Collective behavior of El Farol attendees

Arthur’s paradigm of the El Farol bar for modeling bounded rationality and inductive behavior is undertaken. The memory horizon available to the agents and the selection criteria they utilize for the prediction algorithm are the two essential variables identified to represent the heterogeneity of agent strategies. The latter is enriched by including various rewarding schemes during decision making. Though the external input of comfort level is not explicitly coded in the algorithm pool, it contributes to each agent’s decision process. Playing with the essential variables, one can maneuver the overall outcome between the comfort level and the endogenously identified limiting state. The distribution of algorithm clusters significantly varies for shorter agent memories. This in turn affects the long-term aggregated dynamics of attendances. We observe that a transition occurs in the attendance distribution at the critical memory horizon where the correlations of the attendance deviations take longer time to decay to zero. A larger part of the crowd becomes more comfortable while the rest of the bar-goers still feel the congestion for long memories. Agents’ confidence on their algorithms and the delayed feedback of attendance data increase the overall collectivity of the system behavior

Collective behavior of El Farol attendees

Arthur’s paradigm of the El Farol bar for modeling bounded rationality and inductive behavior is undertaken. The memory horizon available to the agents and the selection criteria they utilize for the prediction algorithm are the two essential variables identified to represent the heterogeneity of agent strategies. The latter is enriched by including various rewarding schemes during decision making. Though the external input of comfort level is not explicitly coded in the algorithm pool, it contributes to each agent’s decision process. Playing with the essential variables, one can maneuver the overall outcome between the comfort level and the endogenously identified limiting state. The distribution of algorithm clusters significantly varies for shorter agent memories. This in turn affects the long-term aggregated dynamics of attendances. We observe that a transition occurs in the attendance distribution at the critical memory horizon where the correlations of the attendance deviations take longer time to decay to zero. A larger part of the crowd becomes more comfortable while the rest of the bar-goers still feel the congestion for long memories. Agents’ confidence on their algorithms and the delayed feedback of attendance data increase the overall collectivity of the system behavior

Digital Twin framework for automated fault source detection and prediction for comfort performance evaluation of existing non-residential Norwegian buildings

Numerous buildings fall short of expectations regarding occupant satisfaction, sustainability, or energy efficiency. In this paper, the performance of buildings in terms of occupant comfort is evaluated using a probabilistic model based on Bayesian networks (BNs). The BN model is founded on an in-depth anal- ysis of satisfaction survey responses and a thorough study of building performance parameters. This study also presents a user-friendly visualization compatible with BIM to simplify data collecting in two case studies from Norway with data from 2019 to 2022. This paper proposes a novel Digital Twin approach for incorporating building information modeling (BIM) with real-time sensor data, occupants’ feedback, a probabilistic model of occupants’ comfort, and HVAC faults detection and prediction that may affect occupants’ comfort. New methods for using BIM as a visualization platform, as well as a pre- dictive maintenance method to detect and anticipate problems in the HVAC system, are also presented. These methods will help decision-makers improve the occupants’ comfort conditions in buildings. However, due to the intricate interaction between numerous equipment and the absence of data integra- tion among FM systems, CMMS, BMS, and BIM data are integrated in this paper into a framework utilizing ontology graphs to generalize the Digital Twin framework so it can be applied to many buildings. The results of this study can aid decision-makers in the facility management sector by offering insight into the aspects that influence occupant comfort, speeding up the process of identifying equipment malfunc- tions, and pointing toward possible solutions.Digital Twin framework for automated fault source detection and prediction for comfort performance evaluation of existing non-residential Norwegian buildingspublishedVersionPaid open acces

The Overall Architecture of a Decision Support System for Public Buildings

AbstractBuilding energy monitoring and real time control strategies can decrease energy consumption on one hand, and improve comfort on the other hand, by increasing the understanding of the control system. A decision support system for building energy management can be a proper tool for supporting the measurement and management of energy usage and costs of public buildings. The aim of the paper is to describe the architecture of the Decision Support System (DSS), that is being developed within the FP7-Smartcities Project OPTIMUS (OPTimising the energy Use in cities with smart decision support system). The architecture of the system is described considering both the energy related and the information technology aspects. An example of action modeling is also presented and the first results are discussed

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Microservices and Machine Learning Algorithms for Adaptive Green Buildings

In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings