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renewable energy


Renewables make it into the grid better with AI

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In a highly competitive market, all energy generators rely on highly accurate predictions of how much electricity they'll be able to make. Australian researchers have figured out a way to improve these predictions for wind and solar farms, using artificial intelligence. The National Energy Market – "the grid" – requires automatic forecasts every five minutes from electricity generators. This ensures that electricity generation meets demand. It can be very costly if those five-minute forecasts prove to be incorrect.


Top 10 Post-Covid Tech Trends

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The global pandemic has resulted in the rapid acceleration of the digital transformation of many aspects of our lives--how we work, where we buy, and what new services and products are offered to us. But it and also exposed the inadequacy, inefficiency, and the sheer primitive aspects of the many processes, practices and policies governing the way we live now. Which technologies promise at least some progress in the near future? In an online broadcast to a global audience, leading VC firm OurCrowd released today a list of what it considers the top tech trends in mid-2021, when global venture capital funding reached an all-time high with more than $288 billion invested worldwide in the first half of this year. "There is no better time to analyze what the tech trends are for the smart investor, and where the technology market is moving," said Jon Medved, OurCrowd's founder and CEO, opening the broadcast.


Automating Wind Farm Maintenance Using Drones and AI

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Turbine maintenance is an expensive, high-risk task. According to a recent analysis from the news website, wind farm owners are expected to spend more than $40 billion on operations and maintenance over a decade. Another recent study finds by using drone-based inspection instead of traditional rope-based inspection, you can reduce the operational costs by 70% and further decrease revenue lost due to downtime by up to 90%. This blog post will present how drones, machine learning (ML), and Internet of Things (IoT) can be utilized on the edge and the cloud to make turbine maintenance safer and more cost effective. First, we trained the machine learning model on the cloud to detect hazards on the turbine blades, including corrosion, wear, and icing.


Duke Energy used computer vision and robots to cut costs by $74M

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All the sessions from Transform 2021 are available on-demand now. Duke Energy's AI journey began because the utility company had a business problem to solve, Duke Energy chief information officer Bonnie Titone told VentureBeat's head of AI content strategy Hari Sivaraman at the Transform 2021 virtual conference on Thursday. Duke Energy was facing some significant challenges, such as the growing issue of climate change and the need to transition to clean energy in order to reach net zero emissions by 2050. Duke Energy is considered an essential service, as it supplies 25 million people with electricity daily, and everything the utility company does revolves around a culture of safety and reliability. The variables together was a catalyst for exploring AI technologies, Titone said, because whatever the company chose to do, it had to support the clean energy transition, deliver value to customers, and find a way for employees to work and improve safety.


Check out this cool underwater robot that inspects hydropower turbines

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GE Renewables has launched a first-of-its-kind underwater robot that can inspect hydropower turbines faster, more easily, and safely than humans can. GE Hydro Solutions' underwater turbine inspection robot, which GE describes as the size of a "beaver," can inspect hard-to-reach areas of any type of hydropower turbine without taking the equipment out of the water. In fact, it's able to inspect between turbine runner blades within openings of less than 200 mm – that's under 8 inches. The inspection robot can detect all the same things as the traditional, time-consuming, expensive inspection method, which involves taking units offline, draining the turbines, and sending people into dark, confined spaces. Potential problems to look for include erosion, cavitation, corrosion, impact marks, and the presence of foreign bodies, according to GE. The robot's onboard camera provides clear images, and it also records what it sees on video for further analysis.


2 Postdoc Positions in AI for Sustainable Power Systems - Sweden

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Moving towards climate security, electric power systems are going through a major paradigm shift with wide integration of distributed energy resources, such as solar PV, wind power, energy storage and electric vehicles. However, today's grid cannot handle the voltage rise and fast voltage fluctuations from high penetration of renewables. It is widely recognized that the lack of adequate control mechanisms to regulate the voltages is a key hindrance. The goal of this project is to use AI and deep reinforcement learning to advance the current control designs by making them more data-driven and communication efficient. Depending on the candidate's qualifications and scientific interests, the project can be directed towards smart grid optimization, AI algorithm development or hardware implementations.


Machine learning models based on thermal data predict solar radiation

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A research team at the University of Córdoba has developed and evaluated models for the prediction of solar radiation in nine locations in southern Spain and North Carolina (USA). Measuring solar radiation is costly, as are all the tasks related to the maintenance and calibration of the most commonly used sensors: pyranometers and radiometers. The result is a paucity of reliable data. Hence, a research group from the University of Córdoba has developed and evaluated several Machine Learning models to predict solar radiation in nine locations (southern Spain and North Carolina, USA) spanning a range of different geo-climatic conditions (aridity, distance to the sea, and elevation). The work has been featured in the journal Applied Energy.


Machine learning models based on thermal data predict solar radiation

#artificialintelligence

A research team at the University of Córdoba has developed and evaluated models for the prediction of solar radiation in nine locations in southern Spain and North Carolina (USA). Measuring solar radiation is costly, as are all the tasks related to the maintenance and calibration of the most commonly used sensors: pyranometers and radiometers. The result is a paucity of reliable data. Hence, a research group from the University of Córdoba has developed and evaluated several Machine Learning models to predict solar radiation in nine locations (southern Spain and North Carolina, USA) spanning a range of different geo-climatic conditions (aridity, distance to the sea, and elevation). The work has been featured in the journal Applied Energy.


AI Promises Climate-Friendly Materials

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To tackle climate change, scientists and advocates have called for a bevy of actions that include reducing fossil fuel use, electrifying transportation, reforming agriculture, and mopping up excess carbon dioxide from the atmosphere. But many of these challenges will be insurmountable without behind-the-scenes breakthroughs in materials science. Today's materials lack key properties needed for scalable climate-friendly technologies. Batteries, for example, require improved materials that can yield higher energy densities and longer discharge times. Without such improvements, commercial batteries won't be able to power mass-market electric vehicles and support a renewable-powered grid.


Elon Musk: Can Superintelligent AI Help us Reach Type 1 Civilization?

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Elon Musk has expressed worry about the advent of a digital superintelligent AI numerous times now. He has put solid solutions to the AI control problem. One of which is the merging scenario with AI. But first Elon Musk, is focused on making sure humanity makes the transition to renewable energy, which is the first right step towards becoming a type 1 civilization.