of new cases for system-integrated data analytics and continues to expand its portfolio.
According to GE, specific cases of data analytics can be split into asset-based analytics and network-based analytical applications. Predictive maintenance of assets can lower the impact of failures and improvements to networks can improve locating assets and enhance protection of energy assets.
Exelon and GE have collaborated on network analytics to enhance storm readiness and believe that the system saves approximately $590,000 per 500,000 meters each year, primarily from preventing power outages.
Siemens Mindsphere big data platform involves asset and network analytics. By using data mining and analysis tools, Siemens can select the perfect location, size, time and other useful features related to grid infrastructure to provide the most effective investment return for utilities. The system is capable of efficiently integrating utility-scale renewable energy and distributed energy resources.
Data analytics has already proven to be valuable for utilities and energy businesses and is starting to enhance customer engagement. The next stage for the market is progressing advanced analytics and implementing it into AI systems. According to many energy analysts, big data will be critical for renewable and distributed energy, integrating them into a secure, reliable and affordable system.
In order to reach this level, Zpryme believes that energy companies need to boost their focus on hiring digital professionals. For example, Duke Energy highlights that data management and data science are now essential parts of the future of their business. The company employs a dedicated team of data scientists that are continually innovating data and a Machine Learning, Artificial Intelligence and Deep Learning Lab that utilises new technology to manage big data processes.
The analytics team at Exelon has increased from only 2 in 2015 to over 10 this year. Exelon has a vision of creating a connected community and is focusing on becoming a data-driven business with a team of people who understand their data and how to apply it to make strategic decisions for the future.
Posted by Matt Sykes – 23.05.19