Power (Management)

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• Case Studies
  • Nuclear Fusion
  • Resources & Utilities
• Energy Use with AI Algorithms
• DeepMind AI Reduces Google Data Centre Cooling Bill by 40% | Richard Evans & Jim Gao
• Machine Learning Applications for Data Center Optimization | Jim Gao, Google
• Artificial Intelligence Accelerates Development of Limitless Fusion Energy | John Greenwald - Princeton Plasma Physics Laboratory

Machine Learning Applications for Energy Efficiency and Customer Care With the world’s largest residential energy dataset at our fingertips, Opower is uniquely situated to use Machine Learning to tackle problems in demand-side management. Our communication platform, which reaches millions of energy customers, allows us to build those solutions into our products and make a measurable impact on energy efficiency, customer satisfaction, and cost to utilities. In this talk, we will survey several Machine Learning projects that we’ve been working on. These projects vary from predicting customer propensity to clustering load curves for behavioral segmentation, and leverage supervised and unsupervised techniques.

DeepMind AI reduces energy used for cooling Google Data Centers by 40% From smartphone assistants to image recognition and translation, machine learning already helps us in our everyday lives. But it can also help us to tackle some of the world’s most challenging physical problems -- such as energy consumption. Large-scale commercial and industrial systems like data centers consume a lot of energy, and while much has been done to stem the growth of energy use, there remains a lot more to do given the world’s increasing need for computing power. Google is taking many steps to reduce energy consumptions. Compared to five years ago, Google now get around 3.5 times the computing power out of the same amount of energy. By applying DeepMind’s machine learning to its own data centers, Google managed to reduce the amount of energy it use for cooling by up to 40 percent. In any large scale energy-consuming environment, this would be a huge improvement. Given how sophisticated Google’s data centers are already, it’s a phenomenal step forward. The implications are significant for Google’s data centers, given its potential to greatly improve energy efficiency and reduce emissions overall. This will also help other companies who run on Google’s cloud to improve their own energy efficiency. Every improvement in data center efficiency reduces total emissions into our environment and with technology like DeepMind’s, we can use machine learning to consume less energy and help address one of the biggest challenges of all -- climate change.

Becoming a BrainBox AI Licensed Reseller Webinar Learn more about becoming a BrainBox AI Licensed Reseller and how you can provide your real estate clients with up to 25% in energy savings in less than 3 months. Launched only a few months ago, BrainBox AI’s Licensed Reseller program is already comprised of over 20 organizations from across North America and abroad, and includes System Integrators, ESCOs and everything in between. This webinar aims to introduce BrainBox AI’s technology and to demonstrate the benefits of adding our AI solution to your portfolio. Join our information webinar, to learn more about: - BrainBox AI’s market leading AI-driven HVAC optimization technology - Benefits of becoming a BrainBox AI licensed reseller - Partnership structure and process

PyCon HK 2017 - Machine Learning on Energy Consumption Prediction PyCon Hong Kong 2017 Talk Machine Learning on Energy Consumption Prediction - by Benny Huang The project is in HKU deep learning lab. We analyze a large set of energy consumption data and build real time application based on the prediction. The topic involves different machine learning approaches and comparison. Since the machine learning is often discussed, I will also talk about the often ignored skill in data science. How to handle huge data and optimize the processing code, how to effectively build useful tools and make the life of data scientist easier, and how to correctly interpret the predicting result to enhance the project level from merely "machine learning" to real "data-driven application"

Energy Demand Forecasting with MACHINE LEARNING Machine Learning to predict on-campus energy use, Develop a Multiple Regression Model, Include weekend variable (Binary), Compare the Mean Absolute Percent Error (MAPE), Multiple Regression vs. Neural Network Model

Saving Energy Consumption With Deep Learning Discover how big data, GPUs, and deep learning, can enable smarter decisions on making your building more energy-efficient with AI startup, Verdigris.

Wattics Energy Management Analytics: Machine Learning Forecasting Don’t waste any more time analysing raw measurements. Let our analytics engines compare your energy use against expected patterns and alert you when your consumption becomes abnormal. Here is a brief tutorial of the Sentinel Trends tab on our Wattics dashboard. If you have any questions, please visit our website at www.wattics.com or contact us at support@wattics.com.

Internet of Things Optimization The Internet of things lives! More and more devices are coming prepackaged with internet access that wouldn’t normally be. That includes everything from smart salt shakers to smart tabletops. And because these devices have a connection to the world wide web, they can communicate with the outside world and each other, sharing information and even learning from one another. In this video, I'll explain how to use a reinforcement learning technique called "Monte Carlo" to optimize electricity consumption and cooling demands for a smart home. Enjoy!