Mediaboss Marketing

The transition to electric vehicles will require significant new amounts of power generation for charging, but utilities say those resources can be developed in time. A more pressing challenge may be managing new charging loads, ensuring millions of vehicles do not put undue stress on the grid.

There will be 30 million to 42 million electric vehicles on U.S. roads in 2030, and they will require about 28 million charging ports,according to the National Renewable Energy Laboratory. Utilities, distributed energy resource aggregators and research institutions are all stepping up to address the issue.

Power generation is only a part of this conversation. Just as important is improving our ability to manage demand in real time, Albert Gore, executive director of the Zero Emission Transportation Association, said Monday in a discussion of how the utility sector must approach EVs.

The industry needs to further its ability to precisely manage demand in real time, including by accurately predicting when and where increases in demand will occur, according to a new ZETA policy brief.

Utilities particularly larger electricity providers in urban areas have been working for years to nudge EV charging to off-peak hours through time-of-use rates or EV-specific rates.

Consolidated Edison, which serves New York City, expects more than a quarter million EVs in its territory by 2025 and has been working since 2017 to encourage grid-beneficial charging through its SmartCharge program, which offers incentives for drivers to avoid charging during peak times.

It's one of, if not the most, successful managed charging programs in the country,Cliff Baratta, Con Edisons electric vehicle strategy and markets section manager, said during ZETAs discussion. At the end of 2022, the utility had 20% of all light-duty EVs registered in its territory enrolled in the program.

In a lot of other places, we see that 5-6% is considered good, Baratta said. We have been able to get really strong engagement with that program, to try and entrench this grid beneficial charging behavior.

Research institutions are working to develop solutions. Argonne National Laboratory and the University of Chicago have partnered on the development of a new algorithm to manage EV charging that utilizes machine learning to efficiently schedule loads.

Distributed energy resource managers are rolling out approaches to managing the anticipated demand..

FlexCharging, which has provided managed charging programs and pilots since 2019, is rolling out a product called EVisionfor smaller utilities that may have fewer resources to devote to demand management initiatives.

Cloud-based software company Virtual Peaker on Tuesday launched a managed charging solution that allows utilities to utilize both vehicle telematics data or internet-connected EV chargers to manage vehicles in charging programs.

The company is focusing on creating a single, scalable solution to increase adoption of distributed energy resources programs and help utilities reach their goals more quickly and efficiently, Virtual Peaker founder and CEO Williams Burke said in a statement.

The companys DER platform is already being used by Efficiency Maine, the states administrator for energy efficiency and demand management programs, to manage battery systems and EV chargers during peak demand periods.

View post:
Machine learning, incentives and telematics: New tools emerge to ... - Utility Dive

Google has lifted the lid on how artificial intelligence (AI) and machine learning (ML) are assisting it with helping consumers and businesses shrink the environmental footprint of their activities by allowing them to make real-time adjustments that can curb their greenhouse gas (GHG) emissions.

Details of its work in this area can be found in the tech giants most recent annualEnvironmental report. Covering the 12 months to 31 December 2022, the document provides updates on how the tech giants efforts to run its datacentres and offices on carbon-free energy (CFE) round-the-clock are progressing and how its bid to reduce the water consumed by its operations is going.

We achieved approximately 64% round-the-clock CFE across all of our datacentres and offices, [and] this year, we expanded our CFE reporting to include offices and third-party datacentres, in addition to Google-owned and operated datacentres, said the company.

At the end of 2022, our contracted watershed projects have replenished 271 million gallons of water equivalent to more than 400 Olympic-sized swimming pools to support our target to replenish 120% of the freshwater we used.

The report also documents how, seven years after declaring itself as being an AI-first company, this technology is underpinning the companys own climate change mitigation efforts.

To this point, the company said it was using AI to accelerate the development of climate change-fighting tools that can provide better information to individuals, operational optimisation for organisations, and improved predicting and forecasting.

As an example, the company pointed to the way Google Maps uses AI to help users plan journeys in a more eco-friendly way by minimising the amount of fuel and battery power they use to get from A to B.

Eco-friendly routing has helped prevent 1.2 metric tonnes of estimated carbon emissions since launch equivalent to taking approximately 250,000 fuel-based cars off the road for a year, it reported.

The technology is also proving useful in the companys work to reduce the environmental footprint of its AI models by helping the datacentres in which they are hosted run in a more energy-efficient way.

Weve made significant investments in cleaner cloud computing by making our datacentres some of the most efficient in the world and sourcing more carbon-free energy, it said in the report. Were helping our customers make real-time decisions to reduce emissions and mitigate climate risks with data and AI.

To reinforce this point, the company cited the roll-out of its Active Assist feature to Google Cloud customers, which uses machine learning to identify unused and potentially wasteful workloads so they can be stopped to save money and cut the organisations carbon emissions at the same time.

On the flipside, though, the report went on to acknowledge that ramping up the use of AI in this way also increases the amount of work its datacentres are doing, which is giving rise to concerns about the environmental impact and energy consumption habits of its AI workloads.

With AI at an inflection point, predicting the future growth of energy use and emissions from AI compute in our datacentres is challenging, the report continued.

Historically, research has shown that as AI/ML compute demand has gone up, the energy needed to power this technology has increased at a much slower rate than many forecasts predicted. We have used tested practices to reduce the carbon footprint of workloads by large margins; together, these principles have reduced the energy of training a model by up to 100x and emissions by up to 1,000x.

The report added: We plan to continue applying these tested practices and to keep developing new ways to make AI computing more efficient.

Original post:
Google reveals how AI and machine learning are shaping its ... - ComputerWeekly.com

Hello,

I hope this email finds you well, coding away and innovating in the dynamic world of Machine Learning.

Today, I am excited to announce a collaboration between Machine Learning Mastery and KDnuggets. Together, we've created something unique to enrich your Machine Learning journey.

I present to you our brand new ebook, "Maximizing Productivity with ChatGPT". While we've been known for our technical, code-heavy books that have guided many through the intricate pathways of Machine Learning, this time we're offering something different but equally impactful.

This ebook shifts the focus from pure coding and technical aspects, to understanding, interacting, and leveraging one of the most advanced AI tools in the market - ChatGPT. This is an evolution from our prior books, aimed at broadening your perspective and deepening your understanding of AI applications.

You'll discover:

In celebration of this launch, we're offering an exclusive 20% early bird discount with the code "20offearlybird" at checkout. But don't delay - this offer ends soon!

Maximizing Productivity with ChatGPT

This ebook is a testament to the fact that not all roads to mastering Machine Learning and AI are paved with code alone. Harnessing the power of AI also involves understanding its applications and learning how to effectively interact with it. "Maximizing Productivity with ChatGPT" offers you exactly that - an avenue to explore and master the usage of AI beyond the traditional coding confines.

If you have any questions, please don't hesitate to hit reply and send me an email directly. Here's to harnessing the power of AI together.

- Jason, Machine Learning Mastery Founder

Read the rest here:
Unlock the Power of AI A Special Release by KDnuggets and ... - KDnuggets

Improved weather and climate forecasting using machine learning and artificial intelligence is the focus of a new University of Hawaii at Mnoa project. Results are expected to have a major impact in Hawaii and other tropical climate areas around the world.

Associate Professor Peter Sadowski from the Information and Computer Sciences Department in the College of Natural Sciences earned a five-year, $424,293 CAREER grant from the National Science Foundation (NSF). CAREER grants are designed to support early-career faculty to serve as academic role models in research and education.

One of the risks of climate change for Hawaii is extreme weather events, and current scientific models are poor at estimating these risks, Sadowski said. This project will provide a completely new approach modeling these risks, using the latest advancements in AI (artificial intelligence).

Sadowskis project will develop machine-learning methods to predict the risk of adverse weather and climate events. AI will be used to develop new data-driven computational methods for modeling risk and apply these methods to weather applications.

In particular, these models will be applied to forecasting solar irradiance and precipitation, two areas that are particularly important for tropical islands such as the Hawaiian Islands. Estimating the risk of rapid changes in solar power generation is necessary for managing energy grids that are seeing a rapid increase in variable renewable sources, and floods claim hundreds of lives and billions in property damage each year in the U.S. alone.

Artificial intelligence methods have greatly improved translating text into predictions using images and video. A key development is the ability to learn probabilistic models of images and video. The research will leverage existing data from numerical simulations of atmospheric variables, observations from satellites and ground-based weather station data from the NSF-funded CHANGE-HI project. The machine-learning methods developed by this project will complement existing physics-based weather prediction models by providing location-specific forecasts with increased speed, higher resolution and probabilistic accuracy.

This research will be paired with an educational outreach program that includes a summer data science course for high school students and a workshop to share data science teaching materials with Hawaiis K12 teachers.

Go here to see the original:
$424K grant to better predict weather, climate through machine ... - University of Hawaii

Some of the significant applications of machine learning in healthcare are Personal Assistants and Chatbots, the finance and banking industry, medical and healthcare, and autonomous vehicles.

FREMONT, CA: Due to the emergence of AI tools like ChatGPT and DALL-E, machine learning, or ML, has garnered significant attention. A widely recognized concept, it allows computers to adapt and learn from experiences. Despite its growing popularity, machine learning is already prevalent in many real-life situations.

The examples of machine learning in action are as follows:

Finance and Banking for Individuals: In the finance and banking sectors, machine learning algorithms provide valuable applications. Advanced fraud detection techniques are one way banks use AI and ML algorithms to protect their customer's assets. By leveraging image and text recognition methods, machine learning models for fraud detection in banking can distinguish between legal and illegal transactions.

Personal finance can also benefit from machine learning, particularly when it comes to portfolio management. As robo-advisors, online investment platforms use machine learning to build, monitor, and automate diversified portfolio management. Without human supervision, these platforms construct portfolios based on preferences for specific assets or risks.

Additionally, machine learning can be used to forecast the market in personal finance. Based on historical data, BL algorithms can predict stock prices and market trends. By acquiring this insight, individuals will be able to develop effective trading strategies and identify favorable trading opportunities.

Medical Diagnosis and Healthcare: Machine learning has also become a crucial tool for medical diagnosis, patient care, and overall outcomes in the healthcare industry. Through collaboration with various healthcare technologies, it improves wellness in a variety of ways.

The following are six critical applications of machine learning in healthcare:

In order to diagnose and prognosis diseases more accurately, machine learning algorithms analyze patient data, including symptoms, medical records, lab results, and imaging scans.

In radiology, machine learning aids radiologists in diagnosing diseases by automatically detecting abnormalities, identifying features, and detecting abnormalities in X-rays, MRI scans, and pathology slides.

By enabling clinical trial optimization, patient recruitment, and identifying suitable candidates for specific treatments, machine learning models optimize drug discovery processes.

Machine learning develops personalized treatment plans by analyzing a patient's characteristics, genetic information, treatment history, and clinical data.

In conjunction with Internet of Things (IoT) wearable devices, machine learning enables predictive analytics.

Autonomous vehicles: Tesla is a prominent example of how machine learning is used in the development of modern cars. Tesla's cars are equipped with AI hardware provided by NVIDIA that incorporates unsupervised ML models for self-learning object recognition. Tesla is not the only company with self-driving features.

They are equipped with cameras, LiDAR, radar, and GPS to gather comprehensive information about their surroundings. The data is then processed to ensure accurate perception and effective decision-making. A self-driving car uses Simultaneous Localization and Mapping (SLAM) techniques to create updated maps to aid navigation.

In self-driving cars, ML models assist in real-time decision-making by determining optimal paths. Furthermore, these models facilitate the development of adaptive systems capable of detecting and predicting potential vehicle malfunctions.

See the original post here:
What are the Critical Applications of Machine Learning in Healthcare - CIO Applications