Integrated Model for Predicting Demand of Supersonic Transports
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Toni Trani↗
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Results for “demand modelling”
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NAS Demand Predictions, Transportation Systems Analysis Model (TSAM) Compared with Other Forecasts
The current work incorporates the Transportation Systems Analysis Model (TSAM) to predict the future demand for airline travel. TSAM is a multi-mode, national model that predicts the demand for all long distance travel at a county level based upon population and demographics. The model conducts a mode choice analysis to compute the demand for commercial airline travel based upon the traveler s purpose of the trip, value of time, cost and time of the trip,. The county demand for airline travel is then aggregated (or distributed) to the airport level, and the enplanement demand at commercial airports is modeled. With the growth in flight demand, and utilizing current airline flight schedules, the Fratar algorithm is used to develop future flight schedules in the NAS. The projected flights can then be flown through air transportation simulators to quantify the ability of the NAS to meet future demand. A major strength of the TSAM analysis is that scenario planning can be conducted to quantify capacity requirements at individual airports, based upon different future scenarios. Different demographic scenarios can be analyzed to model the demand sensitivity to them. Also, it is fairly well know, but not well modeled at the airport level, that the demand for travel is highly dependent on the cost of travel, or the fare yield of the airline industry. The FAA projects the fare yield (in constant year dollars) to keep decreasing into the future. The magnitude and/or direction of these projections can be suspect in light of the general lack of airline profits and the large rises in airline fuel cost. Also, changes in travel time and convenience have an influence on the demand for air travel, especially for business travel. Future planners cannot easily conduct sensitivity studies of future demand with the FAA TAF data, nor with the Boeing or Airbus projections. In TSAM many factors can be parameterized and various demand sensitivities can be predicted for future travel. These resulting demand scenarios can be incorporated into future flight schedules, therefore providing a quantifiable demand for flights in the NAS for a range of futures. In addition, new future airline business scenarios are investigated that illustrate when direct flights can replace connecting flights and larger aircraft can be substituted, only when justified by demand.
Viken, Jeff↗
Quantifying the effect of multiple load flexibility strategies on commercial building electricity demand and services via surrogate modeling
The expansion of commercial building demand response as a demand-side management resource for the electric grid necessitates new decision support resources for customers seeking to assess the benefit–risk tradeoffs of possible strategies for energy flexible building operations. To address this need, we, in this study, develop surrogate models that predict the impacts of several load flexibility strategies on commercial building electricity demand and indoor temperature, focusing on offices and retail buildings at multiple scales. The surrogate models are fit to a synthetic database generated via whole building simulations, which establish the relationships between the key operational features of a given strategy and potential changes in building demand and temperature across a variety of contexts. The surrogate models are translated to a Bayesian framework to allow straightforward communication of uncertainty and parameter updating given new evidence. We find strong predictive performance across the suite of models, underscoring the usefulness of the approach in guiding decisions about implementing load flexibility strategies under a particular set of operational and environmental conditions.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Use of arm measurements to improve radiative transfer models used in climate models
The demands of accurate predictions of radiative transfer for climate applications are well-documented. While much effort is being devoted to evaluating the accuracy of the GCM radiative transfer schemes, the problem of developing accurate, computationally efficient schemes for climate models still remains. This paper discusses our efforts in developing accurate and fast computational methods for global and regional climate models.
Bergstrom, Robert W.↗
Estimation of Unsteady Aerodynamic Models from Dynamic Wind Tunnel Data
Demanding aerodynamic modelling requirements for military and civilian aircraft have motivated researchers to improve computational and experimental techniques and to pursue closer collaboration in these areas. Model identification and validation techniques are key components for this research. This paper presents mathematical model structures and identification techniques that have been used successfully to model more general aerodynamic behaviours in single-degree-of-freedom dynamic testing. Model parameters, characterizing aerodynamic properties, are estimated using linear and nonlinear regression methods in both time and frequency domains. Steps in identification including model structure determination, parameter estimation, and model validation, are addressed in this paper with examples using data from one-degree-of-freedom dynamic wind tunnel and water tunnel experiments. These techniques offer a methodology for expanding the utility of computational methods in application to flight dynamics, stability, and control problems. Since flight test is not always an option for early model validation, time history comparisons are commonly made between computational and experimental results and model adequacy is inferred by corroborating results. An extension is offered to this conventional approach where more general model parameter estimates and their standard errors are compared.
Murphy, Patrick↗
Tethys: A Spatiotemporal Downscaling Model for Global Water Demand
Humans use water for many important tasks, such as drinking, growing food, and cooling power plants. Since future water demands depend on complex global interactions between economic sectors (e.g., demand for wheat in one country causing demand for water to grow that wheat in another country), it is often modeled at coarse spatial and temporal scales as part of models that account for complex, multi-sector system dynamics. However, models that project future water availability typically simulate physical processes at much finer scales. Tethys enables integration between these kinds of models by downscaling region-scale water demand projections using sector-specific proxies and formulas.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Modeling household online shopping demand in the U.S.: a machine learning approach and comparative investigation between 2009 and 2017
Despite the rapid growth of online shopping and research interest in the relationship between online and in-store shopping, national-level modeling and investigation of the demand for online shopping with a prediction focus remain limited in the literature. Here, this paper differs from prior work and leverages two recent releases of the U.S. National Household Travel Survey (NHTS) data for 2009 and 2017 to develop machine learning (ML) models, specifically gradient boosting machine (GBM), for predicting household-level online shopping purchases. The NHTS data allow for not only conducting nationwide investigation but also at the level of households, which is more appropriate than at the individual level given the connected consumption and shopping needs of members in a household. We follow a systematic procedure for model development including employing Recursive Feature Elimination algorithm to select input variables (features) in order to reduce the risk of model overfitting and increase model explainability. Among several ML models, GBM is found to yield the best prediction accuracy. Extensive post-modeling investigation is conducted in a comparative manner between 2009 and 2017, including quantifying the importance of each input variable in predicting online shopping demand, and characterizing value-dependent relationships between demand and the input variables. In doing so, two latest advances in machine learning techniques, namely Shapley value-based feature importance and Accumulated Local Effects plots, are adopted to overcome inherent drawbacks of the popular techniques in current ML modeling. The modeling and investigation are performed at the national level, with a number of findings obtained. The models developed and insights gained can be used for online shopping-related freight demand generation and may also be considered for evaluating the potential impact of relevant policies on online shopping demand.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Leveraging Hydropower Multi-Sensor Data for Inference and Age-Informed Modeling
Increased demand of operational flexibility such as faster ramp up/down in generation, and more frequent start/stops are putting hydropower plants and their associated components in unprecedented stress. Consequently, these plants are at the high risk of extended and more frequent outage to accommodate unscheduled, and unexpected maintenance. Therefore, hydropower plants are in critical need of data driven and age-informed analysis for their regular and unscheduled operation. Yet not all hydropower plants are exhaustively equipped with sensors and/or measurement streams for their respective components – demanding solutions on how to detect, identify, and locate the cause of any event from the unobservable. Idaho National Laboratory (INL) analyzed the anonymized measurements and event records from the Hydropower Research Institute (HRI) to address this issue, as part of the Water Power Technologies Office (WPTO) funded one year multi-lab project. First, we investigated how time series of multiple sensor measurements can be leveraged to identify an event “root cause” as well as to develop an inference (i.e., estimate the unobservable) problem. INL also investigated how individual hydropower components’ reaction or response times vary across the pre-event, during event, and post-event conditions – enabling the hydropower dynamic models to be age-informed. Finally, the impact of clustering multi-sensor time series on short-term vibration prediction is analyzed. INL will present key findings from these analyses and recommend next steps for stakeholder adoption.
13 HYDRO ENERGY↗
Thick Electrode Design for Facile Electron and Ion Transport: Architectures, Advanced Characterization, and Modeling
The demand for lithium ion batteries continues to expand for powering applications such as portable electronics, grid-scale energy storage, and electric vehicles. As the application requirements advance, the innovation of lithium ion batteries toward higher energy density and power output is required. Along with the investigation of new materials, an important strategy for increasing battery energy content is to design electrodes with high areal loading to minimize the fraction of nonactive materials such as current collectors, separators, and packaging components, resulting in significant gains in energy content and the reduction of the system-level cost. However, the adoption of thick high areal loading electrodes has been impeded by sluggish charge transport and mechanical instability. With conventional slurry cast electrodes, battery function significantly deteriorates with increases in electrode thickness due to high cell polarization and the incomplete utilization of active materials. Thus, a consideration of approaches that facilitate an understanding and eventual adoption of high-loading electrodes is warranted to enable the deliberate advancement of next-generation batteries. Herein, this Account considers three aspects critical to the science and technology of thick high-loading electrodes. The first discussion covers recent approaches to the design and fabrication of high-loading electrodes. Ensuring electrical contact throughout the electrode is accomplished through the manipulation of conductive additives or using a conductive scaffold within the electrode. Ion transport can be facilitated through electrode design and fabrication approaches that deliberately control the electrode porosity and tortuosity. Second, advanced characterization methodologies are presented as the ability to determine the origins of transport limitations provide the insight needed to deliberately approach future designs. Spectroscopic and diffraction methods have been used to characterize the 2D and 3D pore structure and composition of the electrodes. Furthermore, operando methods that yield spatially and temporally resolved information regarding the progression of the electrochemical reaction are highlighted. The third aspect considered is the utilization of modeling. Physically based continuum models linked with the results of experimental characterization have been demonstrated and then allow the rapid simulation of a variety of deliberate electrode designs and their impacts on functional electrochemistry. Variables relevant to the designs can be tested by the model under a series of use conditions to identify those of most promise for a specific application. Finally, an outlook on future opportunities for high-loading battery electrode research is provided to inform and entice practitioners in the field to pursue these important directions of inquiry.
25 ENERGY STORAGE↗
Model predictive control for demand flexibility: Real-world operation of a commercial building with photovoltaic and battery systems
Hundreds of studies have investigated Model Predictive Control (MPC) for the optimal operation of building energy systems in the past two decades. However, MPC field tests are still uncommon, especially for small- and medium-sized commercial buildings and for buildings integrated with onsite renewables. This paper describes the implementation and the long-term performance evaluation of an MPC controller in a small commercial building equipped with behind-the-meter photovoltaics and electrochemical batteries. MPC controls space conditioning, commercial refrigeration, and the battery system. We tested two types of demand flexibility applications in the field: electricity bill minimization under time-of-use tariffs and responses to grid flexibility events. Results show that the proposed controller achieves 12% of annual electricity cost savings and 34% peak demand reduction against the baseline, while respecting thermal comfort and food safety. The field tests also demonstrate the ability of the MPC controller to provide a multitude of grid services including real-time pricing, demand limiting, load shedding, load shifting, and load tracking, using the same optimization framework.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Moving Beyond Direct Load Control: A Maturity Model for Realizing the Promise of Demand Flexibility
This report discusses a new maturity model that regulators and utilities can use to guide and expand demand flexibility programs and enable the resources to provide more grid services. The model has six demand flexibility categories: planning and design; customer engagement; program operations; evaluation, measurement and verification; distributed energy resource orchestration; and data infrastructure. Within each category, capabilities are identified and described on a maturity scale that ranges from performing below expectations to improving on best practices.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Powered by dGen Webinar [Slides]
NLR's Powered By Webinar Series featuring NLR's dGen Modeling Tool. The Distributed Generation Market Demand (dGenTM) model simulates customer adoption of distributed energy resources for residential, commercial, and industrial entities in the United States or other countries through 2050. The model enables analysis at multiple geographic levels (national, state, and utility, or below) and offers sophistication in representation of decision-making regarding economic and behavioral considerations. Analysts have used dGen to answer questions about load forecasting and integrated resource planning, policy analysis, locational value of distributed energy resources, and more. dGen is open source, and various energy organizations - including independent system operators, regional transmission organizations, and the California Energy Commission - use the model internally.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Modeling of materials supply, demand and prices
The societal, economic, and policy tradeoffs associated with materials processing and utilization, are discussed. The materials system provides the materials engineer with the system analysis required for formulate sound materials processing, utilization, and resource development policies and strategies. Materials system simulation and modeling research program including assessments of materials substitution dynamics, public policy implications, and materials process economics was expanded. This effort includes several collaborative programs with materials engineers, economists, and policy analysts. The technical and socioeconomic issues of materials recycling, input-output analysis, and technological change and productivity are examined. The major thrust areas in materials systems research are outlined.
Source record↗
A multi-item maintenance center inventory model for low-demand reparable items
In many military and commercial contexts, complex equipment undergoes scheduled maintenance overhauls at regular intervals during which all failed components are replaced. Failure to have replacements on hand for all failed parts requires emergency measures at premium cost. When reparable parts are highly reliable and expensive, both holding and shortage costs are high. This model determines the reparable parts inventory for a maintenance center under three alternative criteria: (1) maximizing job-completion rate subject to constraint on total holding costs, (2) minimizing total holding costs plus expected job noncompletion costs, and (3) minimizing total holding costs subject to a required minimum job-completion rate. Exact solutions may be obtained using dynamic programming. Approximate solutions, found easily by marginal analysis, have readily computed bounds on possible error. The solution methods for the three formulations are illustrated in a simple example.
Schaefer, M. K.↗
Mitigating energy demand sector emissions: The integrated modelling perspective
Mitigating carbon emissions in the current energy system will require fundamental changes of both the energy supply and the energy demand sectors. Previous global model-based analyses, however, have focused mostly on energy supply transformations, while the energy demand sector changes are less well understood. In this study, this knowledge gap is addressed by analysing in detail the projected future energy demand projections, and the required demand-side changes to reach stringent mitigation targets using a suite of integrated assessment models. We examine industry, transport and buildings sector pathways across four models and three different reference scenarios from the Shared-Socioeconomic Pathway framework which is used as a set of common future perspectives by the climate research community. The demand side mitigation efforts are compared to a more detailed, sector-specific, technology-oriented assessments of abatement potential based on a literature review for the year 2030. The results indicate that strong emission growth in the industry and transport sector can be attributed to increasing final energy per capita and population growth. In the stringent mitigation scenarios energy efficiency, electrification and switching to low carbon fuel are all required in the short term. In the green growth SSP1 scenario the required emission reduction is significantly less than other scenarios showing that the demand growth and the technology development largely affects the sectors’ mitigation challenge. The technology assessment estimates that in particular in the transport and buildings sector there is a higher potential to reduce demand-side emissions through energy efficiency improvements than currently envisioned in the integrated assessment models.
Edelenbosch, Oreane Y.↗
Open Power System Datasets and Open Simulation Engines: A Survey Toward Machine Learning Applications
A major factor behind the success of machine learning (ML) models in multiple domains is the availability and accessibility of large, labeled, and well-organized datasets for training and benchmarking. In comparison, power grid datasets face three major challenges: (i) real-world data is often restricted by regulatory constraints, privacy reasons, or security concerns, making it difficult to obtain and work with; (ii) synthetic datasets, which are created to address these limitations, often have incomplete information and are released using specialized tools, making them inaccessible to the broader community; and, (iii) input-output datasets are difficult to generate through simulation for non-experts because open-source simulators are not known outside the power system community. This survey addresses these challenges by serving as an entry point to publicly available datasets and simulators for researchers venturing in this area. We review the current landscape of open-source power network data, machine models, consumer demand profiles, renewable generation data, and inverter models. We also examine open-source power system simulators, which are crucial for generating high-quality, high-fidelity power grid datasets. We aim to provide a foundation for overcoming data scarcity and advance towards a structured web of datasets and simulators to support the development of ML for power systems.
42 ENGINEERING↗
Data-driven modeling to enhance municipal water demand estimates in response to dynamic climate conditions
Altered precipitation and temperature patterns from a changing climate will affect supply, demand, and overall municipal water system operations throughout the arid western U.S. While supply forecasts leverage hydrological models to connect climate influences with surface water availability, demand forecasts typically estimate water use independent of climate and other externalities. Stemming from an increased focus on seasonal water demand management, we use the Salt Lake City, Utah municipal water system as a test bed to assess model accuracy versus complexity trade-offs between simple climate-independent econometric-based models and complex climate-sensitive data-driven models to average to extreme wet and dry climate conditions—representative of a new climate normal. Here, the climate-independent model displayed low performance during extreme dry conditions with predictions exceeding 90% and 40% of the observed monthly and seasonal volumetric demands, respectively, which we attribute to insufficient model complexity. The climate-sensitive models displayed greater accuracy in all conditions, with an ordinary least squares model demonstrating a measurable reduction in prediction bias (3.4% vs. -27.3%) and RMSE (74.0 lpcd vs. 294 lpcd) compared to the climate-independent model. The climate-sensitive workflow increased model accuracy and characterized climate-demand interactions, demonstrating a novel tool to enhance water system management.
54 ENVIRONMENTAL SCIENCES↗
Energy Prediction under Changed Demand Conditions: Robust Machine Learning Models and Input Feature Combinations
Deciding on a suitable algorithm for energy demand prediction in a building is non-trivial and depends on the availability of data. In this paper we compare four machine learning models, commonly found in the literature, in terms of their generalization performance and in terms of how using different sets of input features affects accuracy. This is tested on a data set where consumption patterns differ significantly between training and evaluation because of the Covid-19 pandemic. We provide a hands-on guide and supply a Python framework for building operators to adapt and use in their applications.
Schranz, Thomas↗