LONDON – Long before the real-world effects of climate change became so abundantly obvious, the data painted a bleak picture – in painful detail – of the scale of the problem. For decades, carefully collected data on weather patterns and sea temperatures were fed into models that analyzed, predicted, and explained the effects of human activities on our climate. And now that we know the alarming answer, one of the biggest questions we face in the next few decades is how data-driven approaches can be used to overcome the climate crisis. Data and technologies like artificial intelligence (AI) are expected to play a very large role. But that will happen only if we make major changes in data management.

AI systems are compute-intensive: the AI lifecycle often requires long-running training jobs, hyperparameter searches, inference jobs, and other costly computations. They also require massive amounts of data that might be moved over the wire, and require specialized hardware to operate effectively, especially large-scale AI systems. All of these activities require electricity -- which has a carbon cost. There are also carbon emissions in ancillary needs like hardware and datacenter cooling [1]. Thus, AI systems have a massive carbon footprint[2]. This carbon footprint also has consequences in terms of social justice as we will explore in this article.

In early 2021, Americans living on the East Coast got a sharp lesson on the growing importance of cybersecurity in the energy industry. A ransomware attack hit the company that operates the Colonial Pipeline--the major infrastructure artery that carries almost half of all liquid fuels from the Gulf Coast to the eastern United States. Knowing that at least some of their computer systems had been compromised, and unable to be certain about the extent of their problems, the company was forced to resort to a brute-force solution: shut down the whole pipeline. Leo Simonovich is vice president and global head of industrial cyber and digital security at Siemens Energy. The interruption of fuel delivery had huge consequences.

A Sandia National Laboratories team of materials scientists and computer scientists, with some international collaborators, have spent more than a year creating 12 new alloys -- and modeling hundreds more -- that demonstrate how machine learning can help accelerate the future of hydrogen energy by making it easier to create hydrogen infrastructure for consumers. Vitalie Stavila, Mark Allendorf, Matthew Witman and Sapan Agarwal are part of the Sandia team that published a paper detailing its approach in conjunction with researchers from Ångström Laboratory in Sweden and Nottingham University in the United Kingdom. "There is a rich history in hydrogen storage research and a database of thermodynamic values describing hydrogen interactions with different materials," Witman said. "With that existing database, an assortment of machine-learning and other computational tools, and state-of-the art experimental capabilities, we assembled an international collaboration group to join forces on this effort. We demonstrated that machine learning techniques could indeed model the physics and chemistry of complex phenomena which occur when hydrogen interacts with metals."

The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX's tracking pipeline groups detector measurements to form track candidates and filters them. The pipeline, originally developed using the TrackML dataset (a simulation of an LHC-inspired tracking detector), has been demonstrated on other detectors, including DUNE Liquid Argon TPC and CMS High-Granularity Calorimeter. This paper documents new developments needed to study the physics and computing performance of the Exa.TrkX pipeline on the full TrackML dataset, a first step towards validating the pipeline using ATLAS and CMS data. The pipeline achieves tracking efficiency and purity similar to production tracking algorithms. Crucially for future HEP applications, the pipeline benefits significantly from GPU acceleration, and its computational requirements scale close to linearly with the number of particles in the event.

In many fields of science and engineering, models with different fidelities are available. Physical experiments or detailed simulations that accurately capture the behavior of the system are regarded as high-fidelity models with low model uncertainty, however, they are expensive to run. On the other hand, simplified physical experiments or numerical models are seen as low-fidelity models that are cheaper to evaluate. Although low-fidelity models are often not suitable for direct use in reliability analysis due to their low accuracy, they can offer information about the trend of the high-fidelity model thus providing the opportunity to explore the design space at a low cost. This study presents a new approach called adaptive multi-fidelity Gaussian process for reliability analysis (AMGPRA). Contrary to selecting training points and information sources in two separate stages as done in state-of-the-art mfEGRA method, the proposed approach finds the optimal training point and information source simultaneously using the novel collective learning function (CLF). CLF is able to assess the global impact of a candidate training point from an information source and it accommodates any learning function that satisfies a certain profile. In this context, CLF provides a new direction for quantifying the impact of new training points and can be easily extended with new learning functions to adapt to different reliability problems. The performance of the proposed method is demonstrated by three mathematical examples and one engineering problem concerning the wind reliability of transmission towers. It is shown that the proposed method achieves similar or higher accuracy with reduced computational costs compared to state-of-the-art single and multi-fidelity methods. A key application of AMGPRA is high-fidelity fragility modeling using complex and costly physics-based computational models.

Artificial intelligence is taking the stage as smart cities become not just an idea for the future, but a present reality. Advanced technologies are at the forefront of this change, driving valuable strategies and optimising the industry across all operations. These technologies are quickly becoming the solution for fulfilling smart city and clean city initiatives, as well as net-zero commitments. AI is becoming well integrated with the development of smart cities. Implementation of AI is rapidly being recognised as the not-so-secret ingredient helping major energy providers accomplish their lowest-carbon footprints yet, along with unparalleled sustainability and attractive profit margins. What makes a city'smart' is the collection and analysis of vast amounts of data across numerous sectors, from metropolitan development and utility allocation all the way down to manual functions like city services.

Sign up to receive the Green Daily newsletter in your inbox. Technological solutions to climate change can be put into two categories. Vertical solutions that tackle pollution in one sector, say low-carbon fertilizers that help reduce emissions in agriculture. Or horizontal solutions that address issues across many different industries, say lithium-ion batteries that electrify cars but also better integrate renewables in the electricity mix.

The latest iteration of Amazon's battery-powered Ring doorbell adds a new feature to capture the early details of events most competitors would miss without needing to be plugged in. It tops Ring's battery-powered range, which starts at £89. The look and basic function of the Doorbell 4 is very similar to Ring's older models. It has a camera with night vision, motion sensors and a large doorbell button. When someone pushes the button Ring's signature chime plays and an alert is sent to your phone. You can view a live feed and speak through the doorbell using the app from anywhere with internet.

The global electric car sales in 2020 continued to exceed the expectations climbing to over 3 millions and reaching a market share of over 4%. However, uncertainty of generation caused by higher penetration of renewable energies and the advent of Electrical Vehicles (EV) with their additional electricity demand could cause strains to the power system, both at distribution and transmission levels. Demand response aggregation and load control will enable greater grid stability and greater penetration of renewable energies into the grid. The present work fits this context in supporting charging optimization for EV in parking premises assuming a incumbent high penetration of EVs in the system. We propose a methodology to predict an estimation of the parking duration in shared parking premises with the objective of estimating the energy requirement of a specific parking lot, evaluate optimal EVs charging schedule and integrate the scheduling into a smart controller. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot. This predicted duration feeds the energy management system that will allocate the power over the duration reducing the overall peak electricity demand. We structure our experiments inspired by two research questions aiming to discover the accuracy of the proposed machine learning approach and the most relevant features for the prediction models. We experiment different algorithms and features combination for 4 datasets from 2 different campus facilities in Italy and Brazil. Using both contextual and time of the day features, the overall results of the models shows an higher accuracy compared to a statistical analysis based on frequency, indicating a viable route for the development of accurate predictors for sharing parking premises energy management systems