Bottom-up Simulation Research Articles

Anthropogenic heat from buildings in Los Angeles County: A simulation framework and assessment

Anthropogenic heat (AH), i.e., waste heat from buildings to the ambient environment, increases urban air temperature and contributes to the urban heat island effect, which leads to more air-conditioning energy use and higher associated waste heat during summer, forming a positive feedback loop. This study used a bottom-up simulation approach to develop a dataset of the annual hourly AH profiles for 1.7 million buildings in Los Angeles (LA) County for the year 2018 aggregated at three spatial resolutions: 450 m, 12 km, and the census tract. Building AH exhibits strong seasonal and diurnal patterns, as well as large spatial variations across the urban areas. Building AH peaks in May and reaches a maximum of 878 W/m2 within one of several AH hotspots in the region. Among the three major AH components (surface convection, heat rejection from HVAC systems, and zonal air exchange), the surface convection component is the largest, accounting for 78% of the total building AH across LA County. Higher AH is attributed to large building density, a high percentage of industrial buildings, and older building stock. While AH peaks during the day, the resulting ambient temperature increases are much larger during the night. During the July 2018 heatwave in LA County, building AH (excluding the surface component) leads to a daily maximum ambient temperature increase of up to 0.6 °C and a daily minimum ambient temperature increase of up to 2.9 °C. It is recommended that reducing summer building AH should be considered by policy makers in developing mitigation measures for cities to transition to clean energy while improving heat resilience.

Co-abatement of carbon and air pollutants emissions in China’s iron and steel industry under carbon neutrality scenarios

Co-abatement strategies that can reduce both CO2 and air pollutants emissions are essential for the green and low-carbon transition of China’s iron and steel industry. Most previous studies analyzed CO2 and air pollutants reduction policies independently, but the synergies and tradeoffs between the two types of policies have rarely been investigated. By establishing a bottom-up simulation model that considers combinations of different policy objectives, crude steel demand and technology development options, this study couples air pollutants reduction into the carbon neutrality pathways to evaluate the synergistic effects of different abatement strategies in China’s iron and steel industry. The study also incorporates the indirect CO2 and air pollutants emissions from the upstream energy production sectors into pathway analysis, in order to avoid cross-sectoral carbon and air pollution leakages. The results show that the current emissions reduction policies in the iron and steel industry are far from achieving the carbon neutrality goals. To achieve synergistic reduction in CO2 and air pollutants emissions, the short term policies should mainly rely on increasing the share of the scrap-electric arc furnace (EAF), while reducing crude steel demand and promoting breakthrough technologies, particularly hydrogen-based direct reduction, should play a major role in the long term. Meanwhile, China should promote the decarbonization of power generation and hydrogen production to reduce indirect CO2 and air pollutants emissions from the upstream sectors.

A two-stage optimization method for improving the load flexibility of existing district energy systems

Decarbonization of district energy systems is essential for China to meet its carbon neutrality goal by 2060. Most existing district energy systems are missing historical load data and have incomplete information, resulting in a lack of data support for the low-carbon transition. Moreover, demand-side load flexibility has not been fully exploited in the planning stage. In this paper, we developed a two-stage computational approach to optimize district loads. We first established an integrated Python framework, incorporating the TEASER simulation tool and AixLib model library, to efficiently calculate baseline loads through the bottom-up modeling and simulation of district buildings. Then, a price-based integrated demand response strategy was introduced. A mixed-integer nonlinear programming model was formulated to optimize the energy pricing strategy with the objective of minimum load fluctuations. Finally, a case study was employed to illustrate the feasibility of the calculation method, showing a normalized mean bias error of 7.17%. The results further demonstrated that the strategy could reduce the peak electric and heat loads by 3.55% and 9.57%, and increase load rates by 3.85% and 9.48%, respectively. The strategy could assist district energy service providers to optimize equipment capacity configuration and enhance the low-carbon planning potential of energy systems from the demand-side.

Summertime Urban Ammonia Emissions May Be Substantially Underestimated in Beijing, China.

Ammonia (NH3) is critical to the nitrogen cycle and PM2.5 formation, yet a great deal of uncertainty exists in its urban emission quantifications. Model-underestimated NH3 concentrations have been reported for cities, yet few studies have provided an explanation. Here, we explore reasons for severe WRF-Chem model underestimations of NH3 concentrations in Beijing in August 2018, including simulated gas-particle partitioning, meteorology, regional transport, and emissions, using spatially refined (3 km resolution) NH3 emission estimates in the agricultural sector for Beijing-Tianjin-Hebei and in the traffic sector for Beijing. We find that simulated NH3 concentrations are significantly lower than ground-based and satellite observations during August in Beijing, while wintertime underestimations are much more moderate. Further analyses and sensitivity experiments show that such discrepancies cannot be attributed to factors other than biases in NH3 emissions. Using site measurements as constraints, we estimate that both agricultural and non-agricultural NH3 emission totals in Beijing shall increase by ∼5 times to match the observations. Future research should be performed to allocate underestimations to urban fertilizer, power, traffic, or residential sources. Dense and regular urban NH3 observations are necessary to constrain and validate bottom-up inventories and NHx simulation.

Predicting Diverse Behaviors of Occupants When Turning Air Conditioners on/off in Residential Buildings: An Extreme Gradient Boosting Approach

Occupant behavior (OB) has a significant impact on household air-conditioner (AC) energy use. In recent years, bottom-up simulation coupled with stochastic OB modeling has been intensively developed for estimating residential AC consumption. However, a comprehensive analysis of the diverse behavioral preference patterns of occupants regarding AC use is hampered by the limited availability of large-scale residential energy demand data. Therefore, this study aimed to develop a prediction model for the residential household’s AC usage considering various OB-related diversity patterns based on monitoring data of appliance-level electricity use in a residential community of 586 households in Osaka, Japan. First, individual operation schedules and thermal preferences were identified and quantitatively extracted as the two main factors for the diverse behaviors across the whole community. Then, a clustering analysis classified the target households, finding four typical patterns for schedule preferences and three typical patterns for thermal preferences. These results were used, with time and meteorological data in the summer seasons of 2013 and 2014, as inputs for the proposed prediction model using Extreme Gradient Boosting (XGBoost). The optimized XGBoost model showed a satisfactory prediction performance for the on/off state in the testing dataset, with an F1 score of 0.80 and an Area under the Receiver Operating Characteristic (ROC) Curve (AUC) of 0.845.

Forecasting environmental and social benefits of adopting cleaner technologies in Indian brick manufacturing industry

Embodied carbon of new buildings can be effectively reduced by improving the manufacturing processes for energy intensive raw materials such as bricks and cement. Indian brick manufacturing industry is a major source of air pollution and greenhouse gas (GHG) emissions. This sector uses coal as a primary fuel in nearly 150,000 brick kilns, majority of which are based on energy in-efficient, fixed chimney bull-trench kiln (FCBTK) technology. Annually, the brick making in India consumes nearly 60 million tons of coal, produces almost 250 billion bricks and emits more than 150 million tons of carbon dioxide (CO2) and other GHG emissions. The pollutants produced include carbon monoxide (CO), sulfur oxides (SOx), nitrogen oxides (NOx), particulate matter (PM), methane (CH4), non-methane volatile organic compounds (NMVOC). This study provides the first comprehensive long-term (2020–2050) assessment of Indian brick kiln sector by forecasting the annual fuel demand, GHG emissions, pollutants formations and annual externality cost borne by the community. A Low Emissions Analysis Platform (LEAP) software based bottom-up simulation model has been developed for performing the assessment. Business as usual (BAU) and four alternative scenarios comprising of cleaner kiln technologies are evaluated. These technologies include zig zag kiln (ZZK), vertical shaft brick kiln (VSBK) and tunnel kiln (TK). Values for emission factors for pollutants, energy intensities of kilns, and social costs of pollutants are taken from the literature. Our results show that VSBK is the most energy efficient and environmentally friendly technology, closely followed by ZZK. For the year 2050, ZZK can save up to 16 billion dollars in externality costs and reduce coal demand by approximately 260 million tons. ZZK is not only a cleaner technology, but also has significant resemblance with conventional FCBTK technology which can be easily retrofitted to ZZK. The government and other stakeholders should promote ZZK adoption and develop suitable policy and financial instruments to address barriers to transition.

Detailed luminescence modelling in high-efficiency solar cells for precise calibration of spatially resolved characterisation methods: A bottom-up opto-electrical approach

In the scope of solar cell characterisation, spatially resolved imaging (SRI) methods (EL, PL and LBIC) have long been a standard procedure for valuable in-depth evaluation and extraction of various spatially resolved material properties, especially those related to the electrical behaviour. While this extraction can be straightforward in the case of laterally homogeneous devices, the situation is vastly different when the structural features are laterally varying, such as in the case of interdigitated back contact (IBC) solar cells. We show that in the case of laterally varying devices inherent device optical properties play a far more important role in determining the measured profile in this case and may indeed overshadow any underlying electrical effects. We therefore propose and validate a methodology that couples SRI characterisation with advanced bottom-up simulation of IBC solar cells. The method fully accounts for lateral device variability and allows for accurate extraction of the underlying electrical phenomena. We demonstrate the applicability of the method on state-of-the-art high-efficiency IBC solar cells, and explain the key factors, which could lead to misinterpretation of the results obtained solely by SRI measurements. • Interpretation of measured IBC electroluminescence profiles through opto-electrical simulation. • Decoupling of underlying optical and electrical phenomena. • Device optics overshadows recombination driven fluctuations of luminescence profiles. • Rear interface strongly influences the shape of the extracted luminescence profile. • Shape of the luminescence profile is highly dependent on imaging system’s aperture.

Energy usage and human behavior modeling for residential bottom-up energy simulation

We introduce a probabilistic modeling for a disaggregated Bottom-up simulation of residential energy usage. Parametric probability distributions are modeled with parameters that have a natural explanation in terms of usage and appliance power. Human behavior such as sleep and home occupancy variables are considered too, with its corresponding trained probabilistic Models. Model parameters are adjusted by the minimization of the Kullback–Leibler divergence from known appliance and behavior usage data. Self-generated photovoltaic Energy is included in the simulation with a battery for storage and electric vehicle usage. Simulations match individual and aggregated usage load profiles in the European REMODECE and RSE Italian load data sets. Obtained Models are useful for residential disaggregated simulations allowing individual appliances to change from house to house. Probabilistic distributions can be used as prior knowledge for energy management systems, risk management, and grid failure prediction and can be adapted based on non-stationary real-time house behavior and appliance usage.

Solute sorption, diffusion, and advection in macro–mesoporous materials: Toward a realistic bottom-up simulation strategy

Solute sorption, diffusion, and advection in macro–mesoporous materials: Toward a realistic bottom-up simulation strategy

Optimization on Spatial Distribution of Shelter through Dynamic Evacuation Simulation of High Density Urban Area-Xinjiekou Case

The optimal location of emergency shelters is essential for boosting the evacuation efficiency and resilience of cities. For determining evacuation demands and residents' evacuation allocations, a two-stage model for the optimization of shelter site that utilizes high-precision day and night population data derived from mobile signalling has been developed. In the initial phase, static network analysis served as the foundation for identifying the site arrangements with the smallest number of additional shelters via the enhanced set-covering model. The second-stage optimization determines the best location scheme with the minimum evacuation time using a modified social force model, along with the population's spatial heterogeneity, age structure, and evacuation route selection. Xinjiekou was chosen to assess evacuation efficiency. The utilization rates of shelters and the amount of congestion on the roads before and after optimization are assessed. The outcome demonstrated a 76.5% reduction in evacuation time, a vast improvement in the usage efficiency of shelters, and a significant reduction in congestion along the evacuation route. This method of location optimization marks a break from the usual empirical top-down research paradigm in favor of a bottom-up numerical simulation of the real evacuation process, which satisfies all local residents' and government's needs.

Interpretable machine learning of action potential duration restitution kinetics in single-cell models of atrial cardiomyocytes

Action potential duration (APD) restitution curve and its maximal slope (Smax) reflect single cell-level dynamic instability for inducing chaotic heart rhythms. However, conventional parameter sensitivity analysis often fails to describe nonlinear relationships between ion channel parameters and electrophysiological phenotypes, such as Smax. We explored the parameter–phenotype mapping in a population of 5000 single-cell atrial cell models through interpretable machine learning (ML) approaches. Parameter sensitivity analyses could explain the linear relationships between parameters and electrophysiological phenotypes, including APD90, resting membrane potential, Vmax, refractory period, and APD/calcium alternans threshold, but not for Smax. However, neural network models had better prediction performance for Smax. To interpret the ML model, we evaluated the parameter importance at the global and local levels by computing the permutation feature importance and the local interpretable model-agnostic explanations (LIME) values, respectively. Increases in ICaL, INCX, and IKr, and decreases in IK1, Ib,Cl, IKur, ISERCA, and Ito are correlated with higher Smax values. The LIME algorithm determined that INaK plays a significant role in determining Smax as well as Ito and IKur. The atrial cardiomyocyte population was hierarchically clustered into three distinct groups based on the LIME values and the single-cell simulation confirmed that perturbations in INaK resulted in different behaviors of APD restitution curves in three clusters. Our combined top-down interpretable ML and bottom-up mechanistic simulation approaches uncovered the role of INaK in heterogeneous behaviors of Smax in the atrial cardiomyocyte population.

A bottom-up simulation on competition of online interpersonal communication platforms.

The competition of interpersonal communication platforms is a complex process affected by various factors. This paper aims to simulate and analyze this process from a bottom-up perspective. Individual platform selection serves as the micro-foundation for the study. The evolution of online interpersonal communication networks, and innovations proposed by online interpersonal communication platforms, would also impact this process by affecting individual selection on those platforms. Three scenarios were designed for this study to simulate typical modes of competition. In this regard, the simulation results were compared to practical cases. Taken together, this bottom-up simulation model could reproduce and anticipate the applied competition process associated with such platforms. Based on this model, it was found that, in any case, one online interpersonal communication platform will eventually monopolize the market, either partly or entirely. The late entrant platform, comprising a major innovation, tends to fail when competing with the incumbent monopoly due to “network externalities.” Even when two competing platforms continue to propose innovations, and they will alternately lead the competition due to those innovations, this type of replacement of their competitive positions in the market may only occur a few times and then disappear completely.

Understanding Formulation and Temperature Effects on Dermal Transport Kinetics by IVPT and Multiphysics Simulation

It is often unclear how complex topical product formulation factors influence the transport kinetics through skin tissue layers, because of multiple confounding attributes. Environmental factors such as temperature effect are also poorly understood. In vitro permeation testing (IVPT) is frequently used to evaluate drug absorption across skin, but the flux results from these studies are from a combination of mechanistic processes. Two different commercially available formulations of oxybenzone-containing sunscreen cream and continuous spray were evaluated by IVPT in human skin. Temperature influence between typical skin surface temperature (32°C) and an elevated 37°C was also assessed. Furthermore, a multiphysics-based simulation model was developed and utilized to compute the flux of modeled formulations. Drug transport kinetics differed significantly between the two drug products. Flux was greatly influenced by the environmental temperature. The multiphysical simulation results could reproduce the experimental observations. The computation further indicated that the drug diffusion coefficient plays a dominant role in drug transport kinetics, influenced by the water content which is also affected by temperature. The in vitro testing and bottom-up simulation shed insight into the mechanism of dermal absorption kinetics from dissimilar topical products.

Global land projection based on plant functional types with a 1-km resolution under socio-climatic scenarios

This study presents a global land projection dataset with a 1-km resolution that comprises 20 land types for 2015–2100, adopting the latest IPCC coupling socioeconomic and climate change scenarios, SSP-RCP. This dataset was produced by combining the top-down land demand constraints afforded by the CMIP6 official dataset and a bottom-up spatial simulation executed via cellular automata. Based on the climate data, we further subdivided the simulation products’ land types into 20 plant functional types (PFTs), which well meets the needs of climate models for input data. The results show that our global land simulation yields a satisfactory accuracy (Kappa = 0.864, OA = 0.929 and FoM = 0.102). Furthermore, our dataset well fits the latest climate research based on the SSP-RCP scenarios. Particularly, due to the advantages of fine resolution, latest scenarios and numerous land types, our dataset provides powerful data support for environmental impact assessment and climate research, including but not limited to climate models.

UBEM.io: A web-based framework to rapidly generate urban building energy models for carbon reduction technology pathways

Policymakers are struggling to understand what specific mixes of building retrofitting upgrades are necessary to achieve carbon emission targets. Urban building energy modeling (UBEM) is a bottom-up simulation method to develop policy measures for building stocks. However, the use of UBEM tools requires hard-to-find individuals with training in multiple domains and significant setup time exacerbated through a lack of standardized building use and construction databases. To address these challenges, this paper presents UBEM.io, a novel web-based framework to rapidly generate UBEMs in an automated and scalable fashion with clear handover points. UBEM.io is designed to help any city or municipality conduct low-cost, rapid energy and carbon emissions scenario studies. UBEM.io contributes to practice and research in multiple ways: automated generation of urban-scale building geometries based on widely available inputs; assignment of building simulation templates from a pre-populated library; matching of templates to individual buildings via archetypes; and visualization of simulation results for various carbon emissions reduction pathways. The framework was piloted in Evanston, IL (USA) to build an UBEM comprising 1,363 buildings. It was then successfully deployed with representatives from eight municipalities: Braga (Portugal), Cairo (Egypt), Dublin (Ireland), Florianopolis (Brazil), Kiel (Germany), Middlebury, VT (USA), Montreal (Canada), and Singapore.

Solute Sorption, Diffusion, and Advection in Macro–Mesoporous Materials: Toward a Realistic Bottom-Up Simulation Strategy

Solute Sorption, Diffusion, and Advection in Macro–Mesoporous Materials: Toward a Realistic Bottom-Up Simulation Strategy

Institutional modelling: Adding social backbone to agent-based models

Institutional modelling is a branch of agent-based modelling and simulation (ABMS) that pays special attention to the social structure by incorporating institutions into these models. Institutions, in this regard, are the rules of the system that shape individual behaviour and interaction. Institutional modelling can make use of the Institutional Grammar (IG), which has 6 pre-specified components to conceptualize institutions. The IG can be used for collecting institutional data for modelling purposes, for coding institutions, and for integrating them into different parts of an agent-based model.This modelling approach helps capture the structural complexities of social systems in agent-based models. It also supports the explanation of the emergence and dynamics of institutions, not only for better understanding institutions but also for studying social systems, especially for policy analysis.•Institutional modelling is a branch of agent-based modelling that focuses on and explicitly models the social aspects of socio-ecological-technical systems.•Institutional modelling supports theory development by enabling modellers to study institutions, and institutional change within, the systems they are embedded in.•While agent-based modelling is a bottom-up (individual-based) simulation approach, institutional modelling also incorporates top-down institutional structures and aims at studying interactions between bottom-up processes and top-down structural patterns.

Ontogeny of Scaling Factors for Pediatric Physiology-Based Pharmacokinetic Modeling and Simulation: Microsomal Protein Per Gram of Liver.

Microsomal protein per gram of liver (MPPGL) is an important scaling factor for bottom-up physiology-based pharmacokinetic modeling and simulation, but data in pediatrics are limited. Therefore, MPPGL was determined in 160 liver samples from pediatric (n = 129) and adult (n = 31) donors obtained from four sources: the University of Maryland Brain and Tissue Bank (UMBTB), tissue retrieval services at the University of Minnesota and University of Pittsburgh, and Sekisui-Xenotech. Tissues were homogenized and subjected to differential centrifugation to prepare microsomes, and cytochrome c reductase activities in tissue homogenates and microsomes were used to estimate cytochrome P450 reductase (POR) activity as a marker of microsomal recovery; microsomal POR content was also assessed by quantitative proteomics. MPPGL values varied 5- to 10-fold within various age groups/developmental stages, and tissue source was identified as a contributing factor. Using a "trimmed" dataset comprised of samples ranging from 3 to 18 years of age common to the four sources, POR protein abundance and activity in microsomes and POR activity in homogenates was lower in UMBTB samples (autopsy) compared with other sources (perfused/flash-frozen). Regression analyses revealed that the UMBTB samples were driving an apparent age effect as no effect of age on log-transformed MPPGL values was observed when the UMBTB samples were excluded. We conclude that a mean±SD MPPGL value of 30.4±1.7 mg/g is representative between one month postnatal age and early adulthood. Potential source effects should be considered for studies involving tissue samples from multiple sources with different procurement and processing procedures. SIGNIFICANCE STATEMENT: Microsomal protein per gram of liver (MPPGL) is an important scaling factor for bottom up PBPK modeling and simulation, but data in pediatrics are limited. Although MPPGL varies 5- to 10-fold at a given developmental stage, a value of 30.4 ± 1.7 mg/g (mean ± SD) is representative between one month postnatal age and early adulthood. However, when tissue samples are obtained from multiple sources, different procurement and processing procedures may influence the results and should be taken into consideration.

Predicting household air conditioners’ on/off state considering occupants’ preference diversity: A study in Chongqing, China

The input setting of an air conditioner (AC) usage schedule has a significant influence on the results of the bottom-up simulation model of household AC energy use. To set more accurate inputs when estimating the AC energy consumption based on the bottom-up simulation model, it is essential to establish a prediction model for the AC on/off state. However, due to the limitation of the data collection of household AC usage, the occupant diversity of household AC usage behavior could not be considered fully in existing research. This study aims to establish a prediction model of the AC on/off state combined with occupant behavior diversity on AC usage preference, based on the monitoring dataset of 1,274 ACs in Chongqing, China. First, clustering analysis was employed to identify the occupant thermal and schedule preference diversity on AC usage. Three typical thermal preference patterns were identified for wall-mounted AC and four for floor-standing AC. Three typical schedule preference patterns were discovered for both the wall-mounted AC and floor-standing AC. The results of preference diversity, as well as other environmental and time parameters, were used as inputs for the AC on/off state modeling. In this process, XGBoost is used to establish the prediction model. The proposed model shows good performance for the majority of AC samples in the testing dataset. For wall-mounted AC, in the testing dataset, the F1-score is above 0.7 for approximately 70% of the total AC samples; for floor-standing AC, the F1-score of approximately 50% of the total AC samples is above 0.67. The results and methods in our study could be helpful for improving the input setting of AC schedules in a bottom-up simulation model of household AC energy use.

Evaluating decarbonization scenarios and energy management requirement for the residential sector in Japan through bottom-up simulations of energy end-use demand in 2050

Decarbonization scenarios for achieving net zero greenhouse gas emissions in the Japanese residential sector by 2050 were examined using a bottom-up simulation model of energy end-use demand. The examined scenarios involve the dissemination of currently available technology, including highly insulated houses, high-efficiency equipment, high-efficiency appliances, electrification, and building-integrated photovoltaics (PV) in detached houses. The results show that decarbonization can be mostly achieved by the studied scenarios, especially through the dissemination of highly insulated buildings and high-efficiency water heaters as well as the installation of PV for all detached houses. This scenario reduced the total primary energy demand by 61% in 2013. A land-use strategy for increased detached houses is preferable for increasing PV capacity. For the scenario that achieves net zero energy, the predicted heat load intensity and electricity consumption per capita are almost the same as those of the Low Energy Demand (LED) scenario. Increased dependence on PV will create an imbalance in the relationship between electricity supply and demand, and 26% of the residential energy demand would rely on non-battery energy storage, such as pumped hydropower and hydrogen, owing to the seasonal time gap between residual and deficit electricity.