Using Machine Learning & Neural Networks to Maximize Solar Energy Globally

  • 📰 cleantechnica
  • ⏱ Reading Time:
  • 45 sec. here
  • 2 min. at publisher
  • 📊 Quality Score:
  • News: 21%
  • Publisher: 51%

Ai Ai Headlines News

Ai Ai Latest News,Ai Ai Headlines

Other ways to generate solar energy that just trying to make solar cells efficient potential of luminescent solar concentration

Scientists are always on the lookout for ways to make our world a better place, and one area they’re focusing on is solar energy. One idea in this area is to make solar cells more efficient by concentrating more solar light onto them.

They integrated this data into an electronic model to calculate the solar cells’ output. By simulating various scenarios, they could predict how much energy the solar cells could produce at various locations worldwide., however, revealed a surprising twist. “Making solar cells super-efficient turns out to be very difficult. So, instead of just trying to make solar cells better, we figured some other ways to capture more solar energy,” said Dr.

“We suggest a different plan that can make solar panels work well in lots of different places around the world,” said Baikie. “The idea is to make them flexible, a bit see-through/semi-transparent, and able to fold up. This way, the panels can fit into all kinds of places.”Furthermore, the researchers advocate the use of patterning the solar capture devices with the aim to optimise their arrangement for maximum sunlight absorption.

 

Thank you for your comment. Your comment will be published after being reviewed.
Please try again later.
We have summarized this news so that you can read it quickly. If you are interested in the news, you can read the full text here. Read more:

 /  🏆 565. in Aİ

Ai Ai Latest News, Ai Ai Headlines

Similar News:You can also read news stories similar to this one that we have collected from other news sources.

A brief five-minute brain scan using machine learning is 83 percent accurate.Five brain regions have been identified that can accurately and consistently predict heterosexual or homosexual orientation.
Source: PsychToday - 🏆 714. / 51 Read more »

A Machine Learning Text Classification Case Study with a Product-driven TwistText classification case study with a product-driven twist. We're building various models (logreg, RNN, transformers) and compare their quality and performance
Source: hackernoon - 🏆 532. / 51 Read more »

How to Build a Basic Recommendation Engine without Machine LearningExplore the intricacies of building a recommendation engine without relying on machine learning models.
Source: hackernoon - 🏆 532. / 51 Read more »

Biologists use machine learning to classify fossils of extinct pollenIn the quest to decipher the evolutionary relationships of extinct organisms from fossils, researchers often face challenges in discerning key features from weathered fossils, or with prioritizing characteristics of organisms for the most accurate placement within a phylogenetic tree.
Source: physorg_com - 🏆 388. / 55 Read more »

Enhancing rapeseed maturity classification with hyperspectral imaging and machine learningRapeseed oil, a vital oilseed crop facing growing global demand, encounters a significant challenge in achieving uniform seed maturity, owing to asynchronous flowering. Traditional maturity assessment methods are limited by their destructive nature.
Source: physorg_com - 🏆 388. / 55 Read more »

Harnessing hyperspectral imaging and machine learning for rubber tree nutrient managementRubber trees are essential for natural rubber, and require precise nutrient management. Traditional methods for assessing nutrient levels are expensive and destructive, but near-infrared (NIR) hyperspectral techniques offer a promising nondestructive alternative.
Source: physorg_com - 🏆 388. / 55 Read more »