Power Industry


Can Artificial Intelligence Save The Nuclear Industry?

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Attitudes about nuclear energy are changing, with pundits on both sides of the aisle touting its benefits for extremely efficient and relatively clean energy. Despite an ever more positive public opinion, the nuclear industry in the United States, the largest in the world, is currently experiencing a downturn, even going so far as to need government subsidies to keep afloat. In fact, at present the fastest growing sector of the nuclear industry is profiting not off of growth, but off of the nuclear sector's slow death in the United States. According to reporting by Bloomberg, "the fastest growing part of the nuclear industry in the U.S. involves a small but expanding group of companies that specialize in tearing reactors down faster and cheaper than ever before." Tearing down old nuclear reactors is no easy feat, however.


AI improves crack detection in nuclear reactors

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As I write this, Futurama's Bender is on my TV expressing his opinions about the flaws of us humans. Although he may take it a little farther than I would, it's true that we don't have the best natural detection capabilities. And when you're talking about detecting structural flaws in something like a nuclear reactor, human error isn't something with which I'd want to take a chance. Luckily, technology is able to help us with this, and it's sure to be much more helpful than Bender the Robot. A system in development at Purdue University is poised to help operators detect cracks and their severity in nuclear reactors, according to a recent article by Chris Adam.


Cleaning up nuclear slay is an glaring job for robots – TheSportMail

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SOME PEOPLE fear about robots taking work far from human beings, however there are a pair of jobs that even these sceptics admit most folk would no longer favor. One is cleansing up radioactive slay, in particular when it's miles internal a nuclear vitality self-discipline--and in particular if the vitality self-discipline in quiz has suffered a recent accident. These that gain contend with radioactive arena topic must first don protective suits that are inherently cumbersome and are additional encumbered by the air hoses wanted to allow the wearer to breathe. Even then their working hours are strictly restricted, in relate to resolve far from prolonged exposure to radiation and because operating in the suits is intelligent. Moreover, some forms of slay are too dangerous for even the besuited to intention safely.


Cleaning up nuclear slay is an glaring job for robots – TheSportMail

#artificialintelligence

SOME PEOPLE fear about robots taking work far from human beings, however there are a pair of jobs that even these sceptics admit most folk would no longer favor. One is cleansing up radioactive slay, in particular when it's miles internal a nuclear vitality self-discipline--and in particular if the vitality self-discipline in quiz has suffered a recent accident. These that gain contend with radioactive arena topic must first don protective suits that are inherently cumbersome and are additional encumbered by the air hoses wanted to allow the wearer to breathe. Even then their working hours are strictly restricted, in relate to resolve far from prolonged exposure to radiation and because operating in the suits is intelligent. Moreover, some forms of slay are too dangerous for even the besuited to intention safely.


Opinion Artificial Intelligence needs to become less and less artificial

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AI (Artificial Intelligence) is everywhere and it's here to stay. Along with these consumer applications, companies across sectors are increasingly harnessing AI's power for productivity growth and innovation. There are many who believe that AI has the potential to become more significant than even the internet. Availability of enormous amount of data combined with huge leap in computational power and huge improvements in engineering skills should help AI, backed with deep learning, to make huge impact across various facets of human life. Amid all the hype, genuine and inflated, around the world of AI, it is pertinent to ask an important question.


With the assistance of artificial intelligence, researchers at Argonne are developing new ways to extract insights about the electric grid from mountains of data, with the goal of ensuring reliability and efficiency. The work combines Argonne's long-standing grid expertise with its advanced computing facilities and experts.

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Researchers at Argonne National Laboratory are working on optimization models that use machine learning, a form of artificial intelligence, to simulate the electric system and the severity of various problems. In a region with 1,000 electric power assets, an outage of just three assets can produce nearly a billion scenarios of potential failure. Argonne researchers apply machine learning to inform more reliable grid planning and operations. The following article is part of a series on Argonne National Laboratory's efforts to use the predictive power of artificial intelligence, specifically machine learning, to advance discoveries in a broad range of scientific disciplines. How much electricity will you need tomorrow?


Machine Learning on EPEX Order Books: Insights and Forecasts

arXiv.org Machine Learning

Forecasting electricity prices is an important task in an energy utility and needed not only for proprietary trading but also for the optimisation of power plant production schedules and other technical issues. A promising approach in power price forecasting is based on a recalculation of the order book using forecasts on market fundamentals like demand or renewable infeed. However, this approach requires extensive statistical analysis of market data. In this paper, we examine if and how this statistical work can be reduced using machine learning. Our paper focuses on two research questions: - How can order books from electricity markets be included in machine learning algorithms? - How can order-book-based spot price forecasts be improved using machine learning? We consider the German/Austrian EPEX spot market for electricity. There is a daily auction for electricity with delivery the next day. All 24 hours of the day are traded as separate products.


Deep Recurrent Adversarial Learning for Privacy-Preserving Smart Meter Data Release

arXiv.org Machine Learning

Smart Meters (SMs) are an important component of smart electrical grids, but they have also generated serious concerns about privacy data of consumers. In this paper, we present a general formulation of the privacy-preserving problem in SMs from an information-theoretic perspective. In order to capture the casual time series structure of the power measurements, we employ Directed Information (DI) as an adequate measure of privacy. On the other hand, to cope with a variety of potential applications of SMs data, we study different distortion measures along with the standard squared-error distortion. This formulation leads to a quite general training objective (or loss) which is optimized under a deep learning adversarial framework where two Recurrent Neural Networks (RNNs), referred to as the releaser and the attacker, are trained with opposite goals. An exhaustive empirical study is then performed to validate the proposed approach for different privacy problems in three actual data sets. Finally, we study the impact of the data mismatch problem, which occurs when the releaser and the attacker have different training data sets and show that privacy may not require a large level of distortion in real-world scenarios.


Meta-heuristic for non-homogeneous peak density spaces and implementation on 2 real-world parameter learning/tuning applications

arXiv.org Artificial Intelligence

Observer effect in physics (/psychology) regards bias in measurement (/perception) due to the interference of instrument (/knowledge). Based on these concepts, a new meta-heuristic algorithm is proposed for controlling memory usage per localities without pursuing Tabu-like cut-off approaches. In this paper, first, variations of observer effect are explained in different branches of science from physics to psychology. Then, a metaheuristic algorithm is proposed based on observer effect concepts and the used metrics are explained. The derived optimizer performance has been compared between 1st, non-homogeneous-peaks-density functions, and 2nd, homogeneous-peaks-density functions to verify the algorithm outperformance in the 1st scheme. Finally, performance analysis of the novel algorithms is derived using two real-world engineering applications in Electroencephalogram feature learning and Distributed Generator parameter tuning, each of which having nonlinearity and complex multi-modal peaks distributions as its characteristics. Also, the effect of version improvement has been assessed. The performance analysis among other optimizers in the same context suggests that the proposed algorithm is useful both solely and in hybrid Gradient Descent settings where problem's search space is nonhomogeneous in terms of local peaks density.


Artificial Intelligence and International Security: The Long View Ethics & International Affairs Cambridge Core

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How will emerging autonomous and intelligent systems affect the international landscape of power and coercion two decades from now? Will the world see a new set of artificial intelligence (AI) hegemons just as it saw a handful of nuclear powers for most of the twentieth century? Will autonomous weapon systems make conflict more likely or will states find ways to control proliferation and build deterrence, as they have done (fitfully) with nuclear weapons? And importantly, will multilateral forums find ways to engage the technology holders, states as well as industry, in norm setting and other forms of controlling the competition? The answers to these questions lie not only in the scope and spread of military applications of AI technologies but also in how pervasive their civilian applications will be.