Uncertainty Analysis of a Heavily Instrumented Building at Different Scales of Simulation

Simulation plays a big role in understanding the behavior of building envelopes. With the increasing availability of computational resources, it is feasible to conduct parametric simulations for applications such as software model calibration, building control optimization, or fault detection and diagnostics. In this paper, we present an uncertainty exploration of a building envelope’s thermal conductivity properties for a heavily instrumented residential building involving more than 200 sensors. A total of 156 input parameters were determined to be important by experts, which were then varied using a Markov Order process. Depending on the number of simulations in an ensemble, the techniques employed to meaningfully make sense of the information can be very different, and potentially challenging. This paper discusses different strategies one could employ when the number of simulation range from a few to tens of thousands of simulations in an ensemble. The paper highlights this and the associated computational challenge in the context of ensemble simulations where the chosen sampling process allows one to generate datasets consisting of just of a few simulations to an exponentially large intractable dataset with data in the hundreds of terabytes. Besides the computational and data management challenges, the paper will also presents meaningful visualization approaches that are candidates for extreme scale analysis. The method of analysis almost always depends on the experimental design. While Markov Ordering for sampling will be explicitly presented, the paper will also touch upon various other experimental design strategies and their resulting analysis methods in the context of scientific simulations. We expect the sampling and ensemble analysis at various scales to help us gain insight into unique issues of building energy modeling, especially at different scales of simulation. We also expect the analytic approaches employed for understanding the thermal properties of building envelopes to be beneficial for software calibration and building design. We demonstrate these in the context of a real-world, heavily instrumented building

Critical analysis for big data studies in construction: significant gaps in knowledge

Purpose The purpose of this paper is to identify the gaps and potential future research avenues in the big data research specifically in the construction industry. Design/methodology/approach The paper adopts systematic literature review (SLR) approach to observe and understand trends and extant patterns/themes in the big data analytics (BDA) research area particularly in construction-specific literature. Findings A significant rise in construction big data research is identified with an increasing trend in number of yearly articles. The main themes discussed were big data as a concept, big data analytical methods/techniques, big data opportunities – challenges and big data application. The paper emphasises “the implication of big data in to overall sustainability” as a gap that needs to be addressed. These implications are categorised as social, economic and environmental aspects. Research limitations/implications The SLR is carried out for construction technology and management research for the time period of 2007–2017 in Scopus and emerald databases only. Practical implications The paper enables practitioners to explore the key themes discussed around big data research as well as the practical applicability of big data techniques. The advances in existing big data research inform practitioners the current social, economic and environmental implications of big data which would ultimately help them to incorporate into their strategies to pursue competitive advantage. Identification of knowledge gaps helps keep the academic research move forward for a continuously evolving body of knowledge. The suggested new research avenues will inform future researchers for potential trending and untouched areas for research. Social implications Identification of knowledge gaps helps keep the academic research move forward for continuous improvement while learning. The continuously evolving body of knowledge is an asset to the society in terms of revealing the truth about emerging technologies. Originality/value There is currently no comprehensive review that addresses social, economic and environmental implications of big data in construction literature. Through this paper, these gaps are identified and filled in an understandable way. This paper establishes these gaps as key issues to consider for the continuous future improvement of big data research in the context of the construction industry

Towards Seamless IoT Device-Edge-Cloud Continuum:

In this paper we revisit a taxonomy of client-side IoT software architectures that we presented a few years ago. We note that the emergence of inexpensive AI/ML hardware and new communication technologies are broadening the architectural options for IoT devices even further. These options can have a significant impact on the overall end-to-end architecture and topology of IoT systems, e.g., in determining how much computation can be performed on the edge of the network. We study the implications of the IoT device architecture choices in light of the new observations, as well as make some new predictions about future directions. Additionally, we make a case for isomorphic IoT systems in which development complexity is alleviated with consistent use of technologies across the entire stack, providing a seamless continuum from edge devices all the way to the cloud.Peer reviewe

Page 1 3 rd International High Performance Buildings Conference at Purdue

ABSTRACT Simulation plays a big role in understanding the behavior of building envelopes. With the increasing availability of computational resources, it is feasible to conduct parametric simulations for applications such as software model calibration, building control optimization, or fault detection and diagnostics. In this paper, we present an uncertainty exploration of two types of buildings: a) of a building envelope's thermal conductivity properties for a heavily instrumented residential building involving more than 200 sensors, and b) a sensitivity analysis of a stand-alone retail building from the U.S. Department of Energy's reference model. A total of 156 input parameters were determined to be important by experts which were then varied using a Markov Order process for the residential building generating hundreds of GBs of data for tens of thousands of simulations. For the commercial building, 20 parameters were varied using a fractional factorial design requiring just 1024 simulations generating data in the order of a few hundred megabytes. These represent a wide variety and range of simulations from a few to tens of thousands of simulations in an ensemble. Depending on the number of simulations in an ensemble, the techniques employed to meaningfully make sense of the information can be very different, and potentially challenging. Additionally, the method of analysis almost always depends on the experimental design. The Markov Order sampling strategy and fractional factorials designs of sampling presented represent two approaches one could employ for large sensitivity analysis of buildings at two different scales of simulations. The paper presents the analysis using descriptive statistics as well as employing multiple analysis of variance techniques for comparison and contrast

HPC applications for data-driven agent-based models of pedestrian movement

La gestió de grans instal·lacions multiús és un procés complicat, que té a veure en trobar un equilibri entre la satisfacció del client, aspectes de seguretat i els interessos comercials. Aquest repte s'accentua en períodes de transició, com èpoques de construcció o la millora i manteniment de la instal·lació. Tot i això, amb el creixement de l'Internet of Things(IoT) i l'accés a HPC per a usos comercials als darrers anys ha proporcionat una manera d'adreçar aquest repte a través d'aquestes tecnologies. Les dades provinents d'aquests sistemes fortament monitorats, combinats amb IA i tècniques de simulació, permeten una nova manera d'abordar la gestió de grans instal·lacions. En aquest Treball de Fi de Grau s'ha desenvolupat un Digital Twin del recinte insígnia del Futbol Club Barcelona: el Camp Nou. L'aspecte més important en el funcionament del recinte són els fluxos de vianants i la seva optimització, assegurar un pla robust enfront de les emergències i gestionar els canvis relacionats amb el projecte de construcció que consisteix en la renovació del recinte del Camp Nou també són prioritaris. Aquest prototip de Model de Moviment de Vianants mostra la viabilitat de combinar diverses fonts de dades amb l'objectiu de representar diversos escenaris relacionats amb la gestió de multituds. Aquest model servirà com a referència per integrar les dades provinents de sensors i preprocessades utilitzant tècniques de Machine Learning. Aquest model estarà integrat amb l'estructura de tot IoTwins dins els test-beds 5 i 11 que se centren en les instal·lacions del FCB. El mètode escollit per la simulació del projecte és la Simulació Basada en Agents. Un paradigma de simulació cada vegada més popular que és capaç de representar poblacions heterogènies que estan formades per agents individuals que representen els vianants, per exemple. El seu moviment està definit per un algoritme desenvolupat específicament que representa l'espai al voltant dels Agents de manera matemàtica, derivada d'una funció de cost que combina diferents factors que afecten els vianants. Els Agents entren al recinte, es mouen seguint les seves prioritats i després en surten seguint el seu camí individual. El model es validarà i calibrarà amb les dades accessibles en aquest moment. Diversos escenaris d'exemple han demostrat la viabilitat del model per optimitzar l'evacuació en cas d'emergència i les afectacions que comporten les renovacions del recinte.The management of large multinational facilities is a complex process involving finding the balance between customer satisfaction, safety concerns and commercial interests. This challenge is particularly pronounced in periods of transitions, such as stages construction work, facility upgrade and maintenance. However, with the growth of the Internet of Things (IoT) and unlocking of HPC for commercial endeavours in recent years offers to address this challenge through the use of technology. The increasing amount of data coming from these heavily monitored system, combined with AI and simulation techniques offers a new approach to the management of large facilities. In this Bachelor Thesis' a Digital Twin of the Football Club Barcelona flagship sports venue: the Camp Nou has been developed. The most important aspect in the functioning of the venue are pedestrian flows and optimising them, ensuring robust emergency planning and managing change related to phased construction project involving a full renovation of the Camp Nou precinct are the main priorities. This prototype of Pedestrian Movement Model shows the feasibility of combining various data streams to represent multiple scenarios of crowd management. This model will serve as a baseline for integrating data coming from a number of sensors and preprocessed with Machine Learning techniques. This model will be integrated as a part of the whole IoTwins structure of test-beds 5 and 11 that focus on the FCB facilities. The approach taken in the simulation part of the project is Agent Based Modelling. An increasingly popular simulation technique that is able to represent heterogeneous population consisting of individual Agents, for example, representing pedestrians. Their movement is defined with an specially developed algorithm that represent the space around the Agents as a mathematically derived cost function that combines multiple factors affecting the movement of pedestrians. The Agents enter the precinct, move around it and leave it following their independent paths. The model is validated and calibrated using currently available data. Several example scenarios have been run to show the feasibility of the approach for optimising emergency evacuation and construction-caused disruptions to normal operations

Building performance simulation in the brave new world of Artificial Intelligence and Digital Twins : a systematic review

In an increasingly digital world, there are fast-paced developments in fields such as Artificial Intelligence, Machine Learning, Data Mining, Digital Twins, Cyber-Physical Systems and the Internet of Things. This paper reviews and discusses how these new emerging areas relate to the traditional domain of building performance simulation. It explores the boundaries between building simulation and these other fields in order to identify conceptual differences and similarities, strengths and limitations of each of these areas. The paper critiques common notions about these new domains and how they relate to building simulation, reviewing how the field of building performance may evolve and benefit from the new developments

NASA SBIR abstracts of 1990 phase 1 projects

The research objectives of the 280 projects placed under contract in the National Aeronautics and Space Administration (NASA) 1990 Small Business Innovation Research (SBIR) Phase 1 program are described. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses in response to NASA's 1990 SBIR Phase 1 Program Solicitation. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 280, in order of its appearance in the body of the report. The document also includes Appendixes to provide additional information about the SBIR program and permit cross-reference in the 1990 Phase 1 projects by company name, location by state, principal investigator, NASA field center responsible for management of each project, and NASA contract number

Research summary, January 1989 - June 1990

The Research Institute for Advanced Computer Science (RIACS) was established at NASA ARC in June of 1983. RIACS is privately operated by the Universities Space Research Association (USRA), a consortium of 62 universities with graduate programs in the aerospace sciences, under a Cooperative Agreement with NASA. RIACS serves as the representative of the USRA universities at ARC. This document reports our activities and accomplishments for the period 1 Jan. 1989 - 30 Jun. 1990. The following topics are covered: learning systems, networked systems, and parallel systems

A New Bayesian Inference Calibration Platform for Building Energy and Environment Predictions

Buildings account for nearly 40% of total global energy consumption. It is predicted that by 2050 the combined energy consumptions of the residential and commercial sectors will have increased to 22% of the world's total delivered energy. Moreover, requirements for indoor health, safety, thermal comfort, and air quality have become more demanding due to more intensive and frequent extreme climate events, such as heatwaves and cold waves. Such issues have become challenging for the building energy and environment field, especially during the COVID-19 pandemic. Computer simulations play a crucial role in achieving a safe, healthy, comfortable, and sustainable indoor environment. As an integral step in the development of the building models, model calibration can significantly affect simulation results, model accuracy, and model-based decision-making. Conventional calibration methods, however, are often deterministic. As a result, the uncertainties that have been investigated for a building computer model, and those from the inputs have not been given adequate attention and are thus worth studying in more depth. Bayesian Inference is one of the most effective approaches to calibrating computer models with uncertainties. Several studies have explored its application in building energy modeling, but a comprehensive application in the general field of building energy and environment has not been adequate. This thesis started with a comprehensive literature review of Bayesian Inference calibration focusing on building energy modeling. Then, a systematic Bayesian calibration workflow and a new platform were developed. As well as a general study of its application for the predictions of building energy performance, the thesis investigated how to use the platform to calibrate thermal models of buildings and indoor air quality models. To solve the issue of the computing cost of Bayesian Inference, another calibration and prediction method, Ensemble Kalman Filter (EnKF), was proposed and applied to the estimation of ventilation performance and predictions of free cooling load. The conclusion includes a summary of the contributions of this thesis and suggestions for future work