The first world championship in cybernetic building optimization

ABSTRACT The World Championship in Cybernetic Building Optimization (WCCBO) was held in 2019 to test participants’ ability to optimize buildings cybernetically. Office buildings with a total floor area of 10,000 m2 were built in cyberspace, one for each of the 33 participating teams. The cyber buildings were controlled by BACnet, and the participants competed to show their operational skills by tuning the HVAC system of their respective cyber buildings online. The ability of optimization was evaluated in terms of both energy consumption and thermal comfort, and their scores were published online in real-time. A total of 339 different operations were tested during the two-month competition period. The top-ranked team succeeded in reducing energy consumption and thermally dissatisfied occupant rate by 12.1% and 21.0%, respectively. In this paper, we report on the examination of the rules and schedule of this championship as well as the analysis of the participants’ scores.

[1]  Adolf Acquaye,et al.  Operational vs. embodied emissions in buildings—A review of current trends , 2013 .

[2]  Jeff Haberl,et al.  The Great Energy Predictor Shootout II : Measuring Retriofit Savings-Overview and Discussion of Results , 1996 .

[3]  Gregor P. Henze,et al.  Statistical Analysis of Neural Networks as Applied to Building Energy Prediction , 2004 .

[4]  T. M. Leung,et al.  A review on Life Cycle Assessment, Life Cycle Energy Assessment and Life Cycle Carbon Emissions Assessment on buildings , 2015 .

[5]  Simon Rouchier,et al.  Solving inverse problems in building physics: An overview of guidelines for a careful and optimal use of data , 2018 .

[6]  Christoph van Treeck,et al.  MVD based information exchange between BIM and building energy performance simulation , 2018, Automation in Construction.

[7]  Pieter de Wilde,et al.  The gap between predicted and measured energy performance of buildings: A framework for investigation , 2014 .

[8]  Jeff Haberl,et al.  Developing a physical BIM library for building thermal energy simulation , 2015 .

[9]  P. O. Fanger,et al.  Thermal comfort: analysis and applications in environmental engineering, , 1972 .

[10]  Sei Ito,et al.  Development of Test Procedure for the Evaluation of Building Energy Simulation Tools-Phase II Expansion of Evaluation Targets and Results of Simulation Trials - , 2017 .

[11]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[12]  S. Kaewunruen,et al.  A Digital-Twin Evaluation of Net Zero Energy Building for Existing Buildings , 2018, Sustainability.

[13]  Philippe Rigo,et al.  A review on simulation-based optimization methods applied to building performance analysis , 2014 .

[14]  Arno Schlueter,et al.  Coupled simulation of thermally active building systems to support a digital twin , 2019, Energy and Buildings.

[15]  Steven T. Bushby,et al.  The Virtual Cybernetic Building Testbed-A Building Emulator | NIST , 2009 .

[16]  Carsten Peterson,et al.  Predicting System loads with Artificial Neural Networks : Method and Result from "the Great Energy Predictor Shootout" , 1994 .

[17]  Masato Miyata,et al.  Development of building thermal environment emulator to evaluate the performance of the HVAC system operation , 2019, Journal of Building Performance Simulation.

[18]  Steven T. Bushby,et al.  Using the Virtual Cybernetic Building Testbed and FDD Test Shell for FDD Tool Development , 2001 .

[19]  Pierre Hollmuller,et al.  Understanding and bridging the energy performance gap in building retrofit , 2017 .

[20]  Anne Grete Hestnes,et al.  Energy use in the life cycle of conventional and low-energy buildings: A review article , 2007 .

[21]  Ryo Takeuchi,et al.  A piecewise-linear regression on the ASHRAE time-series data , 1994 .

[22]  B. P. Feuston,et al.  Generalized nonlinear regression with ensemble of neural nets: The great energy predictor shootout , 1994 .

[23]  Xin Wang,et al.  A new approach, based on the inverse problem and variation method, for solving building energy and environment problems: Preliminary study and illustrative examples , 2015 .

[24]  J. F. Kreider.  Prediction Hourly Building Energy Use : The Great Energy Predictor Shootout - Overview and Discussion of Results , 1994 .

[25]  Rita Streblow,et al.  Implementation of Advanced Bim-Based Mapping Rules for Automated Conversions to Modelica , 2015, Building Simulation Conference Proceedings.