Collaborating Authors

Power Industry

Which machine learning / deep learning algorithm to use by problem type


I like to approach algorithms from the perspective of problem solving. I created this list from a Mc Kinsey document (link below). Predict a sales lead's likelihood of closing Simple, low-cost way to classify images (eg, recognize land usage from satellite images for climate-change models).

Power sector seeks to reap benefits and tackle risks from AI applications


AI applications are transforming business operations and processes in the power sector as well as the broader economy, leading to greater cost savings, increased efficiency and new services for consumers. But further developments rely on the ability to foster and support innovation, addressing outstanding matters related to investments, data access and governance, as well as ethics. By 2025, 81% of the energy companies will have adopted artificial intelligence, reaping the numerous benefits of accelerated developments in this field and fast tracking the clean energy transition. This is according to an assessment released by Eurelectric, AI Insights: The Power Sector in a Post-Digital Age. First, AI can enable a faster decarbonisation of the power sector.

Siemens providing long-term gas-fired turbine AI and machine learning upgrades for Jebel Ali power plant in Dubai


Siemens Energy will supply new controllers and other major upgrades as part of an extended service agreement for a Dubai power plant. Dubai Electricity and Water Authority (DEWA) signed Siemens to a new, 20-year long-term service agreement. The service term calls for a wide array of upgrades and supply of new technologies. Among those, Siemens Energy will supply an intelligent controller for each of the four SGT5-4000F gas-fired turbines at the Jebel Ali L2 power and water station. This includes the SPPA-T3000 control system, as well as services for the plant's generators and tools to improve operational flexibility and reduce outage times.


Communications of the ACM

The original version of this paper is entitled "SATURN: A thin and flexible self-powered microphone designed on the principle of triboelectric nanogenerator" and was published in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2.2 (60), 2018, ACM

Wireless charging on the moon


A company called WiBotic, which makes advanced wireless charging and fleet energy management solutions for technologies like drones and industrial robots, announced a major partnership to develop wireless charging solutions for robots on the moon. WiBotic will join in the $5.8 million contract with space robotics company Astrobotic, Bosch, and the University of Washington as part of NASA's'Tipping Point' program. "We're thrilled to have been selected by Astrobotic and NASA to deliver wireless charging capabilities to the next generation of lunar vehicles," says Ben Waters, CEO and co-founder, WiBotic. "While WiBotic specializes in wireless charging for military, industrial and commercial robots in all sorts of punishing environments here on Earth – from large warehouses to dusty deserts and corrosive saltwater – this is our first chance to take our technology into space. We're excited to work closely with NASA and be part of the next chapter of space exploration."

Pittsburgh reinvents itself as an urban innovation hub


Devastated by industrial crisis, America's former "steel city" has reinvented itself as an innovation hub. But today its main challenge is to keep its "One Pittsburgh" promise by ensuring that everybody in its diverse population shares the benefits of new growth. Pittsburgh is back from the brink. A flagship of triumphant industrialisation in the early 20th century, the city has since seen its steel mills decline and then shut down. As the economy lurched from one crisis to another, Pennsylvania's rusting "steel city" became an emblem of decline, like other urban "dead stars" in the rustbelt of America's Middle West. But Pittsburgh never gave up.

How AI can help boost alternative and renewable energy use


Ten years ago, I was engaged in the writing of an energy power grid report that was part of a national initiative to assess the health of our electrical energy grid and its resilience. Assets like wind farms and contemporary fossil and nuclear fuel systems were in place for energy distribution, but to my surprise there was also equipment in the grid that dated back to the 1890s and was still in production. I began to understand the challenges of using renewable energy such as wind and solar when it came to assessing energy supply and demand and ensuring there is enough on-hand energy to power the homes and businesses that are relying on it. When utilities were using gas, coal, or nuclear energy to power the grid, the in-flow of that fuel from its source was consistent, so it was easy to assess supply and demand on any given day and to deliver the energy needed to power homes and businesses. What if the wind gusted to 40 mph one day, and was perfectly still on the next day?

Here are 10 ways AI could help fight climate change


Some of the biggest names in AI research have laid out a road map suggesting how machine learning can help save our planet and humanity from imminent peril. The report covers possible machine-learning interventions in 13 domains, from electricity systems to farms and forests to climate prediction. Within each domain, it breaks out the contributions for various subdisciplines within machine learning, including computer vision, natural-language processing, and reinforcement learning. Recommendations are also divided into three categories: "high leverage" for problems well suited to machine learning where such interventions may have an especially great impact; "long-term" for solutions that won't have payoffs until 2040; and "high risk" for pursuits that have less certain outcomes, either because the technology isn't mature or because not enough is known to assess the consequences. Many of the recommendations also summarize existing efforts that are already happening but not yet at scale.

Wisconsin Utility Turns to AI to Reduce Wasted Power


David Devereaux-Weber uses the Sense home energy monitor app to show the spike in electricity use when turning on the coffee maker in his Madison home. The coffee maker uses about 1 kilowatt of electricity, represented by the largest red circle on the tablet. David Devereaux-Weber installed a Sense home-energy monitor to find potential "energy hogs" in his Madison home. An ongoing study by Alliant Energy using the monitors found the average Wisconsin household could save $90 a year by targeting "always on" electronics. Most Wisconsin households could save $90 a year and slash energy use by selectively unplugging devices that draw power even when not in use, according to a study by Alliant Energy.

Watch Boston Dynamics' Spot robot explore Chernobyl


Boston Dynamics' Spot robot is expanding to its resume every day, and the quadruped can add nuclear power plant exploration and radiation monitoring to the list. Engineers from the University of Bristol recently tested Spot around the Exclusion Zone territory of the Chernobyl Nuclear Power Plant. The Exclusion Zone covers approximately a 1,000-square-mile area in Ukraine surrounding the Chernobyl Nuclear Power Plant, where radioactive contamination is highest and public access and inhabitation are restricted. According to the State Agency for Exclusion Zone Management, this is the first time Spot has been tested there. Spot helped create a 3D map of the distribution of nuclear radiation around the Chernobyl Nuclear Plant.