Improved energy performance evaluating and ranking approach for office buildings using Simple-normalization, Entropy-based TOPSIS and K-means method

Sustainable High-rises

It is widely accepted that the concentration of people living in high-density city centers offers greater operational energy efficiency and lower greenhouse gas emissions than lower-density expanded suburbs. The prevailing assumption is that lower-density suburbs are dominated by larger low-rise buildings that have higher building energy use requirements and greater per-person automobile travel requirements than high-density city centers dominated by medium- and high-rise buildings located in close proximity to a variety of public transit systems. However, very few studies to date have utilized empirical data at an individual household scale to evaluate differences in the operational energy (OE) footprints for both building and transportation energy end-uses between high-rise urban and low-rise suburban households. Therefore, this work collects empirical data on building and transportation OE consumption by individuals and households living in two economically similar groups: existing high-rise residential buildings in downtown Chicago, IL, USA and existing low-rise residential buildings in suburban Oak Park, IL, USA. Data were collected from over 500 households via an online survey. We considered the following components of residential living: (1) building OE (BOE), which includes electricity and/or natural gas use for all building energy end-uses; and (2) transportation OE (TOE), which includes the OE for multiple modes of transportation (i.e., automobile, bus, subway, regional train, etc.) based on average travel behavior in each location, as well as the OE for supporting transportation infrastructure. We estimate that downtown high-rise living in this sample of residences in Chicago, IL accounts for approximately 427 GJ of primary OE per household per year, on average, which was 14% lower than the average for suburban low-rise living in the Oak Park, IL homes (499 GJ per household per year). However, on a per-person basis, downtown high-rise living accounts for approximately 246 GJ of primary OE per person per year, which was approximately 61% higher than suburban low-rise living (153 GJ per person per year). In both building types, building OE was the single largest contributor to total OE use. This study accurately captured the energy requirements associated with realistic behaviors and lifestyles of occupants of both low-rise suburban and high-rise urban households, and found that building OE dominates the total OE, which suggests that efforts to reduce building OE should be given high priority in building design and management as well as urban planning.

A validated low carbon office building intervention model based on structural equation modelling

Studies have shown that space layout design can impact the building energy performance (BEP). However, its isolated effect on the BEP has not been identified yet. Performative computational architecture has proven to be effective to improve the BEP. However, only a few studies have tried to apply the performative computational architecture to space layout design. This research aims to investigate how space layout affects BEP, and to develop a computational optimisation method for space layout to improve the BEP of office buildings. Firstly, the mechanism on how space layout affects the BEP and how much energy is affected by space layout were identified through literature review and simulation. 11 layouts with different function allocations were simulated and compared. The outcome showed that layout variance affected lighting the most, and the maximum difference happened in Harbin, being 46% without shading and 35% with shading. As a follow-up, another literature review was conducted, which identified the functional requirements of space layout design, methods for automatic generation of space layout, and requirements for energy performance optimisation. In addition, a computational method was developed to optimise space layout design for energy performance improvement, regardless of functional requirements. As a result, the relationship between space layout and energy demands were recognised. In conclusion, space layout has proven to be a significant influence on the BEP, and conscientious design can improve it. For optimal energy performance, manual design of space layout is not feasible; in order to do that, a computational approach is needed.

A study on energy performance of hotel buildings in Singapore

As one of the most important design tasks of building design, space layout design affects the building energy performance (BEP). In order to investigate the effect, a literature review of relevant papers was performed. Ten relevant articles were found and reviewed in detail. First, a methodology for studying the effects of space layouts on BEP were proposed regarding design variables, energy indicators and BEP calculation methods, and the methodologies used in the 10 articles were reviewed. Then, the effects of space layouts on energy use and occupant comfort were analysed separately. The results show that the energy use for heating, cooling, lighting and ventilation is highly affected by space layouts, as well as thermal and visual comfort. The effects of space layouts on energy use are higher than on occupant comfort. By changing space layouts, the resulting reductions in the annual final energy for heating and cooling demands were up to 14% and 57%, respectively, in an office building in Sweden. The resulting reductions in the lighting demand of peak summer and winter were up to 67% and 43%, respectively, for the case of an office building in the UK, and the resulting reduction in the air volume supplied by natural ventilation was 65%. The influence of other design parameters, i.e., occupancy and window to wall ratio, on the effects of space layouts on BEP was also identified.

Propagating downscaled future weather file uncertainties into building energy use

Buildings play a major role in total annual energy use worldwide. The purpose of this study is to evaluate the energy performance of University of Florida (UF) buildings and assess the effects of selected Energy Efficiency Measures (EEMs) on their energy performance. For this study, a set of buildings were identified based on a space functionality classification and two of them were chosen for simulation with energy modeling software. After calibrating the models to match actual energy use, we assessed their performance. The effect of EEMs on reducing the energy demands of buildings were analyzed. Analysis showed the potential energy saving for UF buildings. Modifying the EEMs, we could reduce the Energy Use Intensity values of the simulated buildings for 7--13%. Finally, using extrapolation and previous utility bills data, the campus-wide financial benefits of this saving were discussed.

Inter-related effects of cooling strategies and building features on energy performance of office buildings

The effect of neighbourhood-level urban form on residential building energy use: A GIS-based model using building energy benchmarking data in Seattle

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.

Building energy use in China: Ceiling and scenario

Assessing Lighting Energy Saving Potential from Daylight Harvesting in Office Buildings Based on Code Compliance & Simulation Techniques: A Comparison

Climate models for the assessment of office buildings energy performance

Evaluating and ranking the energy performance of office building using technique for order preference by similarity to ideal solution