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O'Neill Z.,UTRC - United Technologies Research Center | Bailey T.,UTRC - United Technologies Research Center | Eisenhower B.,University of California at Santa Barbara | Fonoberov V.,Aimdyn, Inc
ASHRAE Transactions | Year: 2012

This article presents the calibration of a building energy model of a historic office building developed using the EnergyPlus simulation program. The building under study is the Fleet and Family Support Center located at Napal Station Great Lakes, IL. It was built in 1901 and renovated multiple times. This building has a total floor area of 36,843 ft2 (3424 m2) and mainly consists of offices and conference rooms. An extensive sensitivity study that efficiently perturbs more than two thousand model parameters is employed for model calibration. Those parameters that most affect the building's energy end-use are selected and automatically refined to calibrate the model by applying an analytic meta-model based optimization. Real time data including weather and energy meter data in 2010 is used for model calibration and 2011 data is used for model verification. The modeling process, calibration and verification results, as well as implementation issues encountered throughout the model calibration process from a user's perspective are discussed. The total facility and plug electricity consumption predictions from the calibrated EnergyPlus model match the actual measured monthly data within ±5%. ©2012 ASHRAE.


Eisenhower B.,University of California at Santa Barbara | O'Neill Z.,UTRC - United Technologies Research Center | Narayanan S.,UTRC - United Technologies Research Center | Fonoberov V.A.,Aimdyn, Inc | Mezic I.,University of California at Santa Barbara
Energy and Buildings | Year: 2012

As building energy models become more accurate and numerically efficient, model-based optimization of building design and operation is becoming more practical. The state-of-the-art typically couples an optimizer with a building energy model which tends to be time consuming and often leads to suboptimal results because of the mathematical properties of the energy model. To mitigate this issue, we present an approach that begins by sampling the parameter space of the building model around its baseline. An analytical meta-model is then fit to this data and optimization can be performed using different optimization cost functions or optimization algorithms with very little computational effort. Uncertainty and sensitivity analysis is also performed to identify the most influential parameters for the optimization. A case study is explored using an EnergyPlus model of an existing building which contains over 1000 parameters. When using a cost function that penalizes thermal comfort and energy, 45 annual energy reduction is achieved while simultaneously increasing thermal comfort by a factor of two. We compare the optimization using the meta-model approach with an approach using the EnergyPlus model integrated with the optimizer on a smaller problem using only seven optimization parameters illustrating good performance. © 2011 Elsevier B.V. All rights reserved.


Eisenhower B.,University of California at Santa Barbara | O'Neill Z.,UTRC - United Technologies Research Center | Fonoberov V.A.,Aimdyn, Inc | Mezic I.,University of California at Santa Barbara
Journal of Building Performance Simulation | Year: 2012

As building energy modelling becomes more sophisticated, the amount of user input and the number of parameters used to define the models continue to grow. There are numerous sources of uncertainty in these parameters, especially when the modelling process is being performed before construction and commissioning. Past efforts to perform sensitivity and uncertainty analysis have focused on tens of parameters, while in this work, we increase the size of analysis by two orders of magnitude (by studying the influence of about 1000 parameters). We extend traditional sensitivity analysis in order to decompose the pathway as uncertainty flows through the dynamics, which identifies which internal or intermediate processes transmit the most uncertainty to the final output. We present these results as a method that is applicable to many different modelling tools, and demonstrate its applicability on an example EnergyPlus model.


Hubenko A.,University of California at Santa Barbara | Fonoberov V.A.,Aimdyn, Inc | Mathew G.,UTRC - United Technologies Research Center | Mezic I.,University of California at Santa Barbara
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics | Year: 2011

We present a continuous-space multiscale adaptive search (MAS) algorithm for single or multiple searchers that finds a stationary target in the presence of uncertainty in sensor diameter. The considered uncertainty simulates the influence of the changing environment and terrain as well as adversarial actions that can occur in practical applications. When available, information about the foliage areas and a priori distribution of the target position is included in the MAS algorithm. By adapting to various uncertainties, MAS algorithm reduces the median search time to find the target with a probability of detection of at least PD and a probability of false alarm of at most PFA. We prove that MAS algorithm discovers the target with the desired performance bounds PD and PFA. The unique features of the MAS algorithm are realistic second-order dynamics of the mobile sensors that guarantees uniform coverage of the surveyed area and a two-step Neyman-Pearson-based decision-making process. Computer simulations show that MAS algorithm performs significantly better than lawnmower-type search and billiard-type random search. Our tests suggest that the median search time in the MAS algorithm may be inversely proportional to the number of participating searchers. As opposed to lawnmower search, the median search time in the MAS algorithm depends only logarithmically on the magnitude of uncertainty. © 2011 IEEE.


O'Neill Z.,UTRC - United Technologies Research Center | Eisenhower B.,University of California at Santa Barbara | Yuan S.,UTRC - United Technologies Research Center | Bailey T.,UTRC - United Technologies Research Center | And 2 more authors.
ASHRAE Transactions | Year: 2011

Calibrated energy models are useful for commissioning building systems, measurement and verification (M&V) of building retrofit projects, and predictions of savings from energy conservation measures. This paper presents the modeling and calibration process for building energy models of a DoD (Department of Defense) building. The models are developed using EnergyPlus and TRNSYS simulation programs with measured data from an enhanced building management system (BMS) which includes an on-site weather station. The building under study is the Atlantic Fleet Drill Hall located at Naval Station Great Lakes, IL. This LEED® Gold certified building with a total floor area of 69,218 ft 2 (6,431 m 2) consists of a drill deck and administrative offices. Static data from as-built drawings and dynamic data from building operations are collected and analyzed to create energy models with EnergyPlus and TRNSYS. An extensive sensitivity study by systematically perturbing more than a thousand model input parameters is employed for model calibration. Those parameters that most affect the building energy end-use are selected and refined to calibrate the models. The calibration results, as well as problems encountered throughout the process from the user's perspective, are discussed. The total facility and individual equipment electricity consumption predictions from the calibrated models closely match the measured data. © 2011 ASHRAE.


Fonoberova M.,Aimdyn, Inc | Fonoberov V.A.,Aimdyn, Inc | Mezic I.,University of California at Santa Barbara | Mezic J.,Aimdyn, Inc | Jeffrey Brantingham P.,University of California at Los Angeles
JASSS | Year: 2012

We perform analysis of data on crime and violence for 5,660 U.S. cities over the period of 2005-2009 and uncover the following trends: 1) The proportion of law enforcement officers required to maintain a steady low level of criminal activity increases with the size of the population of the city; 2) The number of criminal/violent events per 1,000 inhabitants of a city shows non-monotonic behavior with size of the population. We construct a dynamical model allowing for system-level, mechanistic understanding of these trends. In our model the level of rational behavior of individuals in the population is encoded into each citizen's perceived risk function. We find strong dependence on size of the population, which leads to partially irrational behavior on the part of citizens. The nature of violence changes from global outbursts of criminal/violent activity in small cities to spatio-temporally distributed, decentralized outbursts of activity in large cities, indicating that in order to maintain peace, bigger cities need larger ratio of law enforcement officers than smaller cities. We also observe existence of tipping points for communities of all sizes in the model: reducing the number of law enforcement officers below a critical level can rapidly increase the incidence of criminal/violent activity. Though surprising, these trends are in agreement with the data.


Zou Y.,Princeton University | Fonoberov V.A.,Aimdyn, Inc | Fonoberova M.,Aimdyn, Inc | Mezic I.,Aimdyn, Inc | And 2 more authors.
Physical Review E - Statistical, Nonlinear, and Soft Matter Physics | Year: 2012

Agent-based modeling (ABM) constitutes a powerful computational tool for the exploration of phenomena involving emergent dynamic behavior in the social sciences. This paper demonstrates a computer-assisted approach that bridges the significant gap between the single-agent microscopic level and the macroscopic (coarse-grained population) level, where fundamental questions must be rationally answered and policies guiding the emergent dynamics devised. Our approach will be illustrated through an agent-based model of civil violence. This spatiotemporally varying ABM incorporates interactions between a heterogeneous population of citizens [active (insurgent), inactive, or jailed] and a population of police officers. Detailed simulations exhibit an equilibrium punctuated by periods of social upheavals. We show how to effectively reduce the agent-based dynamics to a stochastic model with only two coarse-grained degrees of freedom: the number of jailed citizens and the number of active ones. The coarse-grained model captures the ABM dynamics while drastically reducing the computation time (by a factor of approximately 20). © 2012 American Physical Society.


Fonoberova M.,Aimdyn, Inc | Fonoberov V.A.,Aimdyn, Inc | Mezic I.,University of California at Santa Barbara
Reliability Engineering and System Safety | Year: 2013

Agent-based models simulate simultaneous actions and interactions of multiple agents, in an attempt to re-create and predict the appearance of complex phenomena. We propose to use global sensitivity analysis as a tool for analyzing and evaluating agent-based models. A general approach for applying the global sensitivity analysis to agent-based models is presented and tested on the example of a socio-cultural agent-based model we developed earlier [45]. We identify the most significant parameters in the model and uncover their contributions to the outputs of interest. Methodology of model reduction for agent-based models is discussed and demonstrated for the aforementioned model. © 2013 Elsevier Ltd.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: | Award Amount: 149.78K | Year: 2014

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is in the field of predictive policing, with broader applications to crime and terrorism-fighting efforts. The product will be the first software to assimilate real-time crime data and provide dynamic model-based forecasting of policing resources necessary to fight criminal activity in specific locations within a city. Over a third of local police agencies in the US with 100 or more officers have implemented a program similar to Compstat (crime information and record management software), which shows their interest in computer-aided policing. The proposed software will be an independent product, but compatible with such current police software tools. Expansions for anti-terrorist applications are envisioned.

This Small Business Innovation Research Phase I project will address the development of a gang activity agent-based model, from which a software tool for law enforcement decision making - Computer Aided Policing (CAP) - will be developed. An agent-based model of gang activity, which will include individual behavior of an agent or a group in interaction with other agents or groups, as well as an individuals reaction to regulatory, economic and other societal pressures, will be developed based on criminology theory and practical experience. Data management and data mining techniques will be used for development and validation of the agent-based model. Using data assimilation, real-time observations will be incorporated into the model. Tools that enable rapid and automated visualization and analysis of simulation results and the optimization of force deployment for crime mitigation will be developed. This model-based development will be first-of-a-kind in computational social science and will go well beyond the current state of the art in predictive policing.


Grant
Agency: National Science Foundation | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 149.78K | Year: 2014

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is in the field of predictive policing, with broader applications to crime and terrorism-fighting efforts. The product will be the first software to assimilate real-time crime data and provide dynamic model-based forecasting of policing resources necessary to fight criminal activity in specific locations within a city. Over a third of local police agencies in the US with 100 or more officers have implemented a program similar to Compstat (crime information and record management software), which shows their interest in computer-aided policing. The proposed software will be an independent product, but compatible with such current police software tools. Expansions for anti-terrorist applications are envisioned. This Small Business Innovation Research Phase I project will address the development of a gang activity agent-based model, from which a software tool for law enforcement decision making - "Computer Aided Policing" (CAP) - will be developed. An agent-based model of gang activity, which will include individual behavior of an agent or a group in interaction with other agents or groups, as well as an individual's reaction to regulatory, economic and other societal pressures, will be developed based on criminology theory and practical experience. Data management and data mining techniques will be used for development and validation of the agent-based model. Using data assimilation, real-time observations will be incorporated into the model. Tools that enable rapid and automated visualization and analysis of simulation results and the optimization of force deployment for crime mitigation will be developed. This model-based development will be first-of-a-kind in computational social science and will go well beyond the current state of the art in predictive policing.

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