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Demand Response Estimation based on Aggregate Data from Heterogeneous Users

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Demand Response Estimation based on Aggregate Data from Hete...

Mediterranean Conference on control and Automation

作者： Donatello Materassi Mardavij Roozbehani Munther A. Dahleh Department of Electrical Engineering and Computer Science University of Tennessee Laboratory for Information and Decision Systems is with Massachusetts Institute of Technology

ISBN: (纸本)9781467383479

The article presents methodologies for the pre-diction of the aggregate consumption of a large number of heterogeneous energy users with deferrable loads. It is assumed that the users respond to an identical coordinating signal (e.g. a price) broadcast by the system operator. The main contributions of the article are two: (i) the presence of heterogeneous users is explicitely addressed by considering the possibility of several "classes" of consumers;(ii) the described methodologies provide predictions and estimates that have the advantage of making use of aggregate data only. When a dynamic response mechanism is implemented, the presence of consumers with deferrable loads introduces memory in the system in the form of the backlogged demand that has to be satisfied in the near future. From the perspective of the system operator, an accurate estimate of the current backlogged demand is of paramount importance, since it allows one to better predict what the aggregate consumption will be for a given coordinating signal. Thus, mathematical models need to be derived in order to predict future aggregate consumption using the accessible information. Unfortunately, consumers tends to be highly heterogeneous, depending on several factors including individual preferences, the interrupt-ability or the criticality of the tasks and the presence of hard deadlines. These factors can not be measured directly. Indeed, both privacy issues and practical constraints on processing and communicating data are likely to prevent utility companies and system operators from collecting and using measurements at the individual user level. Thus, methodologies for predicting aggregate demand response from aggregate data, such as the ones suggested in the article, can help overcome all these challenges.

关键词： Aggregates Load management Markov processes Mathematical model Data privacy Probabilistic logic Optimization

Distributed kalman filter in a network of linear dynamical systems

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arXiv 2017年

作者： Marelli, Damián Zamani, Mohsen Fu, Minyue Department of Control Science and Engineering State Key Laboratory of Industrial Control Technology Zhejiang University 388 Yuhangtang Road Hangzhou Zhejiang Province310058 French-Argentinean International Center for Information and Systems Sciences National Scientific and Technical Research Council Ocampo Esmeralda Rosario2000 Argentina School of Electrical Engineering and Computer Science University of Newcastle University Drive CallaghanNSW2308 Australia

This paper is concerned with the problem of distributed Kalman filtering in a network of interconnected subsystems with distributed control protocols. We consider networks, which can be either homogeneous or heterogeneous, of linear time-invariant subsystems, given in the state-space form. We propose a distributed Kalman filtering scheme for this setup. The proposed method provides, at each node, an estimation of the state parameter, only based on locally available measurements and those from the neighbor nodes. The special feature of this method is that it exploits the particular structure of the considered network to obtain an estimate using only one prediction/update step at each time step. We show that the estimate produced by the proposed method asymptotically approaches that of the centralized Kalman filter, i.e., the optimal one with global knowledge of all network parameters, and we are able to bound the convergence rate. Moreover, if the initial states of all subsystems are mutually uncorrelated, the estimates of these two schemes are identical at each time step. Copyright © 2017, The Authors. All rights reserved.

关键词： Kalman filters

Bounding the number of non-zero coefficients in minimal peak-to-peak gain shaping filters

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Bounding the number of non-zero coefficients in minimal peak...

American control Conference

作者： Omer Tanovic Alexandre Megretski Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge 02139 USA

ISBN: (纸本)9781467386838

In this paper we consider the task of designing linear time invariant filters which minimize maximal peak-to-peak gain subject to certain "spectral mask" and "no intersymbol interference" constraints. This problem (as well as many other similar questions) can be formalized as infinite dimensional L1 minimization subject to a single convex quadratic constraint. We show that, under the assumption of strict feasibility, every optimal solution corresponds to a finite unit sample response (FIR) filter. Furthermore, a constructive upper bound for the number of nonzero coefficients of the optimal filter is given. The results do not rely on a "restricted isometry" assumption, and potentially offer an alternative method of predicting the degree of sparsity of a solution of a convex quadratic program.

关键词： Optimization Hilbert space Linear systems Minimization Standards Peak to average power ratio Finite impulse response filters Peak to average power ratio FIR filters Linear system Hilbert space peak-to-peak Filters Nonzero optimal filters

Mapping the Human Body at Cellular Resolution -- The NIH Common Fund Human BioMolecular Atlas Program

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arXiv 2019年

作者： Snyder, Michael P. Lin, Shin Posgai, Amanda Atkinson, Mark Regev, Aviv Rood, Jennifer Rozenblatt-Rosen, Orit Gaffney, Leslie Hupalowska, Anna Satija, Rahul Gehlenborg, Nils Shendure, Jay Laskin, Julia Harbury, Pehr Nystrom, Nicholas A. Bar-Joseph, Ziv Zhang, Kun Börner, Katy Lin, Yiing Conroy, Richard Procaccini, Dena Roy, Ananda L. Pillai, Ajay Brown, Marishka Galis, Zorina S. Cai, Long Shendure, Jay Trapnell, Cole Lin, Shin Jackson, Dana Snyder, Michael P. Nolan, Garry Greenleaf, William James Lin, Yiing Plevritis, Sylvia Ahadi, Sara Lee, Hayan Nevins, Stephanie Esplin, Ed Horning, Aaron Bahmani, Amir Zhang, Kun Sun, Xin Jain, Sanjay Hagood, James Kharchenko, Peter Atkinson, Mark Bodenmiller, Bernd Brusko, Todd Clare-Salzler, Michael Nick, Harry Otto, Kevin Posgai, Amanda Wasserfall, Clive Maffioletti, Sergio Caprioli, Richard M. Spraggins, Jeffrey M. Gutierrez, Danielle Patterson, Nathan Heath Neumann, Elizabeth K. Harris, Raymond deCaestecker, Mark de Plas, Raf Van Lau, Ken Cai, Long Yuan, Guo-Cheng Zhu, Qian Dries, Ruben Yin, Peng Saka, Sinem K. Kishi, Jocelyn Y. Wang, Yu Goldaracena, Isabel Laskin, Julia Ye, DongHye Burnum-Johnson, Kristin E. Piehowski, Paul D. Ansong, Charles Zhu, Ying Harbury, Pehr Desai, Tushar Mulye, Jay Chou, Peter Nagendran, Monica Bar-Joseph, Ziv Teichmann, Sarah A. Paten, Benedict Murphy, Robert F. Ma, Jian Kiselev, Vladimir Yu Kingsford, Carl Ricarte, Allyson Ruffalo, Matthew Gehlenborg, Nils Kharchenko, Peter Vella, Margaret McCallum, Chuck Börner, Katy Cross, Leonard E. Friedman, Samuel H. Heiland, Randy Herr, Bruce Macklin, Paul Quardokus, Ellen M. Record, Lisel Sluka, James P. Weber, Griffin M. Nystrom, Nicholas A. Silverstein, Jonathan C. Blood, Philip D. Ropelewski, Alexander J. Shirey, William E. Mabee, Paula Lenhardt, W. Christopher Robasky, Kimberly Michailidis, Stavros Satija, Rahul Marioni, John Regev, Aviv Butler, Andrew Stuart, Tim Fisher, Eyal Ghazanfar, Shila Rood, Jennifer Gaffney, Leslie Ersalan, Gokcen Biancalani, Tommaso Conroy, Richard Procaccini, Dena Roy, Anan School of Medicine Stanford University 300 Pasteur Drive United States Department of Genetics Stanford School of Medicine StanfordCA94305 United States Department of Medicine University of Washington SeattleWA98195 United States Department of Pathology University of Florida Diabetes Institute GainesvilleFL32610 United States Klarman Cell Observatory Broad Institute of MIT and Harvard CambridgeMA02142 United States Howard Hughes Medical Institute Koch Institute of Integrative Cancer Research Department of Biology Massachusetts Institute of Technology CambridgeMA02140 United States New York Genome Center New York CityNY10013 United States New York University New YorkNY10012 United States Department of Biomedical Informatics Harvard Medical School BostonMA02115 United States Department of Genome Sciences University of Washington SeattleWA98105 United States Department of Chemistry Purdue University West LafayetteIN47907 United States Department of Biochemistry Stanford University School of Medicine StanfordCA94305 United States Pittsburgh Supercomputing Center Carnegie Mellon University PittsburghPA15213 United States Department of Computational Biology School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Department of Bioengineering University of California San Diego La JollaCA92093 United States Department of Intelligent Systems Engineering School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Department of Surgery Washington University School of Medicine St LouisMO63110 United States Division of Program Coordination Planning and Strategic Initiatives National Institutes of Health BethesdaMD20892 United States National Human Genome Research Institute National Institutes of Health BethesdaMD20892 United States National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Caltech-UW TMC Departmen

Transformative technologies are enabling the construction of three dimensional (3D) maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible 3D molecular and cellular atlas of the human body, in health and various disease settings. Copyright © 2019, The Authors. All rights reserved.

关键词： Mapping

The (logarithmic) least squares optimality of the arithmetic (geometric) mean of weight vectors calculated from all spanning trees for incomplete additive (multiplicative) pairwise comparison matrices

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arXiv 2017年

作者： Bozóki, Sándor Tsyganok, Vitaliy Laboratory on Engineering and Management Intelligence Research Group of Operations Research and Deci-sion Systems Institute for Computer Science and Control Hungarian Academy of Sciences P.O. Box 63 Budapest1518 Hungary Department of Operations Research and Actuarial Sciences Corvinus University of Budapest Hungary Laboratory for Decision Support Systems Institute for Information Recording of National Academy of Sci-ences of Ukraine 2 Shpak str. Kyiv03113 Ukraine Department of Information Systems and Technologies Inst. of Spec. Communication and Information Protection of National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute

Complete and incomplete additive/multiplicative pairwise comparison matrices are ap- plied in preference modelling, multi-attribute decision making and ranking. The equivalence of two well known methods is proved in this paper. The arithmetic (geometric) mean of weight vectors, calculated from all spanning trees, is proved to be optimal to the (loga- rithmic) least squares problem, not only for complete, as it was recently shown in Lundy, M., Siraj, S., Greco, S. (2017): The mathematical equivalence of the "spanning tree" and row geometric mean preference vectors and its implications for preference analysis, European Journal of Operational Research 257(1) 197{208, but for incomplete matrices as well. Unlike the complete case, where an explicit formula, namely the row arithmetic/geometric mean of matrix elements, exists for the (logarithmic) least squares problem, the incomplete case requires a completely different and new proof. Finally, Kirchhoff's laws for the calculation of potentials in electric circuits is connected to our *** Codes 05C50, 90B50, 94C15 Copyright © 2017, The Authors. All rights reserved.

关键词： decision theory

Flow: A modular learning framework for mixed autonomy traffic

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arXiv 2017年

作者： Wu, Cathy Kreidieh, Abdul Rahman Parvate, Kanaad Vinitsky, Eugene Bayen, Alexandre M. Laboratory For Information And Decision Systems Massachusetts Institute Of Technology CambridgeMA02139 United States Department Of Civil And Environmental Engineering Massachusetts Institute Of Technology CambridgeMA02139 United States Institute Of Data Systems And Society Massachusetts Institute Of Technology CambridgeMA02139 United States Department Of Civil And Environmental Engineering University Of California Berkeley BerkeleyCA94720 United States Department Of Electrical Engineering And Computer Sciences University Of California Berkeley BerkeleyCA94720 United States Department Of Mechanical Engineering University Of California Berkeley BerkeleyCA94720 United States Institute For Transportation Studies University Of California Berkeley BerkeleyCA94720 United States

The rapid development of autonomous vehicles (AVs) holds vast potential for transportation systems through improved safety, efficiency, and access to mobility. However, the progression of these impacts, as AVs are adopted, is not well understood. Numerous technical challenges arise from the goal of analyzing the partial adoption of autonomy: partial control and observation, multi-vehicle interactions, and the sheer variety of scenarios represented by real-world networks. To shed light into near-term AV impacts, this article studies the suitability of deep reinforcement learning (RL) for overcoming these challenges in a low AV-adoption regime. A modular learning framework is presented, which leverages deep RL to address complex traffic dynamics. Modules are composed to capture common traffic phenomena (stop-and-go traffic jams, lane changing, intersections). Learned control laws are found to improve upon human driving performance, in terms of system-level velocity, by up to 57% with only 4-7% adoption of AVs. Furthermore, in single-lane traffic, a small neural network control law with only local observation is found to eliminate stop-and-go traffic - surpassing all known model-based controllers to achieve near-optimal performance - and generalize to out-of-distribution traffic densities. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep learning

Steps toward Parallel Intelligence

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IEEE/CAA Journal of Automatica Sinica 2016年第4期3卷 345-348页

作者： Fei-Yue Wang Xiao Wang Lingxi Li Li Li IEEE State Key Laboratory of Management and Control for Complex Systems (SKL-MCCS) Institute of AutomationChinese Academy of Sciences (CASIA) Research Center of Computational Experiments and Parallel Systems the National University of Defense Technology Qingdao Academy of Intelligent Industries (QAII) QingdaoShandongChinaand SKL-MCCSCASIA Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University Indianapolis Department of Automation Tsinghua University Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies

The origin of artificial intelligence is investigated, based on which the concepts of hybrid intelligence and parallel intelligence are presented. The paradigm shift in Intelligence indicates the ''new normal'' of cyber-social-physical systems (CPSS), in which the system behaviors are guided by Merton's Laws. Thus, the ACP-based parallel intelligence consisting of Artificial societies, Computational experiments and Parallel execution are introduced to bridge the big modeling gap in CPSS. © 2014 Chinese Association of Automation.

关键词： control systems electrical engineering

Event-based state estimation of discrete-state hidden Markov models

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Event-based state estimation of discrete-state hidden Markov...

作者： Shi, Dawei Elliott, Robert J. Chen, Tongwen State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China School of Mathematical Sciences University of Adelaide SA5005 Australia Haskayne School of Business University of Calgary CalgaryAB Canada Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 1H9 Canada

The state estimation problem for hidden Markov models subject to event-based sensor measurement updates is considered in this work, using the change of probability approach. We assume the measurement updates are transmitted through wired or wireless communication networks. For the scenarios with reliable and unreliable communication channels, analytical expressions for the probability distributions of the states conditioned on all the past point- and set-valued measurement information are obtained. Also, we show that the scenario with a lossy channel, but without the event-trigger, can be treated as a special case of the reliable channel results. Based on these results, closed-form expressions for the estimated communication rates under the original probability measure are presented, which are shown to be the ratio between a weighted 1-norm and the 1-norm of the unnormalized conditional probability distributions of the states under the new probability measures constructed. Implementation issues are discussed, and the effectiveness of the results is illustrated by numerical examples and comparative simulations. © 2015 Elsevier Ltd. All rights reserved.

关键词： Hidden Markov models

Estimation and Measurement of Closed-loop Delays in the Actual WACS of Guizhou Power Grid

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Estimation and Measurement of Closed-loop Delays in the Actu...

IEEE Power and Energy Society General Meeting

作者： Fang Zhang Lin Cheng Xiong Li Min Zeng Yuanzhang Sun Joe H. Chow State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Tsinghua University School of Electrical Engineering Wuhan University Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute

ISBN: (纸本)9781509041695

The characterization of the closed-loop delays in a wide-area control system (WACS) is an important foundation for further research. The method to estimate and measure the closed-loop delay in actual WACSs were proposed in this paper. Then, the closed-loop delays in the actual WACS in Guizhou Power Grid in China were measured and analyzed. Also, the measured delays were used to verify the normal distribution estimation model proposed in previous research. The results indicate that the closed-loop delay in the WACS followed a normal distribution.

关键词： CLOSED LOOP Power grids Gaussian distribution delay WACS