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Artificial intelligence and machine learning move to the edge
We often associate artificial intelligence (AI) and machine learning (ML) with exotic applications - self-driving cars, speech and facial recognition, robotic control and medical diagnosis - all powered by massive rows of servers filled with CPUs or GPUs, at some distant data center.
The result of this training is then compressed and distilled into smaller and faster applications that can be applied quickly to new data at the edge using specialty hardware designed to perform this task. Google’s initial performance benchmarks for an image classification application with a remote camera using edge AI show a 70-100x faster performance than a CPU-based approach.
By engaging with vendors and system integrators to communicate their requirements and define their use cases for edge intelligence, utilities are in a position to drive a technology revolution in the industry that will unlock new benefit streams and empower customers with more energy choices. The more data that is collected in the cloud, the better the training models become, resulting in a smarter, faster and more accurate inference – all of which enables edge intelligence to be a true business differentiator for utilities.
How artificial intelligence (AI) technologies can support integration of renewable energy
The electrical grid is arguably the most complex machine on Earth and is evolving rapidly from its centralized history of the last century.
The current grid was not designed to accommodate the diversity of renewable energy sources and the inherent variability of solar and wind creates challenges in meeting variable load.
This article explores 4 ways that AI methods can improve the integration and adoption of renewable energy resulting in a modernized electrical grid supporting the reliability and resilience of the overall grid.
2) AI will allow new capabilities for integrating microgrids 3) AI drives new smart consumer devices and value streams and 4) AI optimization techniques will improve the placement and resulting value of Distributed Renewable Energy Improved centralized control centers: The energy grid is becoming increasingly interconnected as computing, data collection and devices scale exponentially.
Because of the dynamic nature of the grid below the substation autonomous controls, and new methods like transactive energy will have an outsized role in this revolution, AI technologies will be integral to the transition.
Learning AI Algorithms like Adaptive Dynamic Programming and intelligent multi-agent systems hold great promise to not only provide real-time control but improve system optimization over time as new generation sources and devices are integrated.
New smart consumer devices and value streams: We are already seeing the remaking of the home with distributed intelligent devices but this trend is moving to the renewables world with smart batteries coupled to roof top solar, EV chargers and energy management systems.
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Mobile Industrial Robots Launches AI-Enabled Robot to Transport Loads and Pallets
Mobile Industrial Robots (MiR), according to a recent release, has launched the MiR1000, the company’s largest autonomous mobile robot (AMR), which reportedly can automatically pick up, transport and deliver pallets and other heavy loads up to 2200 lbs (1000 kg) through dynamic environments.
With the MIR1000 and our other highly flexible autonomous robots, none of which require rebuilding infrastructure or extensive programming capabilities, we have made it especially easy to optimize the transportation of all types of materials.
With artificial intelligence (AI) capabilities incorporated into the software and cameras that function as an extended set of robot sensors, MiR has enabled its robots for optimized route-planning and driving behavior.
- On 3. marts 2021
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