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UAS demand slides discuss the purpose, scope, and assumptions of the UAS Demand Forecast Study. It discusses some operational environments and market research study, this information is broad knowledge in the UAS community.
This paper reviews Space Network (SN) demand forecasting experience over the past 16 years and describes methods used in the forecasts. The paper focuses on the Single Access (SA) service, the most sought-after resource in the Space Network. Of the ten years of actual demand data available, only the last five years (1989 to 1993) were considered predictive due to the extensive impact of the Challenger accident of 1986. NASA's Space Network provides tracking and communications services to user spacecraft such as the Shuttle and the Hubble Space Telescope. Forecasting the customer requirements is essential to planning network resources and to establishing service commitments to future customers. The lead time to procure Tracking and Data Relay Satellites (TDRS's) requires demand forecasts ten years in the future a planning horizon beyond the funding commitments for missions to be supported. The long range forecasts are shown to have had a bias toward underestimation in the 1991 -1992 period. The trend of underestimation can be expected to be replaced by overestimation for a number of years starting with 1998. At that time demand from new missions slated for launch will be larger than the demand from ongoing missions, making the potential for delay the dominant factor. If the new missions appear as scheduled, the forecasts are likely to be moderately underestimated. The SN commitment to meet the negotiated customer's requirements calls for conservatism in the forecasting. Modification of the forecasting procedure to account for a delay bias is, therefore, not advised. Fine tuning the mission model to more accurately reflect the current actual demand is recommended as it may marginally improve the first year forecasting.
Congressional attention to Unmanned Aircraft Systems (UAS) has caused the Federal Aviation Administration (FAA) to move the National Airspace System (NAS) Integration project forward, but using guidelines, practices and procedures that are yet to be fully integrated with the FAA Aviation Management System. The real drive for change in the NAS will to come from both UAS operators and the government jointly seeing an accurate forecast of UAS usage demand data. This solid forecast information would truly get the attention of planners. This requires not an aggregate demand, but rather a picture of how the demand is spread across small to large UAS, how it is spread across a wide range of missions, how it is expected over time and where, in terms of geospatial locations, will the demand appear. In 2012 the Volpe Center performed a study of the overall future demand for UAS. This was done by aggregate classes of aircraft types. However, the realistic expected demand will appear in clusters of aircraft activities grouped by similar missions on a smaller geographical footprint and then growing from those small cells. In general, there is not a demand forecast that is tightly coupled to the real purpose of the mission requirements (e.g. in terms of real locations and physical structures such as wind mills to inspect, farms to survey, pipelines to patrol, etc.). Being able to present a solid basis for the demand is crucial to getting the attention of investment, government and other fiscal planners. To this end, Mosaic ATM under NASA guidance is developing a crowd sourced, demand forecast engine that can draw forecast details from commercial and government users and vendors. These forecasts will be vetted by a governance panel and then provide for a sharable accurate set of projection data. Our paper describes the project and the technical approach we are using to design and create access for users to the forecast system.
This paper proposes an energy management system (EMS) for nanogrids to balance the power supply and forecasted demand in consideration of forecasting errors arising from high instantaneous demand. The proposed EMS employs a power-balancing optimization process for forecasted demand and a reference power modulation strategy for forecasting errors. This power-balancing optimization utilizes nanogrid sources, such as photovoltaics, fuel cells, and batteries, to meet forecasted demand and a supercapacitor charging process to overcome issues with a low energy density. The proposed reference power modulation strategy is utilized to allocate power from a hybrid energy storage system consisting of a battery and supercapacitor in order to compensate for forecasting errors. In addition, this proposed strategy considers battery and supercapacitor constraints such as the power changing rate and total power limitations. Further, the power-balancing optimization process also operates at faster sampling rate than the reference power modulation process in order to improve the computational efficiency. The performance of the proposed EMS is evaluated using real data obtained from the Korea Electric Power Exchange.
Here, the principal objective of this study is to analyze the spatial and temporal variation of ground transportation airport demand and provide demand forecast to inform planning capability and explore alternatives for investments to accommodate airport growth. Because of its good adaptability and strong generalization ability for dealing with high-dimensional input, small-sample, and nonlinear spatial data, Gaussian process (GP) regression is used to provide forecast estimates using data from transportation network company (TNC) trips and urban rail passengers at Chicago's O'Hare International Airport. TNC airport trips differ significantly, with three times more distance, more than twice the travel time, and half of the share requests compared with nonairport trips. This highlights the need for separate demand models. Hourly analysis of the rail service indicates that this is likely heavily used by airport workers, whereas TNC services focus on travelers because of variations in the peak demand hours. Heteroscedastic GP regression is implemented because of differences in trip variance between night and day hours. Estimates are given for weekdays and weekend trips, and the 95% confidence intervals are calculated. The introduction of flight schedule information into the models shows marginal improvements in their performance. However, fitting a GP regression becomes computationally expensive with increased sample size and the introduction of spatial components. Transportation planners and policymakers can use the results and methods implemented in this study to optimize transportation assets and provide long-range simulations of the current and future conditions in the area.
The purpose of this project is to estimate the demand for various Urban Air Mobility Concepts (UAM) of Operations and to generate scenarios for use in analysis and simulations. The demand forecast model, previously developed under NASA/NIA Contract No: NNL13AA08B; Task Order No: NNL16AA36T, for an urban on-demand air-taxi commuter concept is the basis for this work.
The forecast is for the years 1981 - 2000 with benchmark years at 1985, 1990 and 2000. Two typs of markets are considered for this study: Hardware (worldwide total) - satellites, earth stations and control facilities (includes replacements and spares); and non-hardware (addressable by U.S. industry) - planning, launch, turnkey systems and operations. These markets were examined for the INTELSAT System (international systems and domestic and regional systems using leased transponders) and domestic and regional systems. Forecasts were determined for six worldwide regions encompassing 185 countries using actual costs for existing equipment and engineering estimates of costs for advanced systems. Most likely (conservative growth rate estimates) and optimistic (mid range growth rate estimates) scenarios were employed for arriving at the forecasts which are presented in constant 1980 U.S. dollars. The worldwide satellite market demand forecast predicts that the market between 181 and 2000 will range from $35 to $50 billion. Approximately one-half of the world market, $16 to $20 billion, will be generated in the United States.
In 2017, the PAMO/ISAT has sponsored a global aircraft emissions study with the Virginia Tech Air Transportation Systems Laboratory through a contract with the National Institute of Aerospace. As part of this work, Virginia Tech developed the Global Demand Model (GDM), which forecasts global commercial aviation passenger demand out to 2040.
A national nonmetropolitan land mobile traffic model for 1990-2000 addresses user classes, density classes, traffic mix statistics, distance distribution, geographic distribution, price elasticity, and service quality elasticity. Traffic demands for business, special industrial, and police were determined on the basis of surveys in 73 randomly selected nonurban counties. The selected services represent 69% of total demand. The results were extrapolated to all services in the non-SMSA areas of the contiguous United States. Radiotelephone services were considered separately. Total non-SMSA mobile radio demand (one way) estimates are given. General functional requirements include: hand portability, privacy, reduction of blind spots, two way data transmission, position location, slow scan imagery.
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With the emerging penetration of renewables and dynamic loads, the understanding of grid edge loading conditions becomes increasingly substantial. Load modelling researches commonly consist of explicitly expressed load models and non-explicitly expressed techniques, of which artificial intelligence approaches turn out to be the major path. This paper reveals the artificial intelligence-based load modelling technique to enhance the knowledge of current and future load information considering geographical and weather dependencies. This paper presents a recurrent neural network based sequence to sequence (Seq2Seq) model to forecast the short-term power loads. Also, a feature attention mechanism, which is along channel and time directions, is developed to improve the efficiency of feature learning. The experiments over three publicly available datasets demonstrate the accuracy and effectiveness of the proposed model.
This paper aims to forecast high-resolution (hourly) aggregated load for a certain region in the medium term (a few days to over a year). One region is defined as some places with similar climate characteristics because the climate influences people's daily lifestyles and hence the electric usage. We decom- pose the electric usage records into two parts: base load and seasonal load. Considering both temperature and time factors, different deep learning methods are adopted to characterize them. The first goal of our approach is to predict the peak load which is critical for power system planning. Furthermore, our proposed forecast method can provide the depiction of the hourly load profile to provide customized load curves for high- level real-time applications. The proposed method is tested on real-world historical data collected by CAISO, BPA, and PACW. The experimental results show that trained by three years of data, our method could reduce the prediction error for one-year lead hourly load below 5% MAPE, and predict the occurrence of the peak load for next year in CAISO with an error within three days. Furthermore, as a byproduct, an interesting observation on the impact of COVID-19 on human life was made and discussed based on these case studies.
This report examines the potential market of cargo ODM operations in the Northern California area. The analysis presented describes how UAS VTOL cargo aircraft (called heron Cargo ODM) could be used in a dual-role purpose to carry cargo and passengers in a large metropolitan area. The purpose of this study is to provide NASA with insights on the potential market for cargo ODM operations.The analysis presented in the report uses various datasets including the American Community Survey (ACS), LODES data and the IHS International Transearc hdatabase.
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This paper aims to forecast high-resolution (hourly) aggregated load for a certain region in the medium term (a few days to over a year). One region is defined as some places with similar climate characteristics because the climate influences people's daily lifestyles and hence the electric usage. We decompose the electric usage records into two parts: base load and seasonal load. Considering both temperature and time factors, different deep-learning methods are adopted to characterize them. The first goal of our approach is to predict the peak load which is critical for power system planning. Furthermore, our proposed forecast method can provide the depiction of the hourly load profile to provide customized load curves for high-level real-time applications. The proposed method is tested on real-world historical data collected by CAISO, BPA, and PACW. The experimental results show that trained by three years of data, our method could reduce the prediction error for a one-year lead hourly load below $5\%$ MAPE, and predict the occurrence of the peak load for next year in CAISO with an error within three days. Furthermore, as a byproduct, an interesting observation on the impact of COVID-19 on human life was made and discussed based on these case studies.
This paper aims to forecast high-resolution (hourly) aggregated load for a certain region in the medium term (a few days to over a year). One region is defined as some places with similar climate characteristics because the climate influences people's daily lifestyles and hence the electric usage. We decompose the electric usage records into two parts: base load and seasonal load. Considering both temperature and time factors, different deep-learning methods are adopted to characterize them. The first goal of our approach is to predict the peak load which is critical for power system planning. Furthermore, our proposed forecast method can provide the depiction of the hourly load profile to provide customized load curves for high-level real-time applications. The proposed method is tested on real-world historical data collected by CAISO, BPA, and PACW. The experimental results show that trained by three years of data, our method could reduce the prediction error for a one-year lead hourly load below 5% MAPE, and predict the occurrence of the peak load for next year in CAISO with an error within three days. Furthermore, as a byproduct, an interesting observation on the impact of COVID-19 on human life was made and discussed based on these case studies.
This paper presents a search algorithm based framework to calibrate origin-destination (O-D) market specific airline ticket demands and prices for the Air Transportation System (ATS). This framework is used for calibrating an agent based model of the air ticket buy-sell process - Airline Evolutionary Simulation (Airline EVOS) -that has fidelity of detail that accounts for airline and consumer behaviors and the interdependencies they share between themselves and the NAS. More specificially, this algorithm simultaneous calibrates demand and airfares for each O-D market, to within specified threshold of a pre-specified target value. The proposed algorithm is illustrated with market data targets provided by the Transportation System Analysis Model (TSAM) and Airline Origin and Destination Survey (DB1B). Although we specify these models and datasources for this calibration exercise, the methods described in this paper are applicable to calibrating any low-level model of the ATS to some other demand forecast model-based data. We argue that using a calibration algorithm such as the one we present here to synchronize ATS models with specialized forecast demand models, is a powerful tool for establishing credible baseline conditions in experiments analyzing the effects of proposed policy changes to the ATS.
Microgrids can experience frequency instability while operating in the islanded mode due to a significant demand generation imbalance. Presence of net metered (NM) loads can further complicate accurate demand forecasting. For these loads only the net demand, difference of load demand and generation, is available. This paper presents the various stages observed during microgrid restoration with high penetration of NM loads. Disaggregation of demand and generation from the net metered time series is shown to be essential for accurate demand forecasting in each of these stages. A disaggregation approach is proposed using correlation in the net metered data. Time series analysis is then used to forecast the demand in each stage of the microgrid restoration process. A case study using demand and generation time series data from residential loads, shows that the proposed approach is significantly more accurate for demand forecasting than using only the net-metered time series.