Artificial Intelligence, or AI for short, is nothing new; it goes way back to the 1950s. But things are different now; the vast volumes of data and the computing capabilities we have today mean we can do things better. So, what pain points are utilities seeing today that AI can help with? Let me share some examples. My first is about how AI can help optimize aging production capabilities while, at the same time, minimizing maintenance costs.
Rising energy demands, fluctuating oil prices, renewable integration, aging infrastructure and changing regulatory requirements are all challenges facing the energy industry today. While multiple approaches exist for addressing these realities, one constant remains -- technology will be at the heart of the majority of solutions. Whether it's sensors and cameras monitoring utility and oil and gas assets, drones that perform high-risk inspection operations, or machine learning tools that identify energy efficiency opportunities, technology innovation is critical for the future of the industry. The shift to smart electricity grids and digital oil fields does not come without risk. The technologies proliferating in the energy industry are also endangering it -- opening up critical systems to cyberattacks.
But as these organizations grapple with growing demand, erratic temperatures, aging infrastructure, and the threat of cyberattacks, many struggle to maintain a high level of service in an uncertain and unpredictable landscape. Artificial intelligence (AI) and machine learning (ML), as powered by big data, have the potential to modernize energy and utilities organizations by identifying ways to reduce waste and redundancy, protect and manage assets, and detect performance anomalies – all while realizing valuable cost savings, both for the organization and the customer. In this blog, we explore the three main areas where AI is making a mark on the energy and utilities sector today and how such investments may impact the future. Each year in the U.S. alone, trillions of gallons of water are lost due to aging pipes, broken water mains, and faulty meters. Replacing the entire system would be massively expensive, time-consuming, and impractical, which means that utility companies must take a localized approach to repairs.
This blog was originally published here. It's become quite clear that the Industrial Internet of Things (IIoT) is the future of Industry. By now we've well and truly covered the point that IIoT is, in fact, not hype. For end users and OEMs IIoT, cloud and big data analytics are creating very real business opportunities. IIoT not only enhances the communication between machines and people – it is facilitating the next wave of value-added customized business services.
The need for online diagnostics in the electric powergeneration industry is driven by a number of significant factors . Due to the low number of new power plants being built by electric utilities, the average age of existing power plant equipment in the United States and its susceptibility to failure is increasing rapidly. Figure 1 shows the percentage of power-generation equipment over 20 years old as a function of year. Note the rapid increase of average age after 1980 and the fact that by the year 2000 fully 50 percent of all generation equipment in the United States will be over 20, the oldest average age of power plant equipment ever experienced by U.S. utilities. Thus, there is a need to know what the actual operating condition of the equipment is at all times, so that outages can be avoided by taking corrective actions at the earliest possible time and by preplanning for outages if they become necessary in order to to minimize their length.