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DTSTART;TZID=America/New_York:20190508T150000
DTEND;TZID=America/New_York:20190508T160000
DTSTAMP:20260406T103241
CREATED:20190426T181812Z
LAST-MODIFIED:20190501T142054Z
UID:9525-1557327600-1557331200@idss-stage.mit.edu
SUMMARY:Representing Short-Term Uncertainties in Capacity Expansion Planning Using an Rolling-Horizon Operation Model
DESCRIPTION:Flexible resources such as batteries and demand-side management technologies are needed to handle future large shares of variable renewable power. Wind and solar power introduce more short-term uncertainty that have to be considered when making investment decisions as it significantly impacts the value of flexible resources. \nIn this work we present a method for using duals from a rolling horizon operational model\, with wind power uncertainty and market representations\, to represent power system operation in an investment problem. The method is based on benders decomposition and special considerations are made due to the nature of the rolling horizon operational framework. \nBio: Espen Flo Bødal is a PhD student from the Norwegian University of Science and Technology (NTNU) in Trondheim\, Norway. He has a master degree in Electric Power Engineering and are currently starting his third year of the PhD on the topic of ”Large-Scale Hydrogen Production for Wind and Hydro Power in Constrained Transmission Grids”. From September 2018 to May 2019 he is a visiting at LIDS with Audun Botterud. \n____________________________________ \nTea talks are 20-minute-long informal chalk-talks for the purpose of sharing ideas and making others aware about some of the topics that may be of interest to the LIDS and Stats audience. If you are interested in presenting in the upcoming tea talks\, please email lids_stats_tea@mit.edu.
URL:https://lids.mit.edu/news-and-events/events/representing-short-term-uncertainties-capacity-expansion-planning-using
LOCATION:32 – LIDS Lounge\, 32 Vassar Street\, Cambridge\, MA\, 02139\, United States
CATEGORIES:LIDS & Stats Tea Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190501T150000
DTEND;TZID=America/New_York:20190501T160000
DTSTAMP:20260406T103241
CREATED:20190424T142934Z
LAST-MODIFIED:20190501T142123Z
UID:9457-1556722800-1556726400@idss-stage.mit.edu
SUMMARY:Generalization and Learning under Dobrushin's Condition
DESCRIPTION:Statistical learning theory has largely focused on learning and generalization given independent and identically distributed (i.i.d.) samples. Motivated by applications involving time-series data\, there has been a growing literature on learning and generalization in settings where data is sampled from an ergodic process. This work has also developed complexity measures\, which appropriately extend Rademacher complexity to bound the generalization error and learning rates of hypothesis classes in this setting. Rather than time-series data\, our work is motivated by settings where data is sampled on a network or a spatial domain\, and thus do not fit well the framework of prior work. We provide learning and generalization bounds for data that are complexly dependent\, yet their distribution satisfies the standard Dobrushin condition. Indeed\, we show that the standard complexity measures of (Gaussian) Rademacher complexity and VC dimension are sufficient measures of complexity for the purposes of bounding the generalization error and learning rates of hypothesis classes in our setting. Moreover\, our generalization bounds only degrade by constant factors compared to their i.i.d. analogs and our learnability bounds degrade by log factors in the size of the training set. \nJoint work with Constantinos Daskalakis\, Nishanth Dikkala\, and Siddhartha Jayanti. \nBio: Yuval Dagan is a PhD student at the EECS department of MIT. He received his Bachelor’s and Master’s degrees from the Technion – Israel Institute of Technology. \n____________________________________ \nTea talks are 20-minute-long informal chalk-talks for the purpose of sharing ideas and making others aware about some of the topics that may be of interest to the LIDS and Stats audience. If you are interested in presenting in the upcoming tea talks\, please email lids_stats_tea@mit.edu.
URL:https://lids.mit.edu/news-and-events/events/generalization-and-learning-under-dobrushins-condition
LOCATION:32 – LIDS Lounge\, 32 Vassar Street\, Cambridge\, MA\, 02139\, United States
CATEGORIES:LIDS & Stats Tea Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190424T150000
DTEND;TZID=America/New_York:20190424T160000
DTSTAMP:20260406T103241
CREATED:20190423T172233Z
LAST-MODIFIED:20190430T195016Z
UID:9425-1556118000-1556121600@idss-stage.mit.edu
SUMMARY:Hierarchical Bayesian Network Model for Probabilistic Estimation of EV Battery Life
DESCRIPTION:Bayesian models are applied to probabilistic analysis of phenomena which deal with multiple external stochastic factors and unmeasurable variables. Considering the large amount of available data for the EV driving\, recharging and grid services such as solar charging which contains uncertainties and measurement errors\, and their hierarchical effect on the battery life\, this application of Bayesian models can be useful for the aging probabilistic analysis. Causality is of utmost importance for batteries as their aging is affected by a high number of hierarchical variables that depend upon external factors to the battery. Acknowledging the advantages of Bayesian models\, we propose a hierarchical Bayesian model for the probabilistic battery degradation evaluation. Priors distributions are defined based on expert knowledge and Marco Chain Monte Carlo (MCMC) sampling is used to draw the posteriors. This modeling approach reflects the uncertainties of measurements and process\, provides more informative results\, and it is applicable to any type of input data with proper training. \nBio: Mehdi Jafari (Ph.D. Michigan Technological University\, 2018; M.Sc. University of Tabriz\, 2011; B.Sc. University of Tabriz\, 2008; all in Electrical Engineering) is a postdoctoral associate in the Laboratory for Information and Decision Systems (LIDS) at MIT. He is working on Energy Storage solutions for the power system applications and renewables integration. He also has worked on probabilistic analysis of the battery energy storage aging behavior\, especially in the electrified transportation and vehicle-to-grid applications. He has authored more than 30 journal and conference papers in the energy storage\, electric vehicles\, renewable energy and power system fields. His current research interests include energy storage role in renewables integration\, battery energy storage performance and degradation in power system and transportation electrification applications. \n____________________________________ \nTea talks are 20-minute-long informal chalk-talks for the purpose of sharing ideas and making others aware about some of the topics that may be of interest to the LIDS and Stats audience. If you are interested in presenting in the upcoming tea talks\, please email lids_stats_tea@mit.edu.
URL:https://lids.mit.edu/news-and-events/events/hierarchical-bayesian-network-model-probabilistic-estimation-ev-battery-life
LOCATION:32 – LIDS Lounge\, 32 Vassar Street\, Cambridge\, MA\, 02139\, United States
CATEGORIES:LIDS & Stats Tea Talks
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