Co-simulation of dynamic underground heat transfer with building energy modeling based on equivalent slab method

Improving accuracy in building energy simulation via evaluating occupant behaviors: A case study in Hong Kong

Mapping demand for residential building thermal energy services using airborne LiDAR

Rainfall identification and estimation in Guangzhou area used for building energy simulation

Shoeboxer: An algorithm for abstracted rapid multi-zone urban building energy model generation and simulation

Dynamic heat transfer model of vertical green façades and its co-simulation with a building energy modelling program in hot-summer/warm-winter zones

Energy consumption analysis intended for real office space with energy recovery ventilator by integrating BES and CFD approaches

Buildings account for nearly 40% of the global process and industrial greenhouse gas emissions, and within the building sector. And within the building sector, nearly 72% of the CO2 emissions are due operational carbon demand in the building sector. To meet the goals set out in the Paris Accord, we need to halve carbon emissions by the end of this decade: an aggressive and ambitious timeline. It has become an immediate and urgent need to design and construct buildings that are minimally intrusive in their impact on the environment and operate efficiently. Improvement of the building's energy performance must begin at the early design stage as the potential for sustainable intervention in the project's early phases is higher. The opportunity to improve building performance as the design progresses, is constantly reducing, while the cost of optimization is constantly increasing. One of the major design variables impacting the performance at the early phase is the building morphology/form and its associated variables. Sensible optioneering of the building form and its variables at the early stages of design - especially the conceptual design stage - can help improve the performance of the design early on in the design process at minimum cost. However, for effective early-phase design optimisation, there is a need to develop tools/methods that allow instantaneous evaluation of a large design sample space without much “lag” between the design and performance evaluation workflows. Also, in regular early phase sustainable design workflows, multiple stakeholders - like the designer, the performance analyst, etc. - are involved, which leads to a cognitive divide in the design process. For instance, while the architect designs the initial conceptual massing with a functional design intuition, the performance analysts suggest design interventions with a performance oriented design intuition. This cognitive divide leads to a dipartite design cognition in early phase design. The underlying premise of this research is to support the notion of blurring the dichotomy between conflicting design intuitions through exploring Human + Artificial Intelligence (AI) synergies and their underlying foundational technological requirements with respect to early phase design, to enable intuitive high performance design scenarios with a centralized design cognition where there are no other stakeholders other than the designer and the design environment. With the paradigm shift of other industries towards Machine Learning (ML) and AI, recent research advances in ML and the increasing availability of Big-data in the AEC industry have bolstered multivariate problems like building performance evaluation to a great extent. Design optioneering with instantaneous performance feedback through a ML method called Surrogate Modelling is an upcoming and promising methodology, which delivers feedback based on knowledge through available data, rather than simulation. Also, paradigms in Concurrent Human Machine Interaction (HMI) have explored avenues of augmenting the architectural design process through data-driven approaches. The type of data (input-output pairs) forming the basis for these paradigms depend on the target problem at hand. For early phase performance optimization - especially energy use optimization - Building Energy Model (BEM) forms the primary data. The US Department of Energy defines BEM as a versatile, multipurpose tool that is used in new building and retrofit design, code compliance, green certification, qualification for tax credits and utility incentives, and even real-time building control. BEM is also used in large-scale analyses to develop building energy-efficiency codes and inform policy decisions. A comprehensive BEM dataset that is large and accessible can support the growing interest in ML and HMI research in the high performance building design field. However, common techniques to acquire large BEM datasets like manual 3D energy modeling and simulation, 3D scanning, etc. are very tedious and time consuming. Also, existing datasets are usually incomplete, inconsistent and very difficult to access, owing to data privacy and open-access issues. And lastly, these datasets are fixed, static and cant be easily reproduced for different use case scenarios, nor scalable to adapt to needs. This research aims to tackle this problem by introducing a novel framework to generate custom problem-specific synthetic BEM dataset that generates a user defined amount of context specific 3D early phase building geometries and their associated BEM models, that is suitable for ML and HMI research. Synthetic datasets generated with this framework offer flexibility and customization in the generation process, making these datasets modular, reproducible and scalable. The framework uses the concepts of Generative Design, Geometry Manipulation, Simulation and Computational Automation to build the dataset. The dataset is qualified through the concepts of Preservationism and Sustainability, which are discussed in further sections of this article. The research identifies the importance and multifacetedness of the impact of building geometry on its performance, and demonstrates the application of generated synthetic BEM datasets by developing an enactive, conversational design environment that allows the designer to make real-time sustainable design decisions based on instantaneous machine feedback, for an intuitive, centralized design intuition.

In Europe, the energy renovation of the existing building stock is a great opportunity to significantly reduce energy consumption and greenhouse gas (GHG) emissions and reach the European sustainability targets. In this framework, building energy simulations (BES) tools are very useful in verifying energy retrofit measures' effectiveness and compliance with national standards. However, an inaccurate numerical prediction, the so-called “performance gap” between measured and numerical performance, is often obtained, mainly due to the inherent uncertainty of model input. Due to its stochastic nature, the occupants’ behavior (OB) is considered among the key contributors to this gap. However, the most recent Building Energy Model (BEM) approaches adopt deterministic hourly-defined profiles for characterizing OB, thus neglecting the related uncertainty. In this work, the impact of OB uncertainties on energy consumption (EC) prediction is evaluated by adopting a Karhunen-Loève Expansion sampling technique, used to randomly perturb OB profiles such as heating setpoint (HS), internal thermal loads (IL), and windows opening (NV). Two BEMs of a typical Italian residential building in pre- and post-renovation scenarios are considered and calibrated on real EC data. The results demonstrated that HS uncertainty has the highest impact on EC in all scenarios. Moreover, the higher the energy performance of the building, the higher the impact of OB, especially for IL and NV patterns.

It is challenging to accurately establish the dynamic heat transfer model of the internal thermal mass owing to the irregular shape. In building energy simulation software, the internal thermal mass is described as slabs. However, the parameter setting of the equivalent slabs depends primarily on the user experience, which may lead to uncontrollable errors. Therefore, this paper proposes a method, termed the effective area method (EAM), which specifies the transformation of an irregular internal thermal mass to the slab and can be used as a pre-processing method for building simulation software. This paper describes the methodology and pre-processing logic of the EAM, and the implementation of the approach in EnergyPlus considering an office environment. The root-mean-square errors between the simulation and measured temperatures are less than 0.3°C, which indicates that the EAM can effectively eliminate the errors caused by the unreasonable settings of the internal thermal mass.

Energy efficiency and Building Energy Modeling are two successful approaches to architecture, engineering, construction and operation (AECO) programs. In recent year several education initiatives focusing on buildings energy management have been carried out to provide professional profiles with specific skills in technology, architecture, engineering, economics, management and environmental science. It enables them to plan, design, evaluate or research energy supply and design strategies aimed to reduce energy consumption according to sustainability concepts. Building energy performance optimization requires an integrated design approach to explore and evaluate different strategies for building energy saving and to assist in the decision making process along the life cycle including design, operation, management and decommission phases. Moreover, BEM (Building Energy Modeling) is increasingly being included into architecture and engineering curricula, introducing new methodologies and tools for architecture design to provide interdisciplinary profiles in the professional practice. The main objective of this contribution is to report the application of BIM technologies and BEM tools into the Environmental Design Lab training course at the School of Architecture at University of Florence, where the authors are involved into a cross-disciplinary teaching program which students undertake in the fourth year of their curriculum within the 5 years degree program.

HBIM and BEM association: Systematic literature review

The increased application of PV in urban environments goes together with a growing awareness of the need to consider the influence of partial shading and reflected irradiance on PV performance. Most of the conventional PV simulation tools do not have the capabilities to account for the possible additional PV yield due to reflected sunlight. On the other end of the spectrum are complex ray-tracing tools that can accurately model reflected irradiance, but require a large amount of input data, are computationally intensive and have limited or no PV modeling algorithms. In this work, as a step towards quantifying uncertainties due to reflected irradiance in urban settings, the potential of a modeling strategy using building energy simulation software, with intermediate complexity is investigated using a case study of an outdoor PV test site.

Simulations of local heat islands in Zürich with coupled CFD and building energy models

Impact of urban wind environment on urban building energy: A review of mechanisms and modeling

Recently, interoperability between BIM (Building Information Model) and BEM (Building Energy Model) has been highlighted. Even though it has been widely recognized that there are significant potentials in BIM by data sharing and reuse, BIM has not been actively utilized for building energy simulation. With this in mind, in this paper, the authors investigated several issues: (1) limitation of Industry Foundation Class (IFC) and BIM authoring tools, (2) immature IFC data schema, (3) data mapping problem between BIM and BEM. Firstly, IFC files exported from major BIM authoring tools are not consistent with each other and don't include significant thermal parameters. In addition, building geometry, e. g. underground wall, is not properly exported. IFC data schema are not ready to describe detailed information on thermal and dynamic properties (efficiency, performance curves, etc.) of mechanical systems (HVAC, AHU), delivery systems (fans, pumps), and plants (boilers, chillers), which are very essential for BEM. Finally, most of the efforts on BIM-to-BEM are focused on data conversion (data-driven approach), without considering dynamic architectural design process which involves many different stakeholders. In near future, BIM-to-BEM interface should take into account design context and rational decision making so called 'process-driven approach'.