...
Exploratory Studies in Neural Network-based Modeling and Control of Particle Accelerators
Date: Aug
.1,
3:30pm (Note time!)2pm
Speaker: Auralee Edelen (CSU)
Particle accelerators are host to myriad control challenges: they involve a multitude of interacting systems, are often subject to tight performance demands, in many cases exhibit nonlinear behavior, sometimes are not well-characterized due to practical and/or fundamental limitations, and should be able to run for extended periods of time with minimal interruption. One avenue toward improving the way these systems are controlled is to incorporate techniques from machine learning. Within machine learning, neural networks in particular are appealing because they are highly flexible, they are well-suited to problems with nonlinear behavior and large parameter spaces, and their recent success in other fields (driven largely by algorithmic advances, greater availability of large data sets, and improvements in high performance computing resources) is an encouraging indicator that they are now technologically mature enough to be fruitfully applied to particle accelerators. This talk will highlight a few recent efforts in this area that were focused on exploring neural network-based approaches for modeling and control of several particle accelerator subsystems, both through simulation and experimental studies.
| View file | ||||
|---|---|---|---|---|
|
Estimating behind-the-meter solar generation with existing measurement infrastructure
Date: July 11, 2pm
Speaker: Emre Kara
Real-time PV generation information is crucial for distribution system operations such as switching,
state-estimation, and voltage management. However, most behind-the-meter solar installations are not
monitored.Typically, the only information available to the distribution system operator is the installed
capacity of solar behind each meter; though in many cases even the presence of solar may be unknown.
We present a method for disaggreagating behind-the-meter solar generation using only information that
is already available in most distribution systems. Specifically, we present a contextually supervised source
separation strategy adopted to address the behind-the-meter solar disaggregation problem. We evaluate
the model sensitivities to different input parameters such as the number of solar proxy measurements, number
of days in the training set, and region size.
| View file | ||||
|---|---|---|---|---|
|
Development and Application of Online Optimization Algorithms
Date: June 27, 3pm
Location: Kings River, B52-306 (Note change in time and place!)
Speaker: Xiabiao Huang
Automated tuning is an online optimization process. It can be faster and more efficient than manual tuning and can lead to better performance. It may also substitute or improve upon model based methods. Noise tolerance is a fundamental challenge to online optimization algorithms. We discuss our experience in developing a high efficiency, noise-tolerant optimization algorithm, the RCDS method, and the successful application of the algorithm to various real-life accelerator problems. Experience with a few other online optimization algorithms are also discussed.
View file name XiaobiaoHuang_RCDS.pdf height 250
Machine Learning at NERSC: Past, Present, and Future
...
Date: May 2, 2pm
Location: Sycamore Conference Room (040-195)
Speaker: John Fox
Abstract: Plug-in hybrid and all-electric vehicles offer potential to transfer energy demands from liquid petroleum fuels to grid-sourced electricity. We are investigating optimization methods to improve the efficiency and resource utilization of Plug-in Hybrid Electric Vehicles (HEVs). Our optimization uses information about a known or estimated vehicle route to predict energy demands and optimally manage on-board battery and fuel energy resources to maximally use grid-sourced electricity and minimally use petroleum resources for a given route. Our convex optimization method uses a simplified car model to find the optimal strategy over the whole route, which allows for re-optimization on the fly as updated route information becomes available. Validation between the simplified model and a more complete vehicle technology model simulation developed at Argonne National Laboratory was accomplished by "driving" the complete car simulation with the simplified control model. By driving on routes with the same total energy demand but different demand profiles we show fuel efficiency gains of 5-15% on mixed urban/suburban routes compared to a Charge Depleting Charge Sustaining (CDCS) battery controller. The method also allows optimizing the economic lifetime of the vehicle battery by considering the stress on the battery from charge and discharge cycles in the resource optimization.
| View file | ||||
|---|---|---|---|---|
|
...
Detecting Simultaneous Changepoints Across Multiple Data Sequences
...