TY - JOUR

T1 - Quick energy prediction and comparison of options at the early design stage

AU - Singh, Manav Mahan

AU - Singaravel, Sundaravelpandian

AU - Klein, Ralf

AU - Geyer, Philipp Florian

N1 - Funding Information: The authors want to acknowledge the support of Deutsche Forschungsgemeinschaft (DFG), Germany for funding the research through the grant GE1652/3-1 within research unit FOR 2363. The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation - Flanders (FWO) and the Flemish Government – department EWI, Belgium. We would like to express our sincere gratitude to the Institute of Energy Efficient and Sustainable Design and Building, Technical University, Munich (TUM) and Ferdinand Tausendpfund GmbH & Co. KG for providing energy consumption and design data for Tausendpfund building.

PY - 2020/10

Y1 - 2020/10

N2 - The energy-efficient building design requires building performance simulation (BPS) to compare multiple design options for their energy performance. However, at the early stage, BPS is often ignored, due to uncertainty, lack of details, and computational time. This article studies probabilistic and deterministic approaches to treat uncertainty; detailed and simplified zoning for creating zones; and dynamic simulation and machine learning for making energy predictions. A state-of-the-art approach, such as dynamic simulation, provide a reliable estimate of energy demand, but computationally expensive. Reducing computational time requires the use of an alternative approach, such as a machine learning (ML) model. However, an alternative approach will cause a prediction gap, and its effect on comparing options needs to be investigated. A plugin for Building information modelling (BIM) modelling tool has been developed to perform BPS using various approaches. These approaches have been tested for an office building with five design options. A method using the probabilistic approach to treat uncertainty, detailed zoning to create zones, and EnergyPlus to predict energy is treated as the reference method. The deterministic or ML approach has a small prediction gap, and the comparison results are similar to the reference method. The simplified model approach has a large prediction gap and only makes only 40% comparison results are similar to the reference method. These findings are useful to develop a BIM integrated tool to compare options at the early design stage and ascertain which approach should be adopted in a time-constraint situation.

AB - The energy-efficient building design requires building performance simulation (BPS) to compare multiple design options for their energy performance. However, at the early stage, BPS is often ignored, due to uncertainty, lack of details, and computational time. This article studies probabilistic and deterministic approaches to treat uncertainty; detailed and simplified zoning for creating zones; and dynamic simulation and machine learning for making energy predictions. A state-of-the-art approach, such as dynamic simulation, provide a reliable estimate of energy demand, but computationally expensive. Reducing computational time requires the use of an alternative approach, such as a machine learning (ML) model. However, an alternative approach will cause a prediction gap, and its effect on comparing options needs to be investigated. A plugin for Building information modelling (BIM) modelling tool has been developed to perform BPS using various approaches. These approaches have been tested for an office building with five design options. A method using the probabilistic approach to treat uncertainty, detailed zoning to create zones, and EnergyPlus to predict energy is treated as the reference method. The deterministic or ML approach has a small prediction gap, and the comparison results are similar to the reference method. The simplified model approach has a large prediction gap and only makes only 40% comparison results are similar to the reference method. These findings are useful to develop a BIM integrated tool to compare options at the early design stage and ascertain which approach should be adopted in a time-constraint situation.

KW - Building Information Modelling (BIM)

KW - Energy simulation

KW - Machine learning

KW - Prediction gap

KW - Probabilistic

KW - Zoning

UR - http://www.scopus.com/inward/record.url?scp=85094898571&partnerID=8YFLogxK

U2 - 10.1016/j.aei.2020.101185

DO - 10.1016/j.aei.2020.101185

M3 - Article

AN - SCOPUS:85094898571

VL - 46

JO - Advanced engineering informatics

JF - Advanced engineering informatics

SN - 1474-0346

M1 - 101185

ER -