Managing the Information Glut
If there is one thing there is no shortage of in the energy sector, itâ€™s information. The sheer volume of data generated in this industry is growing exponentially, and it will only increase as we seek to collect increasingly sophisticated levels of detail about assets and operations.
Fortunately, as information continues to multiply at alarming rates, there are researchers in university and commercial labs working to better aggregate, analyze and distribute massive amounts of data quickly. New advancements â€“ such as Question Answering (QA) technology — are simplifying this complex and costly process using everyday variables such as the human language.Â Question and answering technology will be instrumental in uncovering the value of data and solving tomorrowâ€™s increasing complex business problems caused by the explosion of unstructured data.
The integration of capabilities like natural language processing, information retrieval and reasoning â€” along with massively parallel computation â€” can make QA technology consistently rival the best human performance. In simple terms, it means that a human being can pose a question in natural language to a computer, which will consult volumes of information and then provide a quick and accurate reply.
Deep QA holds significant promise for the energy and utilities industry. Its ability to digest colossal amounts of information through advanced analytics holds the potential to deliver smarter, more sustainable energy and tackle some of todayâ€™s worst roadblocks.
These roadblocks are not trivial. A growing world population, coupled with global climate concerns, urbanization and antiquated systems, are straining current infrastructures. In 2010 smart grid technology came into its own. By September 2010, over two million new smart meters were installed in the U.S., according to the Department of Energy. According to Lux Research, there will be nine times the smart grid data in 2020 than there is today. What can we do with all the information we are collecting at these millions of endpoints?
That is a question Deep QA can answer. The capabilities demonstrated by Deep QA could help increase energy efficiency by assisting personnel working in the field and consumers managing energy use in the home.
For example, by gathering power and grid infrastructure information along with installation, safety inspection and maintenance records, Deep QA could pre-empt maintenance and reduce the impact of outages by helping personnel explore that data to identify errors, or to assist in making decisions about rerouting power and scheduling future inspections to optimize efficiency. This combination of analytics, natural language understanding and fast processing could enhance productivity and limit system downtime by assisting in locating acute conditions that require immediate attention and providing the information needed to diagnose problems, maintain safety, or fix power disruptions â€“ directly to personnel onsite via mobile devices.
In an industry where nearly 50 percent of personnel who operate smart grids are within a few years of retirement, a Deep QA system could help manage knowledge transfer by digitizing and making technical knowledge more accessible and transferable. By harnessing Deep QAâ€™s capabilities, we could create an archival storehouse or library of key processes, procedures and actions and even automate tasks to maintain operational standards during internal changes. This also could help improve the level of self-service in customer-service call centers and help utility crews accelerate storm recovery and repairs.
Deep QA technology also holds great promise for energy consumers. Despite being a commodity, consumers are becoming more aware of how their electricity is being managed and utilized. Deep QA could become a personalized energy assistant that is customized for each home. Consumers could control home energy consumption, receive instant messages regarding potential outages, insight into billing or guidance on how to improve their energy management based on detailed smart meter data, weather information, and other sources. Deep QA could help consumers diagnose and identify problems in their energy management systems and either fix the problem or instruct the consumer or utility worker.
This is not only a valuable resource for consumers, but will also streamline operations and free up personnel resources by addressing customer requests via text, email, SMS and voice enabling technology.
With an estimated 50 million US customers getting connected to smart grid deployments over the next two years and approximately 300 million customers in China over the next five, the information surplus in the energy industry shows no signs of abating. Deep QA technology can help add an extra layer of grid resiliency and modernization to help meet energy demands with more dynamic, responsive, and adaptive systems.
Ron Ambrosio is the Global Research Executive for Energy & Utilities at IBM, which celebrates its centennial anniversary in 2011. Â IBMâ€™s DeepQA system, Watson, uses breakthrough analytics to analyze and interpret massive amounts of data, before providing the best answer to a list of broad questions based on collated evidence. Watson recently beat two human champions on Jeopardy!
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