HydroWIRES-PNNL/fisch
Forecast Informed Scheduler for Hydropower (FIScH)
Broman, Dan [Pacific Northwest National Laboratory↗
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Results for “forecast scheduling”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Flow Forecasting on New England’s Great River Hydro (Abstract)
Great River Hydro (GRH) operates 13 generating stations and 3 storage-only reservoirs along the Upper Connecticut River, draining 6,266 square miles. Managing the reservoirs requires coordination over a couple days. So far seasonal flow-forecast and medium range probabilistic flow forecast during high flow conditions are leading to satisfactory management. Great River Hydro is seeking technical assistance to evaluate potential improvements in inflow forecasting and scheduling accuracy, particularly during short- to medium- duration periods (1-10 days). Improved accuracy is anticipated to enhance the efficiency with which GRH utilizes water, improving GRH’s ability to hit the best priced hours throughout the system and enhancing revenues as a result.
13 HYDRO ENERGY↗
Delta's role in reaching the fourth environment
The Delta launch vehicle has played a significant role in spaceand airborne studies since its first launch in 1960, and a high volume period of service is planned for the 1980s. The historical role played by Delta in launching satellites from 1960 to the present is discussed, and vehicle modifications made during this period are summarized. It is shown that out of 154 launches, 143 proved successful for an overall reliability of 93%. The forecasted launch schedule through 1985 is also presented. Various modifications are now under way to provide spacecraft interchangeability with the Shuttle: a Payload Assist Module (PAM) is proposed to provide an orderly transition from the Delta expendable vehicle to the Shuttle reusable vehicle; the new Delta 3920 Improved Second Stage is the result of a need for improved Delta performance to meet 3910 payload capabilities; the firing sequence of the solid rocket motors was altered from five at liftoff and four during ascent to a sequence of six and three, thereby increasing spacecraft weight in geosynchronous transfer orbit. Potential future improvements discussed include the Delta 4920, 9-ft-diam fairing, booster engine performance, PAM solid motor performance, a universal second stage, a hydrogen-oxygen second stage, and large strap-on solids.
Grimes, D. W.↗
JPL Resource Allocation Planning Scheduling Office (RAPSO) long range forecasting
The paper introduces the concept of the long range forecasting function, which is then used to identify periods of peak oversubscription of resources years before they actually happen, so that proper corrective actions (construction of additional antennas, reduction in support committment to the impacted projects) can be implemented.
RAPSO long range forecasting↗
Mission and Assets Database
Mission and Assets Database (MADB) Version 1.0 is an SQL database system with a Web user interface to centralize information. The database stores flight project support resource requirements, view periods, antenna information, schedule, and forecast results for use in mid-range and long-term planning of Deep Space Network (DSN) assets.
Baldwin, John↗
Integrated Planning and Scheduling for NASA’s Deep Space Network – from Forecasting to Real-time
Over a period of several years, the software systems that plan and schedule the use of NASA’s Deep Space Network (DSN) for the projects it serves have been upgraded from a disparate set of decades-old software components, to an integrated suite covering long-range planning and forecasting, all the way to real-time scheduling. The most recent component of this suite is known as LAPS, for Loading Analysis and Planning Software, and is responsible for long-term planning and forecasting, including studies and analysis of new missions, changed mission requirements, downtime, and new or changed antenna capabilities. This paper discusses the architecture of LAPS and its interfaces with other elements of DSN planning and scheduling, its user interfaces, and some lessons learned from development and deployment.
Lad, Jigna↗
Identification of Robust Terminal-Area Routes in Convective Weather
Convective weather is responsible for large delays and widespread disruptions in the U.S. National Airspace System, especially during summer. Traffic flow management algorithms require reliable forecasts of route blockage to schedule and route traffic. This paper demonstrates how raw convective weather forecasts, which provide deterministic predictions of the vertically integrated liquid (the precipitation content in a column of airspace) can be translated into probabilistic forecasts of whether or not a terminal area route will be blocked. Given a flight route through the terminal area, we apply techniques from machine learning to determine the likelihood that the route will be open in actual weather. The likelihood is then used to optimize terminalarea operations by dynamically moving arrival and departure routes to maximize the expected capacity of the terminal area. Experiments using real weather scenarios on stormy days show that our algorithms recommend that a terminal-area route be modified 30% of the time, opening up 13% more available routes that were forecast to be blocked during these scenarios. The error rate is low, with only 5% of cases corresponding to a modified route being blocked in reality, whereas the original route is in fact open. In addition, for routes predicted to be open with probability 0.95 or greater by our method, 96% of these routes (on average over time horizon) are indeed open in the weather that materializes
Pfeil, Diana Michalek↗
A prediction model to forecast the cost impact from a break in the production schedule
The losses which are experienced after a break or stoppage in sequence of a production cycle portends an extremely complex situation and involves numerous variables, some of uncertain quantity and quality. There are no discrete formulas to define the losses during a gap in production. The techniques which are employed are therefore related to a prediction or forecast of the losses that take place, based on the conditions which exist in the production environment. Such parameters as learning curve slope, number of predecessor units, and length of time the production sequence is halted are utilized in formulating a prediction model. The pertinent current publications related to this subject are few in number, but are reviewed to provide an understanding of the problem. Example problems are illustrated together with appropriate trend curves to show the approach. Solved problems are also given to show the application of the models to actual cases or production breaks in the real world.
Delionback, L. M.↗
Tactical Surface Metering Procedures for Charlotte Douglas International Airport
NASA has been collaborating with the Federal Aviation Administration (FAA) and aviation industry partners to develop and demonstrate new concepts and technologies for the Integrated Arrival, Departure, and Surface (IADS) traffic management capabilities under the Airspace Technology Demonstration 2 (ATD-2) project. The primary goal of the ATD-2 project is to improve the predictability and the operational efficiency of the air traffic system in metroplex environments while maintaining or improving throughput by enhancing and integrating arrival, departure and surface prediction, scheduling, and management systems. In the Phase 1 Baseline IADS Demonstration, the tactical surface scheduling capability and the user interfaces for ramp controllers and ramp traffic managers were implemented for ramp operations. The purpose of the tactical surface scheduling capability is to provide the airline ramp controller with aircraft pushback advisories that prevent surface congestion and to respond to surface and airspace constraints that become known over relatively short time horizons. For this purpose, the tactical surface metering tool first estimates the capacity of current and near-future runway resources from flight schedule and surveillance data. With demand forecasts and predicted taxi trajectories, this tool computes an efficient runway schedule of aircraft in the planning horizon based on their readiness, Earliest Off-Block Times (EOBTs), and a ration by schedule (RBS) rule. Details on the implementation of the Tactical Surface Metering tool will be provided in the full paper. Both pushback and recommended hold times advisories provided by this surface metering tool are shown on the user interfaces for the ramp controller and the ramp traffic manager, called Ramp Traffic Console (RTC) and Ramp Manager Traffic Console (RMTC), respectively. There is excess queue time in the system due to demand capacity imbalance, this time can be taken as a hold on the runway queue or at the gate and was referred to as the Metering Value. This metering value can be adjusted by the Ramp Manager in collaboration with Air Traffic Controller-Tower Traffic Management Coordinator (TMC). They selected a set of metering values as default values for the tool during human-in-the-loop simulation. As the metering value increases, there is a decrease in the gate hold and increase in the queue time at the runway. Procedures and Information needs related to managing the surface metering procedures were researched in the simulated environment. These procedures will be compared to the procedures adopted at Charlotte Douglas International Airport when the tools were deployed and adopted in November 2017 for one departure push bank per day. Feedback regarding initial issues, information needs such as the need to see EOBTs on the flight data tags and how they compare to scheduled times will also be discussed in the full paper. Initial results will be provided regarding the choice of the metering value and how it was adjusted on a daily basis and what procedures evolved will also be presented in the paper.
surface metering↗
Inertia estimation for power grids: A review of methods, challenges, and future prospects
The electric power grid is undergoing a significant transformation, shifting from traditional synchronous generators to inverter-based resources (IBRs) such as solar photovoltaics, wind turbines, and energy storage systems. This evolution leads to a reduction in system inertia, a critical attribute for maintaining frequency stability in response to disturbances. Consequently, the ability to monitor and estimate system inertia has become increasingly essential. This paper provides a comprehensive review of existing inertia estimation methodologies, analyzing them from multiple perspectives, including the types of data utilized, underlying estimation principles, operational modes, and system-wide applicability. A comparative summary table is included to distill commonalities and key characteristics across various studies. In addition, the paper examines practical implementations of inertia estimation across several major power systems worldwide, including the U.S. interconnections, the Nordic power system, and the U.K. grid. Key challenges are identified, particularly in estimating contributions from virtual inertia sources and load-induced inertia in increasingly converter-dominated networks. To address these emerging challenges, the paper proposes an integrated framework for real-time inertia estimation and monitoring. This framework encompasses critical components such as data acquisition, inertia estimation from both synchronous and non-synchronous sources, load-induced effects, optimization techniques, forecasting, and virtual inertia scheduling. Collectively, these elements enable dynamic, system-wide monitoring and adaptive control of grid inertia.
Inertia estimation↗
Expert system terms and building tools
The characteristics of an Expert System Building Tool (ESBT) capable of inference and hypothetical reasoning are discussed. The inference capabilities of ESBTs allow such functions as classification, design-synthesis, forecasting, decision-aiding, scheduling and planning, real-time monitoring, situation assessment, the discovery of novel relations, and debugging. ESBTs are noted to have made possible order-of-magnitude improvements in expert system construction. Higher-end ESBTs are moving from LISP machines to less expensive workstations, and lower-end ones are appearing on PCs.
Gevarter, William B.↗
NASA Annual HSR Workshop: Boeing HSCT program summary
Boeing's view of the High Speed Civil Transport (HSCT) is given in viewgraph format. Boeing believes that the forecast for long range scheduled international traffic is sufficiently large in the post year 2005 time period to support a fleet of HSCT's. Technologies are projected to be available to create an HSCT that will have the required performance and operating economics, and which can be sold at a price that will provide a reasonable return to Boeing and the airlines. With relatively modest surcharges over competing subsonic fares, it is expected that an HSCT providing roughly a 50 percent time savings would capture a significant market share.
Henderson, Michael L.↗
Project Assessment Framework through Design (PAFTD) - A Project Assessment Framework in Support of Strategic Decision Making
Research and development organizations that push the innovation edge of technology frequently encounter challenges when attempting to identify an investment strategy and to accurately forecast the cost and schedule performance of selected projects. Fast moving and complex environments require managers to quickly analyze and diagnose the value of returns on investment versus allocated resources. Our Project Assessment Framework through Design (PAFTD) tool facilitates decision making for NASA senior leadership to enable more strategic and consistent technology development investment analysis, beginning at implementation and continuing through the project life cycle. The framework takes an integrated approach by leveraging design principles of useability, feasibility, and viability and aligns them with methods employed by NASA's Independent Program Assessment Office for project performance assessment. The need exists to periodically revisit the justification and prioritization of technology development investments as changes occur over project life cycles. The framework informs management rapidly and comprehensively about diagnosed internal and external root causes of project performance.
Depenbrock, Brett T.↗
Benefit Opportunities for Integrated Surface and Airspace Departure Scheduling: A Study of Operations at Charlotte-Douglas International Airport
NASA is collaborating with the FAA and aviation industry to develop and demonstrate new capabilities that integrate arrival, departure, and surface air-traffic operations. The concept relies on trajectory-based departure scheduling and collaborative decision making to reduce delays and uncertainties in taxi and climb operations. The paper describes the concept and benefit mechanisms aimed at improving flight efficiency and predictability while maintaining or improving operational throughput. The potential impact of the technology is studied and discussed through a quantitative analysis of relevant shortfalls at the site identified for initial deployment and demonstration in 2017: Charlotte-Douglas International Airport. Results from trajectory analysis indicate substantial opportunity to reduce taxi delays for both departures and arrivals by metering departures at the gate in a manner that maximizes throughput while adhering to takeoff restrictions due mostly to airspace constraints. Substantial taxi-out delay reduction is shown for flights subject to departure restrictions stemming from traffic flow management initiatives. Opportunities to improve the predictability of taxi, takeoff, and climb operations are examined and their potential impact on airline scheduling decisions and air-traffic forecasting is discussed. In addition, the potential to improve throughput with departure scheduling that maximizes use of available runway and airspace capacity is analyzed.
air traffic management↗
Software for Allocating Resources in the Deep Space Network
TIGRAS 2.0 is a computer program designed to satisfy a need for improved means for analyzing the tracking demands of interplanetary space-flight missions upon the set of ground antenna resources of the Deep Space Network (DSN) and for allocating those resources. Written in Microsoft Visual C++, TIGRAS 2.0 provides a single rich graphical analysis environment for use by diverse DSN personnel, by connecting to various data sources (relational databases or files) based on the stages of the analyses being performed. Notable among the algorithms implemented by TIGRAS 2.0 are a DSN antenna-load-forecasting algorithm and a conflict-aware DSN schedule-generating algorithm. Computers running TIGRAS 2.0 can also be connected using SOAP/XML to a Web services server that provides analysis services via the World Wide Web. TIGRAS 2.0 supports multiple windows and multiple panes in each window for users to view and use information, all in the same environment, to eliminate repeated switching among various application programs and Web pages. TIGRAS 2.0 enables the use of multiple windows for various requirements, trajectory-based time intervals during which spacecraft are viewable, ground resources, forecasts, and schedules. Each window includes a time navigation pane, a selection pane, a graphical display pane, a list pane, and a statistics pane.
Wang, Yeou-Fang↗
FFTSF: Revisiting Sub-Seasonal Streamflow Forecasting with Simple Feedforward Network
Accurate short-to-subseasonal streamflow forecasts are vital for water management, including flood preparedness, drought mitigation, hydropower scheduling, and ecosystem protection. However, extending a forecast beyond a few days remains challenging due to complexity of hydrological processes. While recent self-attention based transformer architectures such as iTransformer have gained traction in time-series forecasting, these models suffer from several critical limitations: (1) significant computational overhead that scales quadratically with sequence length, (2) vulnerability to overfitting on limited hydrological datasets, (3) degraded performance on long-horizon forecasts due to attention decay, and (4) excessive architectural complexity that hampers interpretability and operational deployment. In this study, we propose a simple Feedforward Time Series Forecasting (FFTSF) network that directly addresses these limitations through its lightweight architecture and long-range forecasting capabilities. We evaluate FFTSF across 178 USGS stream gauges spanning diverse climate regimes by forecasting lead times of 1-, 7-, 14-, and 30-days. Our results demonstrate that FFTSF achieves competitive performance at short lead times (NSE of 0.778 for 1-day forecasts) while substantially outperforming complex baselines at longer forecast period, achieving the highest NSE (0.271) at 30-day forecasts with greater robustness and stability. For 30-day forecasts, FFTSF achieves a 71% improvement over NLinear, 57% improvement over DLinear and 12% improvement over the computationally intensive iTransformer while requiring fewer computational resources. Our findings reveal that architectural complexity is not necessary for hydrological forecasting, demonstrating that well-designed simple models can outperform attention mechanisms for subseasonal streamflow forecasting. The computational efficiency and consistent long-range performance of FFTSF make it suitable for water management applications where reliable extended forecasts are essential.
Krishnan Kutty Ambika, Anukesh [ORNL] (ORCID:00000↗
Operational Probabilistic Tools for Solar Uncertainty (OPTSUN) (Final Project Report for DOE Solar Forecasting II Project)
Increasing levels of solar PV can challenge system operations and may require novel methods to operate the power system reliably and efficiently. Power system operating plans generally use deterministic forecasts, in which the variable energy resources are represented by the expected value for each interval of the decision horizon. Probabilistic forecasts are relatively new but have the potential to address the shortfalls of deterministic forecasts. However, understanding how best to use such forecasts is still a key gap in industry and was the focus of this project. The project had three workstreams. In a forecasting workstream, improvements were made to baseline probabilistic forecasts using a number of new approaches such as machine learning methods and improved input data. In a design workstream, advanced simulation tools used these forecasts to investigate newly proposed reserve determination methods. Lastly, in a demonstration workstream a scheduling management platform (SMP) was developed to leverage probabilistic forecasts in a modular and customizable manner. In order to study the benefits that could be accrued, the project team collaborated with three utility partners (Duke Energy, Southern Company and Hawaiian Electric) to deliver improved probabilistic forecasts for each region and to model each region in case studies using advanced production cost modeling tools. Different methods to determine operating reserve requirements from probabilistic forecasts were developed, simulated, and tested across each region. The benefits of using these newly proposed methods varied by utility, but, in general, using probabilistic forecasts as well as historical data to set the reserve requirements seems to improve reliability related results, with less risk of reserve or supply shortfalls. The cost implications were not always straightforward; in some cases the new methods could show a reduction in expected operating costs, but often the increase in reserves associated with better risk mitigation using probabilistic forecasts could result in an increase in operating costs in the simulations. The SMP tool was developed to process probabilistic forecasts from their initial receipt through to scheduling decisions. This open-source tool consists of several modules for scenario development, reserve requirements calculation, and visualization. The SMP tool was demonstrated to a wide range of operators and stakeholders at all three utilities and further improved based on their feedback. The tool will be available on www.epri.com/optsun. The proposed probabilistic information-based reserve determination approaches have the potential to be implemented by different regions to ensure an economic and reliable power system operation on power systems integrating increasing levels of variable renewable resources. The innovative yet practical methods developed in this project demonstrated tangible benefits from using probabilistic forecasts beyond just study-based assessments to include three unique balancing areas. The demonstrated benefits across the multiple utility environments, are expected to provide system operators in all regions the confidence required and a platform to adopt the new forecasting and operating methods.
14 SOLAR ENERGY↗