solar energy

Machine learning used to identify high-performing solar materials


Finding the best light-harvesting chemicals for use in solar cells can feel like searching for a needle in a haystack. Over the years, researchers have developed and tested thousands of different dyes and pigments to see how they absorb sunlight and convert it to electricity. Sorting through all of them requires an innovative approach. Now, thanks to a study that combines the power of supercomputing with data science and experimental methods, researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory and the University of Cambridge in England have developed a novel "design to device" approach to identify promising materials for dye-sensitized solar cells (DSSCs). DSSCs can be manufactured with low-cost, scalable techniques, allowing them to reach competitive performance-to-price ratios.

Improved Demand Response Management and Better Resource Allocation...


AI, in the coming years, is expected to make a lot of progress in the solar and wind energy sector by updating manual processes into an automated one. AI with the help of other groundbreaking technologies like machine learning, advanced neural networks, and deep learning have shown their ability to make a big revolution in the utility and energy sectors. The increasing modal share of renewable energy sources have caused insufficiency in demand and supply of energy, and now, many companies are implementing AI with various other new technologies to allow utilities to manage the imbalance. AI, in the future, is expected to improve the efficiency of the renewable energy industry by changing traditional manual operations of the industry into automated processes. Also, the other transforming technologies such as IoT and big data are expected to contribute a lot to AI processes to help improve the process to overcome the energy insufficiency.

AI & IoT Insider Labs: Helping transform smallholder farming


From smart factories and smart cities to virtual personal assistants and self-driving cars, artificial intelligence (AI) and the Internet of Things (IoT) are transforming how people around the world live, work, and play. But fundamentally changing the ways people, devices, and data interact is not simple or easy work. Microsoft's AI & IoT Insider Labs was created to help all types of organizations accelerate their digital transformation. Member organizations around the world get access to support both technology development and product commercialization, for everything from hardware design to manufacturing to building applications and turning data into insights using machine learning. Here's how AI & IoT Insider Labs is helping one partner, SunCulture, leverage new technology to provide solar-powered water pumping and irrigation systems for smallholder farmers in Kenya.

Here Comes the Sun: A New Wave of Solar-Powered AI at the Edge


For decades, those four words--not to be confused with a hit Daft Punk song--have both driven fear into developers and driven sales. However, as the energy burdens for the Internet of Things (IoT), cloud computing, crypto currencies and artificial intelligence (AI) increase, a fifth word is necessary: greener. (Xnor) isn't scared of the "greener" challenges facing industries today, and the unveiling of its new application-specific integrated circuit (ASIC) technologies proves so. "Power will become the biggest bottleneck to scaling AI," said Ali Farhadi, co-founder of Xnor. "What Xnor has proved today is that it is now possible to run AI inference at such low power that you don't even need a battery. This will change not only the way products are built in the future, but how entire cities and countries deploy AI solutions at scale."

Submodular Load Clustering with Robust Principal Component Analysis Machine Learning

Traditional load analysis is facing challenges with the new electricity usage patterns due to demand response as well as increasing deployment of distributed generations, including photovoltaics (PV), electric vehicles (EV), and energy storage systems (ESS). At the transmission system, despite of irregular load behaviors at different areas, highly aggregated load shapes still share similar characteristics. Load clustering is to discover such intrinsic patterns and provide useful information to other load applications, such as load forecasting and load modeling. This paper proposes an efficient submodular load clustering method for transmission-level load areas. Robust principal component analysis (R-PCA) firstly decomposes the annual load profiles into low-rank components and sparse components to extract key features. A novel submodular cluster center selection technique is then applied to determine the optimal cluster centers through constructed similarity graph. Following the selection results, load areas are efficiently assigned to different clusters for further load analysis and applications. Numerical results obtained from PJM load demonstrate the effectiveness of the proposed approach.

This wireless AI camera runs entirely on solar power


A big trend in AI is the transition from cloud to edge computing. Benefits of this approach can include faster results, greater security, and more flexibility. But how far can you push this model? Seattle-based startup Xnor is certainly right at the bleeding-edge. This week the company unveiled a prototype AI camera that runs entirely off solar power -- no battery or external power source required.

AI is reinventing the way we invent


Amgen's drug discovery group is a few blocks beyond that. Until recently, Barzilay, one of the world's leading researchers in artificial intelligence, hadn't given much thought to these nearby buildings full of chemists and biologists. But as AI and machine learning began to perform ever more impressive feats in image recognition and language comprehension, she began to wonder: could it also transform the task of finding new drugs? The problem is that human researchers can explore only a tiny slice of what is possible. It's estimated that there are as many as 1060 potentially drug-like molecules--more than the number of atoms in the solar system. But traversing seemingly unlimited possibilities is what machine learning is good at. Trained on large databases of existing molecules and their properties, the programs can explore all possible related molecules.

Xnor shrinks AI to fit on a solar-powered chip, opening up big frontiers on the edge


It was a big deal two and a half years ago when researchers shrunk down an image-recognition program to fit onto a $5 computer the size of a candy bar -- and now it's an even bigger deal for to re-engineer its artificial intelligence software to fit onto a solar-powered computer chip. "To us, this is as big as when somebody invented a light bulb,"'s co-founder, Ali Farhadi, said at the company's Seattle headquarters. Like the candy-bar-sized, Raspberry Pi-powered contraption, the camera-equipped chip flashes a signal when it sees a person standing in front of it. The point is that has figured out how to blend stand-alone, solar-powered hardware and edge-based AI to turn its vision of "artificial intelligence at your fingertips" into a reality. "This is a key technology milestone, not a product," Farhadi explained.

R.I.P., Opportunity Rover: the Hardest-Working Robot in the Solar System


Last night, NASA reached out one final time to the Opportunity rover on Mars, hoping the golf-cart-sized machine would phone home with good news. Since June, the robot has been unresponsive, likely because a planet-wide sandstorm coated its solar panels in dust. NASA has pinged it over 1,000 times in those gloomy eight months, to no avail. Last night's attempt was no exception: NASA has announced that Opportunity is officially dead. "I was there yesterday and I was there with the team as these commands went out into the deep sky," said NASA associate administrator Thomas Zurbuchen in a briefing this morning, titled A Lifetime of Opportunity.

Ordnance Survey launch a solar-powered drone that can fly for 90 days at a time

Daily Mail

Ordnance survey has unveiled a solar-powered drone that is capable of flying for 90 days at a time without needing to come back to Earth and will be used to provide higher quality images of Earth. It will circle at approximately 67,000 ft (20,400m) above the ground and snap images to sell to organisations and businesses. First tests of the Astigan unmanned aerial vehicle are scheduled to take place before the end of 2019. Ordnance Survey is the majority stakeholder in Astigan, a firm based in Bridgwater, Somerset. The company works in the same factory that was once home to Facebook's Aquila internet drone project.