UK-based energy tech startup wants to stop climate change with AI & blockchain


Verv, the Google-mentored energy tech startup behind the smart energy hub and green electricity sharing platform, recently announced that it has raised over £6.5 million (€7.5 million) in its Series A round led by environmental fund Earthworm. Earthworm has invested £5 million in Verv's pioneering IoT and renewable energy trading technology that could drive down household electricity bills and carbon emissions by over 20%. Other investors in the round include European innovation engine for sustainable energy, InnoEnergy, Crowdcube and international energy and services company, Centrica. Earthworm's investment is an important backing of Verv's vision to make millions of homes more green with a global network of smart hubs that offer a real-time breakdown of key appliance use and spend, as well as enable the trading of domestic renewable energy between communities. At Earthworm we are driven by sustainability and Verv represents a brilliant example of'enabling' technology.

ABB highlights at Hannover Messe 2019 - Day 2


This year at Hannover Messe, ABB is streamlined into four entrepreneurial businesses: Electrification, Industrial Automation, Motion, and Robotics & Discrete Automation. As our main focus is IIoT, "the factory of the future" is clearly one of the topics we want to know more about. YuMi collaborative robots offer unmatched precision in assembly operations, while the SuperTrak flexible transport system orchestrates the timely movement of parts from one station to another. New partnerships were announced, and first joint solutions are showcased. ABB and Ericsson have strengthened their commitment to accelerate the industrial ecosystem for flexible wireless automation, which will enable enhanced connected services, industrial IoT and artificial intelligence technologies in the future.

How a Brooklyn renewable energy company ended up making a surveillance drone — Future Blink


Pliant Energy Systems has spent the last two years developing a drone named Velox. The robot, which was initially meant to be a generator that could harness the flow of water, is now more of a sleek-looking surveillance drone with potential to one day even help deliver medical supplies and ammo in combat. We spoke to the founder and CEO of Pliant about the evolution of his company. We also wanted to see the robot in person because it's really cool.

Google will groom these 10 Indian startups that use AI and machine learning


Google just announced the 10 startups that have been shortlisted for the second calls of its Launchpad Accelerator program in India. All of the startups on the list have used artificial intelligence and machine learning to formulate their products. Google just announced the second wave of startups selected for their Launch Accelerator program in India. The program kicks off today with a one week mentorship programme boot camp organised by Google in Bengaluru which will be followed by more classes in April and May to address more specific issues -- lasting a total of three months. Aside from guidance, Google will also provide support for AI and ML, cloud computing, developing user interfaces, using the Android platform, online presence, product strategy and marketing.

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.

Students helping to make islands carbon neutral

MIT News

Small island communities across the globe are facing some of the earliest and most severe impacts of climate change. Many have started to turn away from traditional energy sources to reduce their own carbon footprints and inspire broader conversations on the urgent need for all communities to help mitigate climate change by dramatically reducing carbon dioxide emissions. Recently, the Massachusetts island community of Martha's Vineyard engaged with MIT students to discuss pathways toward a net-zero carbon future. Getting to net-zero carbon emissions entails transitioning to low- or no-carbon energy generation, employing energy efficiency measures, offsetting emissions by purchasing carbon credits, and other measures. Prompted by the Vineyard Sustainable Energy Committee, Martha's Vineyard is looking to achieve net-zero carbon by 2030.

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.

Google and DeepMind are using AI to predict the energy output of wind farms


Google announced today that it has made energy produced by wind farms more viable using the artificial intelligence software of its London-based subsidiary DeepMind. By using DeepMind's machine learning algorithms to predict the wind output from the farms Google uses for its green energy initiatives, the company says it can now schedule set deliveries of energy output, which are more valuable to the grid than standard, non-time-based deliveries. According to Google, this software has improved the "value" of the wind energy these farms are providing by 20 percent over a baseline where no such time-based predictions are being performed. We don't know exactly what that value is in monetary terms or in terms of energy output. We also don't know where exactly this is being deployed, although Google works with wind farms largely in the Midwest, where some of its US data centers are located.

Machine learning can boost the value of wind energy


Carbon-free technologies like renewable energy help combat climate change, but many of them have not reached their full potential. Consider wind power: over the past decade, wind farms have become an important source of carbon-free electricity as the cost of turbines has plummeted and adoption has surged. However, the variable nature of wind itself makes it an unpredictable energy source--less useful than one that can reliably deliver power at a set time. In search of a solution to this problem, last year, DeepMind and Google started applying machine learning algorithms to 700 megawatts of wind power capacity in the central United States. These wind farms--part of Google's global fleet of renewable energy projects--collectively generate as much electricity as is needed by a medium-sized city.

DeepMind and Google Train AI To Predict Energy Output Of Wind Farms


DeepMind claims it has trained an artificial intelligence system how to predict the energy output of Google wind farms in the U.S. The variable nature of wind makes it difficult to accurately predict how much energy a wind farm could produce in any given time period. But DeepMind says that its AI system-- a neural network trained on widely available weather forecasts and historical turbine data -- can predict wind power output 36 hours ahead of actual generation with a reasonable degree of accuracy. "Based on these predictions, our model recommends how to make optimal hourly delivery commitments to the power grid a full day in advance," a team of DeepMind researchers wrote in a blog post on Tuesday. "This is important because energy sources that can be scheduled (i.e. can deliver a set amount of electricity at a set time) are often more valuable to the grid." Google claims that DeepMind's AI system has boosted the "value" of its wind energy by roughly 20 per cent.