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Tackling Climate Change with Machine Learning Artificial Intelligence

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

Renewable power critic is chosen to head energy price review

The Guardian > Energy

An academic who is a vocal critic of the price of renewable power is the government's preferred choice to head a review of the financial cost of energy in the UK. Dieter Helm, an economist at the University of Oxford, has been chosen by the Department for Business, Energy and Industrial Strategy (BEIS) to carry out the review, the Guardian has learned. The Conservative manifesto promised that the resulting report would be the first step towards "competitive and affordable energy costs". Theresa May is among those in the government taking an interest in the cost-of-energy review, which will examine how power prices can be kept down while meeting the UK's carbon targets and keeping the lights on. But the choice of Helm, author of a new book on the slow demise of oil companies in the face of energy trends, will be controversial in some quarters because of his criticism of wind and solar power.

National Grid exploring the potential of Artificial Intelligence to optimise renewables


The National Grid has confirmed that it is in the "earliest stages" of discussions exploring the use of Artificial Intelligence (AI), which could potentially maximise the use of renewable energy by predicting peaks in demand across the UK. The National Grid, which operates and owns the infrastructure that transports electricity across the UK, has seen its ability in balancing and stabilising the grid challenged in recent years as intermittent renewables such as solar and wind have been fed into the energy mix. While the introduction of renewables into the mix forms a key role in both national and European legislation to decarbonise the grid, concerns have been raised as to the National Grid's ability to deal with fluctuating wind and solar resources, which can sometimes produce more energy than the system can cope with. Energy storage and demand response initiatives, whereby businesses either store surplus energy or increase or reduce energy consumption based on demand, are being incorporated by the National Grid, which is now "exploring what opportunities" AI could offer to balance the situation. The National Grid revealed that it is in discussions with the UK-based AI company DeepMind about introducing new technologies to help balance the grid and improve the use of renewables.

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.

Mapped: The climate change conversation on Twitter - Carbon Brief


Twitter has become a popular social media platform for discussing climate change. But amid the sheer volume of chatter, it's often hard to get a top-down sense of who the most influential users are on Twitter – and where they "sit" within the Twitter "universe" compared to other users. So Carbon Brief has commissioned Right Relevance to start harvesting data from Twitter and use it to build maps periodically showing how the influencers within the climate change conversation shift over time. Below, John Swain from Right Relevance explains the methodology and highlights some key findings from the first wave of analysis. In the coming months, Carbon Brief will publish updated maps created by Right Relevance, as well as expand the analysis to also look at the Twitter conversation about energy issues, such as nuclear power, wind farms, solar energy, shale gas and coal. You can also take a look at the full-screen version. At Right Relevance we provide information about influence on social media and, particularly, Twitter. We have a free service where you can discover information about topical influencers on thousands of topics. We provide an API framework to provide access to our data on influencers, which we call "Relevance as a Service".