ObepME: An online building energy performance monitoring and evaluation tool to reduce energy performance gaps

Abstract A major challenge facing the buildings sector is the absence of continuous commissioning and the lack of performance monitoring and evaluation leading to buildings energy performance gaps between predicted and actual measured performance. Aiming to better characterize, evaluate and bridge these gaps, the paper proposes an online building energy performance monitoring and evaluation tool ObepME, serving as a basis for fault detection and diagnostics and forming a backbone for continuous commissioning. A calibrated building dynamic energy model is developed and employed to automatically run on a daily basis and simulate the building transient performance for the previous day. The simulated energy consumption results form a baseline to which the actual collected data are compared to evaluate the dynamic energy performance gap. The OU44 University building in Denmark is considered as a case study to implement the proposed framework. A holistic energy model was developed in EnergyPlus and calibrated employing data from various building meters, collected weather conditions, generated occupancy schedules and systems operational parameters and set-points. The calibrated model was employed in the ObepME tool to automatically and continuously monitor and evaluate the OU44 building energy performance, on the level of the whole building and individual energy systems consumption, throughout the period from February to mid-March 2017. The reported dynamic energy performance gap was around −2.85%, −3.47% and 5.48% for heating, total electricity and ventilation system electricity consumption. In addition, specific observations were made on a daily basis in terms of the overall electricity, heating, lighting and ventilation energy consumption as highlighted by the ObepME tool. The ObepME tool is currently running automatically as a part of the OU44 building continuous commissioning and performance evaluation aiming to identify possible discrepancies and deviations paving the way for a methodical preventive fault detection and diagnostics process on various levels in the building.

[1]  Mikkel Baun Kjærgaard,et al.  Challenge: Advancing Energy Informatics to Enable Assessable Improvements of Energy Performance in Buildings , 2015, e-Energy.

[2]  Balaji Rajagopalan,et al.  Model-predictive control of mixed-mode buildings with rule extraction , 2011 .

[3]  Mikkel Baun Kjærgaard,et al.  PLCount: A Probabilistic Fusion Algorithm for Accurately Estimating Occupancy from 3D Camera Counts , 2016, BuildSys@SenSys.

[4]  Moncef Krarti,et al.  Predictive Optimal Control of Active and Passive Building Thermal Storage Inventory , 2003 .

[5]  Adrian Leaman,et al.  Assessing building performance in use 3: energy performance of the Probe buildings , 2001 .

[6]  Lars Lisell,et al.  Cloud-Based Model Calibration Using OpenStudio , 2014 .

[7]  María Herrando,et al.  Energy Performance Certification of Faculty Buildings in Spain: The gap between estimated and real energy consumption , 2016 .

[8]  Neil Brown,et al.  Improved occupancy monitoring in non-domestic buildings , 2017 .

[9]  Dimitrios Gyalistras,et al.  Performance gaps in Swiss buildings: an analysis of conflicting objectives and mitigation strategies , 2017 .

[10]  Markku Hienonen,et al.  The Importance of Building Physics in Improving the Quality Control of Buildings – The Role of Public Authority , 2017 .

[11]  Martin Kumar Patel,et al.  Actual energy performance of student housing: case study, benchmarking and performance gap analysis , 2017 .

[12]  Rory V. Jones,et al.  Driving factors for occupant-controlled space heating in residential buildings , 2014 .

[13]  Steven J. Emmerich,et al.  Improving infiltration modeling in commercial building energy models , 2015 .

[14]  Mahdi Safa,et al.  Improving sustainable office building operation by using historical data and linear models to predict energy usage , 2017 .

[15]  Christian Anker Hviid,et al.  The European Energy Performance of Buildings Directive: Comparison of calculated and actual energy use in a Danish office building , 2012 .

[16]  D Miles-Shenton,et al.  Low carbon housing: lessons from Elm Tree Mews , 2010 .

[17]  D. Kolokotsa,et al.  Evaluation of the performance gap in industrial, residential & tertiary near-Zero energy buildings , 2017 .

[18]  Dino Bouchlaghem,et al.  Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap , 2012 .

[19]  Jiechao Li.  A software approach for combining real time data measurement and building energy model to improve energy efficiency , 2014 .

[20]  Bo Nørregaard Jørgensen,et al.  Deep Energy Renovation of the Mærsk Office Building in Denmark Using a Holistic Design Approach , 2017 .

[21]  Frauke Oldewurtel,et al.  Building modeling as a crucial part for building predictive control , 2013 .

[22]  Paul Raftery,et al.  Calibrating whole building energy models: An evidence-based methodology , 2011 .

[23]  Catalina Spataru,et al.  Corrigendum: A Review of the Energy Performance Gap and Its Underlying Causes in Non-Domestic Buildings , 2016, Front. Mech. Eng..

[24]  Jose M. Adam,et al.  Designing construction processes in buildings by heuristic optimization , 2016 .

[25]  Vincent Lemort,et al.  From model validation to production of reference simulations: how to increase reliability and applicability of building and HVAC simulation models , 2008 .

[26]  Dionysia Kolokotsa,et al.  The role of smart grids in the building sector , 2016 .

[27]  Catalina Spataru,et al.  A Review of the Regulatory Energy Performance Gap and Its Underlying Causes in Non-domestic Buildings , 2016, Front. Mech. Eng..

[28]  Shahryar Habibi,et al.  The promise of BIM for improving building performance , 2017 .

[29]  Miguel Á. Carreira-Perpiñán,et al.  OBSERVE: Occupancy-based system for efficient reduction of HVAC energy , 2011, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks.

[30]  Miguel Molina-Solana,et al.  Data science for building energy management: A review , 2017 .

[31]  Gyunghyun Choi,et al.  Study of construction convergence technology for performance improvement in functional building materials , 2017 .

[32]  Benjamin C. M. Fung,et al.  A methodology for identifying and improving occupant behavior in residential buildings , 2011 .

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

[34]  Jlm Jan Hensen,et al.  Evaluating energy performance in non-domestic buildings : a review , 2016 .

[35]  Kristian Fabbri,et al.  A Round Robin Test for buildings energy performance in Italy , 2010 .

[36]  Philip Haves,et al.  DEVELOPMENT OF A USER INTERFACE FOR THE ENERGYPLUS WHOLE BUILDING ENERGY SIMULATION PROGRAM , 2011 .

[37]  Joseph H. M. Tah,et al.  A framework for the utilization of Building Management System data in building information models for building design and operation , 2016 .

[38]  Peter G. Taylor,et al.  Performance gap analysis case study of a non-domestic building , 2016 .

[39]  Ljubomir Jankovic.  A METHOD FOR REDUCING SIMULATION PERFORMANCE GAP USING FOURIER FILTERING , 2013 .

[40]  Pieter de Wilde,et al.  Predictability of occupant presence and performance gap in building energy simulation , 2017 .

[41]  Chirag Deb,et al.  Energy performance model development and occupancy number identification of institutional buildings , 2016 .

[42]  Amrita Dasgupta,et al.  Operational versus designed performance of low carbon schools in England: Bridging a credibility gap , 2011, HVAC&R Research.

[43]  Olufolahan Oduyemi,et al.  Building performance modelling for sustainable building design , 2016 .