Urban Building Energy Modeling Research Articles

Comparison of dynamic urban building energy models (UBEM): Sigmoid energy signature and physical modelling approach

Thermal energy demand represents over 30% of overall energy consumption and thus is a vital target for the decarbonisation of our energy supply. This fact is the reason why dynamic thermal modelling of buildings, components and infrastructure has been gaining increasing importance in the context of the transformation of existing energy systems into smart energy systems. For the first time, this study presents a comparison of two major bottom-up approaches to modelling urban neighbourhoods: a physical modelling approach and a data-driven approach. The physical method is represented by a GIS-based automated modelling approach with detailed dynamic building simulation in IDA ICE. In the data-driven approach, an energy signature is applied, which uses a non-linear data-driven method. These two methods were validated on the basis of a multi-family house, an office building and a residential area with 34 buildings by means of detailed multi-zone building simulation and measurement data. The simulation results show that both applied approaches are applicable in these cases within good agreement compared to the measurement data (physical/data-driven: multi-family house (RN_RMSE(%) = 14.7/9.3; R2 = 0.68/0.87), office building (RN_RMSE(%) = 7.6/5.4; R2 = 0.92/0.96) and residential area (RN_RMSE(%) = 8.2/4.8; R2 = 0.92/0.97)). Finally, the fields of application of both approaches are discussed. A major finding here is that the energy signature shows slightly better results at deriving the load profile when the measured heating demand is present from a previous heating season. Furthermore, the number of buildings does not affect the duration of a simulation because cumulative user profiles are used. Whereas the simulation duration of the physical approach depends essentially on the size of the investigated area. The physical approaches have the advantage of being able to include densification or renovation scenarios, demand forecasting and coupled simulations of buildings and smart energy systems of neighbourhoods.

Hierarchical calibration of archetypes for urban building energy modeling

The application of building archetypes is a widespread approach used in urban building energy modeling. Working with archetypes has a range of benefits, but it is important that modelers avoid using oversimplified approaches when establishing the archetype as they lead to loss of uncertainty and, consequently, to models with inferior predictive capabilities. In this paper, we propose a multilevel take on the challenge of establishing archetypes. A simultaneous modeling and calibration framework is formulated using Bayesian inference techniques – a technique that allows for the propagation of uncertainty throughout the calibration process. By means of hierarchical modeling, information from training buildings is partially pooled together to form an optimal solution between separate building energy models and a completely pooled model. This enables the inference of uncertain archetype parameters that are less prone to building outliers than what is achieved using ordinary aggregation of individual building estimates. The proposed framework incorporates dynamic building energy modeling of arbitrary temporal resolution where uncertain parameters are fitted for individual building models and the archetype model simultaneously. The application of the framework is demonstrated using case-study data from the Danish residential building stock, containing 3-hourly measurements of energy use for 50 training buildings. The model is tested for the prediction of 100 out-of-sample test buildings’ aggregated energy use time series on a holdout validation period. With a prediction error of only NMBE = 2.9% and CVRMSE = 7.8%, the archetype framework promises well for urban modeling applications.

Profiling Occupancy Patterns to Calibrate Urban Building Energy Models (UBEMs) Using Measured Data Clustering

Uncertainty in predicting occupancy patterns leads to discrepancies in simulated building energy when compared to measured data. Typical simulation models represent occupants through identical schedules and repetitive behavior. However, users’ activity patterns comprise numerous variations, especially when focusing on interactions in buildings on the neighborhood scale. Urban-scale simulations inform design decisions, and one of the major challenges is identifying representable inputs for occupancy behavior. This paper presents a framework for modeling occupancy and consequent energy loads in residential buildings using measured data for calibration; it employs a functional clustering approach to profile energy use, which generates inputs for Urban Energy Models (UBEMs). The framework is demonstrated on a residential neighborhood and reveals that the generated inputs can more accurately predict community energy load patterns.

Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow

The world is rapidly urbanizing, and the energy intensive built environment is becoming increasingly responsible for the world’s energy consumption and associated environmental emissions. As a result, significant efforts have been put forth to develop methods that can accurately model and characterize building energy consumption in cities. These models aim to utilize physics-based building energy simulations, reduced-order calculations and statistical learning methods to assess the energy performance of buildings within a dense urban area. However, current urban building energy models are limited in their ability to account for the inter-building energy dynamics and urban microclimate factors that can have a substantial impact on building energy use. To overcome these limitations, this paper proposes a novel Data-driven Urban Energy Simulation (DUE-S) framework that integrates a network-based machine learning algorithm (ResNet) with engineering simulation to better understand how buildings consume energy on multiple temporal (hourly, daily, monthly) and spatial scales in a city (single building, block, urban). We validate the proposed DUE-S framework on a proof of concept case study of 22 densely located university buildings in California, USA. Our results indicate that the DUE-S framework is able to accurately predict urban scale energy consumption at hourly, daily and monthly intervals. Moreover, our results also demonstrate that the integration of data-driven and engineering simulation approaches can partially capture the inter-building energy dynamics and impacts of the urban context and merits future work to explore how they can be improved to predict sub-urban scale energy predictions (single building, block). In the end, successfully predicting and modeling the energy performance of urban buildings has the potential to inform the decision-making of a wide variety of urban sustainability stakeholders including architects, engineers and policymakers.

Predicting Danish residential heating energy use from publicly available building characteristics

Urban building energy modeling (UBEM) is a valuable tool for analyzing the building stock. Many different model approaches have been proposed in recent years suggesting various ways of dealing with the challenges of UBEM; however, central for all modeling approaches is the need for informative input data about the building stock. The availability of data for urban-scale modeling is both country-specific, time-consuming to aggregate, and data access is often limited due to privacy constraints. In this paper, we present a hierarchical bottom-up model of the Danish residential building stock using public building data for predicting the annual heating energy consumption. For more than 10,000 randomly selected single-family dwellings, the annual energy consumption is modeled and validated for the city of Aarhus, Denmark. We found that approx. 50% of the energy use is explained using only four widely available building characteristics, which enables building-scale predictions with a mean absolute error of approx. 25%. In addition, for city-scale predictions, the regression-based model enables aggregated predictions with a mean bias error of less than ±2%. Even though building-scale predictions are only somewhat accurate, the performance remains comparable to state-of-the art high-fidelity models in the literature.

A methodology for auto-calibrating urban building energy models using surrogate modeling techniques

Owners of large building portfolios such as university campuses have long relied on building energy models to predict potential energy savings from various efficiency upgrades. Traditional calibration procedures for individual building model are time intensive and require specially trained personnel, making their applications to campuses with hundreds of buildings prohibitive. Recently proposed automatic calibration techniques reduce the manual effort during calibration but require hundreds of thousands of energy simulations which increase their cost. To reduce the computational effort of these methods, this paper proposes a methodology that uses a data-driven approximation technique. Instead of brute-force simulations using detailed engineering models, this study employs statistical surrogate models with an optimization algorithm to estimate properties of unknown building parameters. Results demonstrate that when envelope information is available, this workflow yields sufficiently accurate estimates of hard to observe building characteristics, about 500 times faster than traditional approaches.

Implementation of urban building energy modeling in historic districts. Seville as case- study

Implementation of urban building energy modeling in historic districts. Seville as case- study

Urban energy performance monitoring for Smart City decision support environments

In this essay we wish to present an interdisciplinary study carried out in the framework of two Interreg Italy-Austria projects which were intended to design methodologies, simulation tools and decision support instruments to face energy challenges within the overall Smart City concept. The proposed strategy uses a physical model (“City Model”) and an energy behavior model (“City Sensing”) of the urban environment. They were developed using two different approaches: the first is mainly based on analysis of surveys data and the second one on simulations and processing of large-scale datasets. “City Model” and “City Sensing” were then merged to provide an Urban Building Energy Model - that we called “City Energy Model” - integrated with a Participatory Public GIS platform.

Balancing demand and supply: Linking neighborhood-level building load calculations with detailed district energy network analysis models

Operational building energy has long been recognized as both a major contributor of as well as an opportunity to save carbon emissions. To do so, one may follow two paths, reduce the energy use in buildings (demand) or provide the required energy in more efficient ways (supply). In the literature, extensive research has been made on both fronts and at different levels of detail resulting in a myriad of tools unique to specific stages in the development timeframe of a district energy project. This manuscript describes a modeling workflow based on a new Rhinoceros-based plugin that combines an Urban Building Energy Model with a network topology optimization and a heat generation scenario model bridging the gap between the planning phase and the design phase. The new plugin builds on the foundation of umi, a Rhino-based link to Radiance and EnergyPlus developed at the MIT Sustainable Design Lab, as an addition to the demand simulation module. A demonstration project shows how the workflow serves as a stepping stone from the design environment of Rhinoceros to the technical environment of TRNSYS, thus enabling a more streamlined process to use different modeling strategies for different purposes, within the same design workflow.

Impacts of building geometry modeling methods on the simulation results of urban building energy models

Urban-scale building energy modeling (UBEM)—using building modeling to understand how a group of buildings will perform together—is attracting increasing attention in the energy modeling field. Unlike modeling a single building, which will use detailed information, UBEM generally uses existing building stock data consisting of high-level building information. This study evaluated the impacts of three zoning methods and the use of floor multipliers on the simulated energy use of 940 office and retail buildings in three climate zones using City Building Energy Saver. The first zoning method, OneZone, creates one thermal zone per floor using the target building’s footprint. The second zoning method, AutoZone, splits the building’s footprint into perimeter and core zones. A novel, pixel-based automatic zoning algorithm is developed for the AutoZone method. The third zoning method, Prototype, uses the U.S. Department of Energy’s reference building prototype shapes. Results show that simulated source energy use of buildings with the floor multiplier are marginally higher by up to 2.6% than those modeling each floor explicitly, which take two to three times longer to run. Compared with the AutoZone method, the OneZone method results in decreased thermal loads and less equipment capacities: 15.2% smaller fan capacity, 11.1% smaller cooling capacity, 11.0% smaller heating capacity, 16.9% less heating loads, and 7.5% less cooling loads. Source energy use differences range from -7.6% to 5.1%. When comparing the Prototype method with the AutoZone method, source energy use differences range from -12.1% to 19.0%, and larger ranges of differences are found for the thermal loads and equipment capacities. This study demonstrated that zoning methods have a significant impact on the simulated energy use of UBEM. One recommendation resulting from this study is to use the AutoZone method with floor multiplier to obtain accurate results while balancing the simulation run time for UBEM.

Data-driven Urban Energy Simulation (DUE-S): Integrating machine learning into an urban building energy simulation workflow

Urban building energy models are emerging tools meant to analyze and understand the energy performance of multiple buildings within a dense urban area. However, accurate performance prediction of these models remains a challenge because of their inability to account for the inter-building energy dynamics and interdependencies in an urban area. This paper analyzes the literature to highlight the limitations of current urban scale energy simulation models and proposes a new Data-driven Urban Energy Simulation (DUE-S) workflow capable of capturing inter-building effects on a building’s energy usage. Specifically, DUE-S combines a data-driven machine learning model with a traditional physics-based energy simulation to enable more accurate simulation results on multiple scales (single building, community, urban). More accurate and robust energy performance characterization and simulations of urban buildings could provide valuable insight on early-stage building design, building conservation and portfolio management, and urban energy efficiency policy-making crucial to helping our cities transition to a more sustainable energy future.

Demonstration of reduced-order urban scale building energy models

The aim of this study is to demonstrate a developed framework to rapidly create urban scale reduced-order building energy models using a systematic summary of the simplifications required for the representation of building exterior and thermal zones. These urban scale reduced-order models rely on the contribution of influential variables to the internal, external, and system thermal loads. OpenStudio Application Programming Interface (API) serves as a tool to automate the process of model creation and demonstrate the developed framework. The results of this study show that the accuracy of the developed reduced-order building energy models varies only up to 10% with the selection of different thermal zones. In addition, to assess complexity of the developed reduced-order building energy models, this study develops a novel framework to quantify complexity of the building energy models. Consequently, this study empowers the building energy modelers to quantify their building energy model systematically in order to report the model complexity alongside the building energy model accuracy. An exhaustive analysis on four university campuses suggests that the urban neighborhood buildings lend themselves to simplified typical shapes. Specifically, building energy modelers can utilize the developed typical shapes to represent more than 80% of the U.S. buildings documented in the CBECS database. One main benefits of this developed framework is the opportunity for different models including airflow and solar radiation models to share the same exterior representation, allowing a unifying exchange data. Overall, the results of this study have implications for a large-scale modeling of buildings in support of urban energy consumption analyses or assessment of a large number of alternative solutions in support of retrofit decision-making in the building industry.

Seasonal effects of input parameters in urban-scale building energy simulation

Urban Building Energy Models are powerful tools for estimating future states of energy consumption and energy generation in buildings. Due to the complexity of these systems, large amounts of data are required, which are often incomplete or unavailable. Through the implementation of building archetypes, models such as the City Energy Analyst minimize the amount of input data. However, these simplifications inherently increase the uncertainty of the expected results.This paper presents a sensitivity analysis of architectural properties (window-to-wall ratio, occupant density and envelope leakiness), thermal properties (U-values, G-values, thermal mass and emissivity of building surfaces), operating parameters (set point temperatures and ventilation rates) and internal loads (heat gains due to occupancy, appliance use and lighting). For this, the study combines a two-step process of sensitivity analysis with Saltelli's extension of the Sobol method and the City Energy Analyst. The methodology is applied to a case study area in central Zurich, Switzerland, comprising 284 buildings with predominantly educational, hospital and residential uses.The results showed that the cooling demand in the area was very strongly influenced by the set point temperature, with other variables having a relatively minor influence. For the heating case a larger number of variables were needed in order to explain variations in demand, primarily the thermal properties of the envelope and air exchange rates of the buildings. This was generally true for all occupancy types, shapes, sizes and locations, showing the importance of accurate estimates of these parameters in urban building energy modeling. On a broader sense, the results contribute to the development of urban energy simulations that are both practical and accurate.

Effects of air infiltration modeling approaches in urban building energy demand forecasts

The air infiltration rate is a highly sensitive variable that influences heating and cooling demand forecasts in urban building energy modeling. This paper analyses the effect of two different simplified modeling techniques of air infiltration - fixed air change rate vs. a model based on wind pressure and air temperatures - on the heating and cooling demand in a district. The urban energy simulation toolbox City Energy Analyst (CEA) is used to simulate a case study in Switzerland, comprising of 24 buildings of various functions. Results indicate that despite the large differences for individual buildings, a fixed infiltration rate model could be sufficient for early design studies of district energy systems, as the impact on the sizing of district energy systems remains relatively low. This comparison will contribute to the continued development of urban energy simulations that are robust, as well as computationally fast.

Comparison of four building archetype characterization methods in urban building energy modeling (UBEM): A residential case study in Kuwait City

Significant research effort has gone into developing bottom-up, physics based, urban building energy modeling (UBEM) tools, which allow evaluating district-wide energy demand and supply strategies. In order to reduce the simulation inputs required in UBEM, groups of buildings are typically classified into representative “archetypes”. This approach necessarily simplifies the real diversity of occupant behaviors and construction variations, reducing in turn the variety of predicted building energy demands. To further understand the limitations of archetype-based modeling, this paper evaluates four characterization methods of increasing complexity, by comparing simulation results to measured Energy Use Intensity (EUI) distributions of 336 residential buildings in a district in Kuwait City. The first two methods follow a typical deterministic approach, organizing buildings into one and four archetypes respectively, while the third method introduces probabilistic uncertainty modeling for occupant related parameters. The fourth method introduces a new “Bayesian” calibration technique for archetype parameters, and applies it to occupant parameters in method three. In a first step, frequency calibrated distributions for occupancy, lighting power density, plug load density and cooling set points are generated through parametric modeling from a test set consisting of about half of the 336 buildings. The resulting joint distribution of parameters is then sampled using Latin Hyper Cube (LHC) for the probabilistic simulation of the remaining urban model. The comparison of simulated EUI distributions with measured data suggests that the proposed probabilistic Bayesian calibration leads to significantly closer predictions, with a maximum error in the 10th and 90th percentiles of 15% against 55%, 60% and 29% for the other three methods.

Automatic generation and simulation of urban building energy models based on city datasets for city-scale building retrofit analysis

Buildings in cities consume 30–70% of total primary energy, and improving building energy efficiency is one of the key strategies towards sustainable urbanization. Urban building energy models (UBEM) can support city managers to evaluate and prioritize energy conservation measures (ECMs) for investment and the design of incentive and rebate programs. This paper presents the retrofit analysis feature of City Building Energy Saver (CityBES) to automatically generate and simulate UBEM using EnergyPlus based on cities’ building datasets and user-selected ECMs. CityBES is a new open web-based tool to support city-scale building energy efficiency strategic plans and programs. The technical details of using CityBES for UBEM generation and simulation are introduced, including the workflow, key assumptions, and major databases. Also presented is a case study that analyzes the potential retrofit energy use and energy cost savings of five individual ECMs and two measure packages for 940 office and retail buildings in six city districts in northeast San Francisco, United States. The results show that: (1) all five measures together can save 23–38% of site energy per building; (2) replacing lighting with light-emitting diode lamps and adding air economizers to existing heating, ventilation and air-conditioning (HVAC) systems are most cost-effective with an average payback of 2.0 and 4.3years, respectively; and (3) it is not economical to upgrade HVAC systems or replace windows in San Francisco due to the city’s mild climate and minimal cooling and heating loads. The CityBES retrofit analysis feature does not require users to have deep knowledge of building systems or technologies for the generation and simulation of building energy models, which helps overcome major technical barriers for city managers and their consultants to adopt UBEM.

Development of prototypical buildings for urban scale building energy modeling: A reduced order energy model approach

Prototypical building energy models represent the “typical” energy consumption behavior of a group of buildings with similar functions, thus can serve the purpose of energy performance evaluation, urban energy planning, etc., for a buiding stock rather than a single building. Normative energy model is a special type of model in that it is both physically meaningful and computationally fast. In the current article, a general approach to establish prototypical building energy models based on normative method is proposed. This approach firstly selects “representative” buildings (those with distinct features) using clustering method, then develops normative model for each of the “representative” buildings, and estimate their energy related performance parameters. The estimated parameters are then statistically analyzed, and two different aggregation approaches are taken to calculate the parameters of the prototypical building energy model. To validate this approach, 34 office buildings in Qingdao City, China were chosen as testing cases. The results show that the prototypical building energy model built based on this approach is able to mimic the “typical” energy consumption behavior with reasonable goodness-of-fit metrics. Therefore, this approach is capable of evaluating the energy retrofit options of a group of buildings whose information except for energy use is unknown to the modeler, with good speed and acceptable accuracy.

Material Based Urban Modeling: An Approach to Integrate Smart Materials in a Near-Zero Community Design

This article outlines a research agenda to incorporate smart materials into district-scale urban building energy modeling frameworks. This research agenda aims to design eco-communities driven by the near-zero emission objective. Two strategic materials, polyurethane and polycarbonate, were selected for testing. Tests were conducted to evaluate to what extent these smart materials, when embedded in multi-scale systems from construction components, to buildings, to a community, would enhance total urban energy performance. A conceptual framework of holarchies is proposed to articulate subsystems of an urban system. This framework explores the multiple-scale and cross-sectorial relationship of material properties and their building spatial configurations. An experimental test case uses Building Information Modeling (BIM) to construct databases from the various levels of the urban system from M (material), to P (products and building component) and B (building systems). It is then connected to N (neighborhood) by incorporating Urban Energy Building Modeling (UBEM). This connection considers contextual factors such microclimate and landscape features. After conducting the M-P-B-N multi-scale modeling experiment, emerging system properties will be derived and turned into design principles for informing future design decision-making.

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

In this paper the authors present an algorithm that abstracts an arbitrarily shaped set of building volumes into a group of simplified ‘shoebox’ building energy models. It is shown that for generic perimeter and core floorplans the algorithm provides a faster but comparably accurate simulation results of annual load profiles vis-à-vis multi-zone thermal models generated according to ASHRAE90.1 Appendix G guidelines. Envisioned applications range from rapid thermal model generation for urban building energy modelling to schematic architectural design. Following a description of the algorithm, its ability to produce load profiles for a mixed-use neighborhood of 121 fully conditioned buildings for a variety of climates is demonstrated. The comparison yields relative mean square errors in simulated annual building energy use intensity of five to 10 percent compared to ASHRAE 90.1 compliant building energy models while reducing simulation times by a factor of 296.

Validation of a Bayesian-based method for defining residential archetypes in urban building energy models

Urban Building Energy Modeling (UBEM) is an emerging method for exploring energy efficiency solutions at urban or district scales. More versatile than statistical models, physical bottom-up UBEMs allow planners to quantitatively assess retrofit strategies and energy supply options, leading to more effective policies and management of energy demand. The most common approach for formulating an UBEM involves segmenting a building stock into archetypes, characterizing each type, and validating the model by comparing its output to aggregated measured energy consumption. This paper presents a more detailed methodology for setting up UBEMs while faced with incomplete information about the buildings. The procedure calls for defining unknown or uncertain parameters in archetype descriptions as probability distributions and, if available, using measured energy data to update these distributions by Bayesian calibration. The methodology is validated on residential houses in Cambridge, Massachusetts. Distributions for uncertain parameters are initially generated using a training set of 399 homes with monthly electricity and gas consumption records and then applied to a larger test set of 2263 homes. The procedure is applied both for monthly and annual metered energy usage data. Results show that both annual and monthly Bayesian calibration lead to significantly better annual energy use intensity (EUI) fits compared to traditional deterministic archetype definitions. As expected, an UBEM calibrated with monthly metered data more truthfully mimics monthly EUI distributions than one based on annual data, revealing the benefit of calibrating UBEMs using the smallest measurement time step available.