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

Abstract

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. To reduce 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 the 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 timeconstraint situation.

Utilização do BIM como apoio à simulação para análise do desempenho das edificações:

“…Na Tabela 4, existem muitas possibilidades de escolha de ferramentas conforme usos específicos, então é necessário analisar o enfoque da pesquisa para depois escolher os softwares que serão mais assertivos dentro do uso pretendido para o modelo. O cuidado na fase operacional e de gerenciamento é importante para melhoria da avaliação de desempenho da edificação [18]. A análise e utilização de ferramentas de simulação desde os estágios iniciais do projeto permite a integração com ferramentas de design, tornam o flexível, trazendo alternativas projetuais e de requisitos, tais como elementos qualitativos e quantitativos, evitando conflitos entre disciplinas.…”

Machine learning-based energy use prediction for the smart building energy management system

“…The study stated that the conventional tools for simulating building performance might be replaced with the ML method due to its high reliability and low computational times. Moreover, Singh et al (2020) have developed energy predictions utilizing four schematic predictive approaches, one of which is a long shortterm memory (LSTM)-based ML method that claims to have a faster computation time than the EnergyPlus simulation. On the other hand, Westermann et al (2020) used the convolutional neural network (CNN) algorithm to produce an ML model to displace a building energy modeling tool that can accurately predict thermal energy with varied building designs and climate variations as inputs.…”

BIM-based design decisions documentation using design episodes, explanation tags, and constraints

“…Matern and König introduced an approach for managing various design variants across multiple planning stages in a consistent digital building model (Mattern and König, 2018). Geyer and Singaravel showed that engineering surrogate models based on components and machine learning (ML) can predict energy demand with the required accuracy in the early stages of design (Geyer and Singaravel, 2018) and with a small prediction gap in comparison to the dynamic simulation approach (Singh et al, 2020).…”