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Energy Conservation Measures (A Sample List) Used in CityBES

Energy Conservation Measures (A Sample List) Used in CityBES

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Conference Paper
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Buildings in cities consume 30 to 70% of the cities’ total primary energy. Retrofitting the existing building stock to improve energy efficiency and reduce energy use is a key strategy for cities to reduce green-house-gas emissions and mitigate climate change. Planning and evaluating retrofit strategies for buildings requires a deep understanding o...

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... the current energy usage data as the baseline, CityBES can offer a wide array of analysis suited for city's energy efficiency program, including energy benchmarking, energy savings, greenhouse gas reductions, operation improvements, and energy costs reductions. Table 1 shows a sample list of ECMs that can be applied during energy retrofit analysis. ...

Citations

... Various physics-based UBEM tools have been developed, as illustrated in Figure 1. Among these UBEM tools, there are primarily two types of building energy models (BEM): EnergyPlus-based BEM (such as CityBES (Hong et al. 2016), umi (Reinhart et al. 2013), and AutoBPS (Deng et al. 2023)) and resistance-capacitance (RC)-based BEM (such as CitySim (Robinson et al. 2009), CEA (Fonseca et al. 2016), and UECC (Wang et al. 2024)). ...
... However, integrating CFD models as meteorological boundary conditions into the BEM for annual hourly energy predictions is challenging owing to their high computational costs (Katal et al. 2019). This could explain why almost all UBEM tools ignore microclimate effects and instead use TMY as a meteorological boundary condition (Robinson et al. 2009;Reinhart et al. 2013;Fonseca et al. 2016;Hong et al. 2016;Deng et al. 2023;Wang et al. 2024) In summary, the existing UBEMs focus solely on heat flows within urban buildings, neglecting moisture effects at both building and urban microclimate levels. Therefore, the main purpose of this study is to integrate moisture effects into UBEMs. ...
Article
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To address the limitations of current urban building energy modeling (UBEM), which often neglects moisture effects, we developed a comprehensive roadmap for modeling urban heat and moisture flows. This effort included developing an urban-scale whole-building heat and moisture transfer (HAMT) model that considers wind-driven rain, integrated with a microclimate model known as Urban Weather Generator (UWG). The proposed model was validated through analytical and comparative cases of whole-building hygrothermal performance analyses from the Annex 41 Project. The integrated whole-building and microclimate HAMT models were applied to a real urban building to assess the impact of moisture on annual energy predictions in a hot-humid region of Shanghai. The results show that incorporating moisture effects into the UBEM increases the annual cooling energy demand by 22.11% (5.92% owing to latent heat loads) and the annual heating loads by 6.06%, resulting in a 19.73% increase in the total annual energy loads. Additionally, the outer wall surface temperature decreases during and after rainfall events, with maximum decreases of 3.23 °C in winter and 8.80 °C in summer. Therefore, integrating moisture effects into UBEM is crucial, particularly in humid regions.
... The physical models are based on heat balance equations considering multi-physical processes. Due to its clear physical meanings, these methods have been widely used in various urban building energy simulation tools such as URBANopt, CityBES and umi [4,5]. The data-driven models are based on machine learning algorithms by training historical urban building operation data. ...
... Several tools, including CityBES, 58 UMI, 59 COFFEE, 60 the Massachusetts Institute of Technology (MIT) tool, 61 and the Columbia University tool, 62 have emerged to facilitate meticulous energy performance analysis. These tools leverage the robust EnergyPlus engine for dynamic energy simulations in urban settings and often employ the OpenStudio Software Development Kit (SDK) 63 to generate energy models for EnergyPlus simulations. ...
Chapter
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Built environment is experiencing significant changes due to population growth, improved living standards, and climate change impacts. With the building sector responsible for around 40% of global energy consumption and contributing to 30% of the world's greenhouse gas emissions, it becomes crucial to scrutinize the influence of climate change on energy usage in built spaces. This chapter seeks to explore accepted methodologies and forecasts regarding shifts in building energy demand. The efforts to refine building energy estimations and consider broader contextual factors is crucial for the sustainable and adaptive development of the built environment.
... The core objective of this case study is to compare the selected region's simulated urban building energy profile using the shapefile generated by the proposed sat2shp pipeline against the ground truth shapefile. Among numerous UBEM tools available, such as the City Energy Analyst (CEA) (Fonseca, Nguyen, Schlueter & Marechal, 2016) from ETH Zurich, the Urban Modeling Interface (UMI) (Reinhart et al., 2013) and the complementary UBEM.io developed by MIT, and CityBES (Hong, Chen, Lee & Piette, 2016) developed by Lawrence Berkeley National Laboratory, UMI was chosen for this study. UMI stands out due to its user-friendly input interface, compatibility with shapefiles, and its utilization of advanced algorithms like the Shoeboxer (Dogan & Reinhart, 2017) for efficient and accurate simulation and analysis. ...
... Other factors such as the experience of the user or the availability of the program, especially if it is an open-access software, influence the choice of software. Some of the tools that can be used for urban simulations are the following: CityBES [67], City Energy Analyst (CEA) [68], CitySim [69], UMI [70], and URBANopt [71]. The main characteristics of the existing software tools are indicated in Table 2. CityBES is a software developed in 2016 by Hong et al. [67]. ...
... Some of the tools that can be used for urban simulations are the following: CityBES [67], City Energy Analyst (CEA) [68], CitySim [69], UMI [70], and URBANopt [71]. The main characteristics of the existing software tools are indicated in Table 2. CityBES is a software developed in 2016 by Hong et al. [67]. It is a web-based software that allows for energy retrofit analyses, urban energy planning, building operations improvement, and energy benchmarking from a small group of buildings to all the buildings in a city. ...
... Architecture of CityBES. Adapted with permission from[67]. Copyright 2024, copyright Tianzhen Hong. ...
Article
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Building integrated photovoltaics (BIPVs) consist of PV panels that are integrated into a building as part of its construction. This technology has advantages such as the production of electricity without necessitating additional land area. This paper provides a literature review on recent developments in urban building energy modelling, including tools and methods as well as how they can be used to predict the effect of PV systems on building outdoor and indoor environments. It is also intended to provide a critical analysis on how PV systems affect the urban environment, both from an energy and a comfort point of view. The microclimate, namely the urban heat island concept, is introduced and related to the existence of PV systems. It is concluded that urban building energy models (UBEMs) can be effective in studying the performance of PV systems in the urban environment. It allows one to simultaneously predict building energy performance and microclimate effects. However, there is a need to develop new methodologies to overcome the challenges associated with UBEMs, especially those concerning non-geometric data, which lead to a major source of errors, and to find an effective method to predict the effect of PV systems in the urban environment.
... Several UBEM tools have become widely adopted for urban applications [5]. For example, CityBes, developed by Hong et al. [8], is a platform intended to simulate the energy performance of building stocks across cities, supporting the evaluation of retrofit strategies and energy-saving interventions. In a recent study, Li et al. [9] expanded CityBes capabilities to include district heating and cooling systems, enabling city planners to model district-level energy configurations and assess their impact on energy demand and emissions. ...
... Internal heat gains can significantly affect a building's energy balance and indoor thermal environment, and they are calculated through Equation (8). ...
Article
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This study introduces a computationally efficient urban building energy model (UBEM) to assess decarbonization strategies for the residential sector at the regional level. The model considers a range of inputs, including building characteristics, climate data, technology penetration, and occupant behavior. The model provides an economic analysis associating emission reduction potential with economic returns through an abatement cost curve, which is critical to designing cost-effective solutions. The model was validated at its full scale in Portugal, using actual consumption data from all municipalities. Key findings showed that lighting upgrades (100% LEDs) are the most cost-effective measure, offering the lowest abatement cost (−521 EUR/tonCO2eq) and a low discounted payback period of 2 years, while heat pumps for water heating provide the highest emission reduction potential, with an annual reduction of 863 tonnes of CO2eq annually, equivalent to a 20% reduction in national emissions. Additionally, behavioral measures achieved an annual reduction of 147 tonnes of CO2eq. The analysis further reveals that, while some measures might have a negative abatement cost at the national level, their economic viability varies locally, with certain municipalities incurring positive abatement costs, highlighting how local context affects the economic viability of decarbonization strategies.
... This method needs climate data, building geometric information, and non-geometric information (schedule, thermo-physical properties of the envelope, etc.) as input, and it calculates the energy consumption with energy equations. The most common computational tools are EnergyPlus and a large number of derivatives using EnergyPlus as the underlying computational engine, including CityBES [11], COFFEE [12], and UMI (1.0) [13], in addition to IDA-ICE and DOE2. This method is highly comprehensible, but the model's level of development has a significant impact on the results of the calculations, which are greatly reduced when complete modelling information is difficult to obtain [14]. ...
Article
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Urban building energy modelling (UBEM) has consistently been a pivotal tool to evaluate and control a building stock’s energy consumption. There are two main approaches to build up UBEM: top-down and bottom-up. The latter is the most commonly used in engineering. The bottom-up approach includes three methods: the physical-based method, the data-driven method, and the grey-box method. The first two methods have previously received ample attention and research. The grey-box method is a modelling method that has emerged in recent years that combines the traditional physical method with the data-driven method while it aims to avoid their problems and merge their advantages. Nowadays, there are several approaches for modelling the grey-box model. However, the majority of existing reviews on grey-box methods concentrate on a specific technical approach and thus lack a comprehensive overview of modelling method perspectives. Accordingly, by conducting a comprehensive review of the literature on grey-box research in recent years, this paper classifies grey-box models into three categories from the perspective of modelling methods and provides a detailed summary of each, concluding with a synthesis of potential research opportunities in this area. The aim of this paper is to provide a foundational understanding of grey-box modelling methods for similar research, thereby removing potential barriers in the field of research methods.
... Among the three approaches, the physically based approach estimates the energy consumption of each building using a simulation engine (such as EnergyPlus) with actual building sample data and considers the thermal influence of the environment, which shows great flexibility and predictive power (Ali et al., 2021). Various computational tools have been developed in UBEM from the physically based approach, including SEMANCO (Madrazo et al., 2012), CitySim (Vermeulen et al., 2013), UMI (Reinhart et al., 2013), CityBES (Hong et al., 2016), UrbanOPT (Polly et al., 2016), and CityBEUM (Li et al., 2018). These tools run on the Web, as standalone desktop applications, or as plugins in other software. ...
Article
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Rapid urbanization, climate change, and aging infrastructure pose significant challenges to achieving sustainability and resilience goals in urban building energy use. Although retrofitting offers a viable solution to mitigate building energy use, there has been limited analysis of its effects under various weather conditions associated with climate change in urban building energy use simulations. Moreover, certain parameters in energy simulations necessitate extensive auditing or survey work, which is often impractical. This research proposes a framework that integrates various datasets, including building footprints, Lidar data, property appraisals, and street view images, to conduct neighborhood-scale building energy use analysis using the Urban Modeling Interface (UMI), an Urban Building Energy Model (UBEM), in a coastal neighborhood in Galveston, Texas. Seven retrofit plans and three weather conditions are considered in the scenarios of building energy use. The results show that decreasing the U-value of building envelopes helps reduce energy use, while increasing the U-value leads to higher energy consumption in the Galveston neighborhood. This finding provides direction for coastal Texas cities, like Galveston, to update building standards and implement retrofit measures.
... For instance, Reinhart et al. [91] introduced the Lightswitch-2002 model, applying heat transfer and mass flow theory to assess the energy consumption and environmental impacts of individual buildings. [93]. Dogan et al. [12] introduced "Shoeboxer," a tool that reconstructs a group of buildings by combining and selecting simplified perimeters and core shoeboxes. ...
... This process is repeated by looping through all the previous subsections for each individual building in the entire city model. It's important to note that while the results may not be as accurate due to the simplification of complex interactions between buildings, the decomposed approach holds the potential to conduct energy analysis quickly with fewer inputs [93]. ...
Thesis
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Urbanization poses a significant challenge in the 21st century. Currently, more than half of the global population resides in urban areas, and this percentage is projected to reach 68% by 2050. The increase in urban population has led to a substantial rise in residential energy consumption, alongside a surge in commercial energy use to meet the growing demand for services. Consequently, overall building energy consumption has witnessed a significant increase. Therefore, effectively managing energy use in urban buildings has become imperative. To achieve this goal, various methodologies and tools for urban building energy modeling have been developed. These models offer valuable insights into the energy demands of building stock, covering benchmarking analysis, scenario assessments, peak load evaluations, energy pattern analysis, and other specialized analyses. Despite extensive research in the field of energy modeling, assessing urban energy remains complex due to three significant challenges. Firstly, urban building simulation involves various aspects such as geography, construction, materials, and HVAC (Heating, Ventilation, and Air Conditioning) systems, each of which is stored in its own unique data model. As a result, creating text-based simulation files for urban buildings from scratch is an intricate task which requires the integration and processing of cross-domain data models. Secondly, conventional simulation models rely on climate conditions provided by a limited number of weather stations, which do not accurately capture the microclimate variations caused by urban morphologies, natural conditions, and man-made structures. This limitation results in unrealistic and unreliable simulation outputs, further hindering effective decision-making for urban sustainability. Lastly, previous efforts have primarily focused on complex physical conditions within cities but have often encountered challenges such as intricate modeling and substantial computational loads. To address these gaps, this dissertation proposes a system architecture for urban building energy distributed simulation. The first aspect involves designing ontologies using semantic network technology, grounded in the features of building energy simulation inputs, clearly defining the potential logical relationships between the inputs, and facilitating the generation of qualified simulation files. Additionally, the concept of UrbanPatch, which represents the microclimate perception domain of urban buildings, is introduced. By analyzing the building morphology and green spaces within each UrbanPatch, a microclimate tuning approach is proposed to localize weather conditions for buildings. Finally, a rapid simulation approach is created, which decomposes the city model into spatially correlated building blocks for distributed simulation. The proposed algorithm, known as distributed adjacency blocks (DABs), uses 2D footprints to construct 3D building groups and considers solar azimuth angles, altitude angles, and shading planes to simplify the simulation targets. Using multiple threads and abstracted inter-building boundary conditions, the energy dynamics of an entire city can be simulated in parallel. The innovative system architecture for urban building energy distributed simulation proposed in this dissertation offers a novel solution that prompts researchers to reconsider the traditional bottom-up approach towards city-scale energy simulation. Centered around distributed building networks, this dissertation not only distributes the computational load across multiple computing components, enabling dynamic energy simulations for extensive metropolitan areas, but also accounts for the influence of microclimate on building energy consumption in the urban built environment, resulting in more precise and reliable simulation outcomes and enhancing the efficiency of city energy decision-making and management.
... A third method involves developing BEMs for each individual building in an area of interest. Examples of this method include the CityBES [46][47][48][49] and URBANopt™ [50][51][52][53][54][55] tools. ...
Article
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This study examines the impact of low-income assistance and electrification programs on a disadvantaged community in Southern California. An urban building energy model is paired with an AC power flow and electric distribution system degradation model to evaluate how the cost of energy, carbon emissions, and pollutant emissions change after applying building weatherization, energy efficiency, and electrification measures to the community. Results show that traditional weatherization and energy efficiency measures (upgrading lighting and appliances, improving insulation to current building code standards) are the most cost-effective, reducing the cost of energy and carbon emissions by 10-20 % for the current community. Heat pump water heaters offer a 40 % average reduction in carbon emissions and almost 50 % decrease in criteria pollutant emissions, but at a cost increase of 17-22 %. Appliance electrification also reduces carbon emissions 5-10 % but increases cost by 7 % to 25 %. For reducing carbon, government programs that support building electrification are most cost-effective when they combine switching from natural gas to electricity with high efficiency system. Electrifying hot water and appliances effectively reduces emissions but must be paired with improved low-income assistance programs to prevent increased energy burden for low-income families. The urban building energy model and electrical distribution simulations used in this study can be replicated in other low-income communities.