CitA BIM Gathering 2021, September 21st – 23rd 2021
From Building Simulation Software to Ontology Language: Using a Calibrated HVAC Model as the Core of a Digital Twin Platform Adalberto Guerra Cabrera1, Graham Darroch2 and Dereje Workie3 Research and Development Integrated Environmental Solutions Limited, Glasgow, United Kingdom E-mail: 1adalberto.cabrera@iesve.com 2graham.darroch@iesve.com 3 dereje.workie@iesve.com Integrated analytics and control applications, such as monitoring-based commissioning, Automated Fault Detection and Diagnostics (AFDD), Predictive Maintenance (PdM), Measurement and Verification (M&V), operational optimisation and demand response, can benefit from the use of building energy simulation (BES) models as virtual testbeds for the evaluation of control strategies and improved energy performance assessments. Hence, the ideal Digital Twin Platform (DTP) solution should enable cross-domain information exchange between virtual and physical assets. However, no solution aiming to integrate a BES model using a standardised convention was found. This paper introduces a Python-based tool that maps the components and variables contained in the Apache Data Model (ADM) to BRICK entities and relationships. The script allows an IES-VE model to be exported as a BRICK model, giving applications access to simulated variables with the same query language as the one used in their existing domains. Two examples where IES-VE models with various systems, equipment and variables being exported as BRICK models are presented. Future research considers a similar solution for other ontology languages such as Project Haystack and RealEstateCore. Keywords ̶ BRICK, IES-VE, Ontology, Standard, Project Haystack, Smart Buildings.
I INTRODUCTION In 2017, building stock accounted for 36% of the energy used globally and was responsible for 39% of the total CO2 emissions [1]. In the UK, buildingrelated industries accounted for 20% of the total annual greenhouse gas emissions in 2018 [2]. The country has set the goal of achieving an 80% reduction in greenhouse gas (GHG) emissions by 2050 against a 1990 baseline [3]. It has been shown that adequate hardware across the building, including submetering, sensors, actuators, and integrated analytics and control software, can save energy by influencing user behaviour, operations optimisation and uncovering inefficiencies only detected when combining multiple data sources [4]. Lawrence Berkeley National Laboratory (LBNL) documented energy analytics enabled primary energy savings ranging from 12 to 30% [5].
Analytics solutions include monitoring-based commissioning [6], Automated Fault Detection and Diagnostics (AFDD) [7], Predictive Maintenance (PdM) [8], Measurement and Verification (M&V) [9], operational optimisation, demand response electricity supply [10], among others. Building energy simulation (BES) can support these analytics-based solutions through baseline modelling for energy-saving estimations in M&V [9], enabling quantitative model-based AFFD [7], and model-based PdM [8]. Additionally, building energy simulation can be used as a virtual testbed for the evaluation of individual [11] and grid [12] level operational optimisation and demand response strategies. Finally, research shows that cross-domain information, e.g. the integration of simulated and metered datasets, is essential for building energy performance assessment [13]. Hence, there is a growing effort for seamlessly integrating building simulated data into the analytics domain. These solutions can be packaged into Digital
Page 48
