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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Quantifying and simulating the weather forecast uncertainty for advanced building control

Weather forecast uncertainty is unavoidable despite technological advancements. Accurately quantifying and modelling this uncertainty is essential for developing and comparing advanced building controllers. In this study, we present a structured approach using a first-order autoregressive model (AR(1)) to model uncertainty in ambient temperature and global solar irradiation (GHI) forecasts. We analyzed weather data from four cities and employed Jensen–Shannon divergence (JSD) to evaluate the similarity between synthetic and actual forecast errors. The average JSD values for temperature are 0.027 (Berkeley), 0.021 (Leuven), 0.018 (Berlin), and 0.008 (Oslo), and for GHI, the average JSD values are 0.016 (Berkeley), 0.058 (Leuven), and 0.013 (Berlin). The low JSD values indicate a high similarity between the synthetic and real forecast error distributions. Further, our approach successfully generates synthetic weather forecasts that mirror the statistical properties of actual forecasts. The implementation of our method for uncertain forecast generation is being added to the BOPTEST framework.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Flexibility Requirements for Energy Systems with Renewable Generation under Forecast Uncertainties

Energy systems with high fractions of renewable energy-based resources require adequate assets providing flexibility in electricity usage to maximize the benefits of renewable energy. In this paper, we provide an analytical approach to estimate the flexibility requirements of such energy systems, with forecast uncertainties in both demand and generation. Our analytical results show that even with forecast errors, the expected system operating cost decreases with an increase in the amount of flexibility capacity -- however, there is an inflection point, beyond which addition of further flexibility capacity does not reduce expected system cost any further. Additionally, an enumeration-based approach is presented to estimate the maximum flexibility capacity needed to optimize the operating cost. Numerical experiments conducted on a network-abstracted modified IEEE 30-bus system are used for empirical validation and gaining additional insights on the effect of prosumers' willingness to offer flexibility on the dispatch performance.

Bhattacharya, Saptarshi↗
DOE OSTI2023

Multi-layered Energy Management Framework for Extreme Fast Charging Stations Considering Demand Charges, Battery Degradation, and Forecast Uncertainties

To achieve a cost-effective and expeditious charging experience for extreme fast charging station (XFCS) owners and electric vehicle (EV) users, the optimal operation of XFCS is crucial. It is however challenging to simultaneously manage the profit from energy arbitrage, the cost of demand charges, and the degradation of a battery energy storage system (BESS) under uncertainties. This paper, therefore, proposes a multi-layered multi-time scale energy flow management framework for an XFCS by considering long- and short-term forecast uncertainties, monthly demand charges reduction, and BESS life degradation. In the proposed approach, an upper scheduling layer (USL) ensures the overall operation economy and yields optimal scheduling of the energy resources on a rolling horizon basis, thereby considering the long-term forecast errors. A lower dispatch layer (LDL) takes the short-term forecast errors into account during the real-time operation of the XFCS. Per the latest research, monthly demand charges can be as high as 90% of the total monthly bills for EV fast charging stations; to this end, this paper takes the first attempt at the reduction of demand charges cost by considering the trade-off between the energy cost and monthly demand charges. Contrasting literature, this work allocates an energy reserve in the BESS stored energy to deal with the impact of short-term forecast errors on the optimized real-time operation of the XFCS. Moreover, degradation modeling considers the trade-off between short-term benefits and long-term BESS life degradation. As a result, case studies and a comparative analysis prove the efficacy of the proposed framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Multi-Factor-Coupled, Ahead-of-Time Aggregation of Power Flexibility Under Forecast Uncertainty

The increasing penetration of distributed energy resources (DERs) is significantly reshaping the role of distribution systems under active energy management. To aggregate the active-reactive power flexibility of DERs dispersed at the feeder and provide capacity support to the transmission system, it is essential to efficiently identify feasible substation power injection trajectories. This paper introduces a novel ahead-of-time flexibility characterization method to address it. First, a polyhedral non-feeder-level power flexibility region (PFR) is constructed, accounting for various time-dependent, power-coupled, and forecast error uncertainties. Then, a polyhedral feeder-level PFR is analytically derived through a coordinate transformation, which can reveal the uncertainty propagation path, i.e., how uncertainty applies to the feeder-level PFR. To facilitate the high-level application, a tractable chance-constrained Chebyshev centering optimization model is further developed to find a ball-shaped inner approximation of the feeder-level PFR. Finally, the proposed method is validated on a modified IEEE 123-bus test system. Here, both theoretical and experimental results show that, with appropriate robustness parameter settings, the proposed method can make the approximated PFR less conservative with abundant robustness against forecast error uncertainty.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A shared-mobility-based framework for evacuation planning and operations under forecast uncertainty

To meet evacuation needs from carless populations who need personalized assistance to evacuate safely, in this article we propose a ridesharing-based evacuation program that recruits volunteer drivers before a disaster strikes, and then matches volunteer drivers with evacuees once demand is realized. Here we optimize resource planning and evacuation operations under uncertain spatiotemporal demand, and construct a two-stage stochastic mixed-integer program to ensure high demand fulfillment rates. We consider three formulations to improve the number of evacuees served, by minimizing an expected penalty cost, imposing a probabilistic constraint, and enforcing a constraint on the conditional value at risk of the total number of unserved evacuees, respectively. We discuss the benefits and disadvantages of the different risk measures used in the three formulations, given certain carless population sizes and the variety of evacuation modes available. We also develop a heuristic approach to provide quick, dynamic and conservative solutions. We demonstrate the performance of our approaches using five different networks of varying sizes based on regions of Charleston County, South Carolina, an area that experienced a mandatory evacuation order during Hurricane Florence, and utilize real demographic data and hourly traffic count data to estimate the demand distribution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A Novel Modeling Framework for Computationally Efficient and Accurate Real-Time Ensemble Flood Forecasting With Uncertainty Quantification

A novel modeling framework that simultaneously improves accuracy, predictability, and computational efficiency is presented. It embraces the benefits of three modeling techniques integrated together for the first time: surrogate modeling, parameter inference, and data assimilation. The use of polynomial chaos expansion (PCE) surrogates significantly decreases computational time. Parameter inference allows for model faster convergence, reduced uncertainty, and superior accuracy of simulated results. Ensemble Kalman filters assimilate errors that occur during forecasting. To examine the applicability and effectiveness of the integrated framework, we developed 18 approaches according to how surrogate models are constructed, what type of parameter distributions are used as model inputs, and whether model parameters are updated during the data assimilation procedure. We conclude that (1) PCE must be built over various forcing and flow conditions, and in contrast to previous studies, it does not need to be rebuilt at each time step; (2) model parameter specification that relies on constrained, posterior information of parameters (so-called Selected specification) can significantly improve forecasting performance and reduce uncertainty bounds compared to Random specification using prior information of parameters; and (3) no substantial differences in results exist between single and dual ensemble Kalman filters, but the latter better simulates flood peaks. The use of PCE effectively compensates for the computational load added by the parameter inference and data assimilation (up to ~80 times faster). Therefore, the presented approach contributes to a shift in modeling paradigm arguing that complex, high-fidelity hydrologic and hydraulic models should be increasingly adopted for real-time and ensemble flood forecasting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Impacts of Dispatch Strategies and Forecast Errors on the Economics of Behind-the-Meter PV-Battery Systems: Preprint

To assess the economic value of batteries in hybrid PV-battery systems, one must create a dispatch profile for the battery. Many analyses of battery value assume perfect forecasts of PV generation and load, determining an upper limit on the value of the battery. Prior work that accounts for forecast uncertainty often does so in the context of a single dispatch algorithm, which does not provide a baseline for comparison. Furthermore, when multiple dispatch algorithms are assessed with uncertainty, the benefits considered are for diesel generation in a microgrid, not retail rate savings. This work addresses the gaps in the literature by comparing the performance of both heuristic and optimal dispatch algorithms for retail rate savings under forecast uncertainty, and provides comparisons of the robustness of these algorithms and their associated estimates of economic value. We find that using a perfect forecast can overestimate the value of hybrid PV-battery systems between 1% and 8% compared to the reality of using a day-ahead forecast, depending on the dispatch algorithm used. Thus, accounting for forecast uncertainty in system design and analysis will significantly improve the accuracy of modeled system values.

batteries↗
DOE OSTI2023

Impacts of Dispatch Strategies and Forecast Errors on the Economics of Behind-the-Meter PV-Battery Systems

To assess the economic value of batteries in hybrid PV-battery systems, one must create a dispatch profile for the battery. Many analyses of battery value assume perfect forecasts of PV generation and load, determining an upper limit on the value of the battery. Prior work that accounts for forecast uncertainty often does so in the context of a single dispatch algorithm, which does not provide a baseline for comparison. Furthermore, when multiple dispatch algorithms are assessed with uncertainty, the benefits considered are for diesel generation in a microgrid, not retail rate savings. This work addresses the gaps in the literature by comparing the performance of both heuristic and optimal dispatch algorithms for retail rate savings under forecast uncertainty, and provides comparisons of the robustness of these algorithms and their associated estimates of economic value. We find that using a perfect forecast can overestimate the value of hybrid PV-battery systems between 1% and 8% compared to the reality of using a day-ahead forecast, depending on the dispatch algorithm used. Thus, accounting for forecast uncertainty in system design and analysis will significantly improve the accuracy of modeled system values.

batteries↗
DOE OSTI2020

Building thermal load prediction through shallow machine learning and deep learning

Building thermal load prediction informs the optimization of cooling plant and thermal energy storage. Physics-based prediction models of building thermal load are constrained by the model and input complexity. In this study, we developed 12 data-driven models (7 shallow learning, 2 deep learning, and 3 heuristic methods) to predict building thermal load and compared shallow machine learning and deep learning. The 12 prediction models were compared with the measured cooling demand. It was found XGBoost (Extreme Gradient Boost) and LSTM (Long Short Term Memory) provided the most accurate load prediction in the shallow and deep learning category, and both outperformed the best baseline model, which uses the previous day's data for prediction. Then, we discussed how the prediction horizon and input uncertainty would influence the load prediction accuracy. Major conclusions are twofold: first, LSTM performs well in short-term prediction (1 h ahead) but not in long term prediction (24 h ahead), because the sequential information becomes less relevant and accordingly not so useful when the prediction horizon is long. Second, the presence of weather forecast uncertainty deteriorates XGBoost's accuracy and favors LSTM, because the sequential information makes the model more robust to input uncertainty. Training the model with the uncertain rather than accurate weather data could enhance the model's robustness. Finally, our findings have two implications for practice. First, LSTM is recommended for short-term load prediction given that weather forecast uncertainty is unavoidable. Second, XGBoost is recommended for long term prediction, and the model should be trained with the presence of input uncertainty.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Evaluation of an open forecasting challenge to assess skill of West Nile virus neuroinvasive disease prediction

Abstract Background West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement. Methods We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill. Results Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill. Conclusions Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases). Graphical Abstract

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Probabilistic Zonal Reserve Requirements for Improved Energy Deliverability with Wind Power

In power systems with high penetration of renewable energy resources, uncertainty and variability of these stochastic resources introduce additional challenges for the operation of power systems. To improve the power system's reliability in the face of uncertainty, reserves are required as additional generation capacity to rebalance the power system following random disturbances. However, reserve deliverability is not guaranteed, because it may encounter potential transmission line congestion. Zonal reserve requirements can address this issue, but operators lack efficient ways to allocate reserves to zones while accounting for wind power forecast uncertainty. Herein we propose a methodology for probabilistic zonal reserve requirements to address wind power forecast uncertainties. This method estimates the probability distribution of line flows based on the system generation margin and injection shift factor. This estimate is then used to construct pre-defined and post-zonal reserve requirements. Case studies demonstrate that the proposed method efficiently schedules energy and reserves to balance energy and manage deliverability with wind power forecast uncertainty.We also discuss operational implications of the proposed method.

17 WIND ENERGY↗
DOE OSTI2022

The WRF-Solar Ensemble Prediction System: Development, Test, and Validation

Providing reliable probabilistic solar radiation information is needed to improve management of the uncertainty and variability of solar generation. Thus, guidance on how to develop skillful and accurate ensemble forecasts is essential and it will ultimately contribute to integration of high amounts of solar energy on the grid. A team from the National Renewable Energy Laboratory and the National Center for Atmospheric Research had been collaborating to develop the WRF-Solar ensemble prediction system (WRF-Solar EPS) in the past three years to produce probabilistic solar irradiance forecasts and better predict solar energy by quantifying forecast uncertainty. The WRF-Solar EPS basically generates ensemble members for solar irradiance based on stochastic perturbations to provide intraday and day-ahead probabilistic forecasts. This study will present main research steps in developing the WRF-Solar EPS including: (a) tangent linear analysis for identifying key input variables of six WRF-Solar modules significantly related to predicting of cloud and solar irradiance, (b) combining stochastic perturbation technique with the WRF-Solar model, and (c) ensemble calibration method to decrease error and uncertainty of ensemble-based solar forecasts. The capability of WRF-Solar EPS is now updated to the most recent version of standard WRF model. This presentation will summarize comprehensive results from the evaluation of forecasts against the National Solar Radiation Data Base as well as ground-measured observations. Moreover, we will introduce the user's guide for WRF-Solar EPS (e.g., parameters to configure stochastic perturbations) and future extension of this research.

day-ahead forecast↗
DOE OSTI2022

Probabilistic Cloud Optimized Day-Ahead Forecasting System Based on WRF-Solar (Final Report)

The most persistent challenge in both intraday and day-ahead solar forecasting is to get numerical weather prediction models to produce the right type of clouds with the right frequency at the right time and place. Another challenge is to understand and communicate the forecast uncertainty. The objective of this project was to develop an optimized ensemble-based solar irradiance forecasting system that will (1) demonstrably improve the current state-of-the-art solar forecasts from the deterministic Weather Research and Forecasting-Solar (WRF-Solar) model and (2) provide probabilistic forecasts for grid operations. This probabilistic solar forecasting system, referred to as the WRF-Solar Ensemble Prediction System (WRF-Solar EPS), aims to significantly enhance both the intraday and the day-ahead solar forecasting capability for grid operations. This technical report summarizes the work performed in the past 3 years through a collaboration between the National Renewable Energy Laboratory and the National Center for Atmospheric Research as part of the U.S. Department of Energy's Solar Forecasting 2 program that aims to improve the accuracy of solar energy forecasts and enable increased deployment of solar energy on the electric grid.

14 SOLAR ENERGY↗
DOE OSTI2025

Yesterday’s extremes, today’s new normal: flood risk in the Kathmandu Valley, Nepal

Unplanned urban growth has left many cities increasingly vulnerable to extreme rainfall events, particularly in regions with inadequate drainage infrastructures and development encroaching on natural floodplains. Here, in this perspective paper, we examine the September 2024 floods that struck Central Nepal, triggered by a persistent low-pressure system and enhanced by converging moisture flows from the Arabian Sea and the Bay of Bengal which led to widespread catastrophic damage. In the Kathmandu Valley, floodwaters expanded to more than 2.5 times the bankfull water extent, causing significant damage to housing, transportation network, and critical infrastructure, displacing thousands of residents, and severely disrupting urban services. This event highlights the urgent need for improved flood management strategies that integrate both structural and non-structural measures into the infrastructure development. While early warning systems provided critical lead time, challenges remain in reducing forecasting uncertainties and improving communication across government agencies and with local communities. A forward-looking approach is essential, including probabilistic flood forecasting systems, sustainable floodplain management, risk-sensitive land use planning, climate- and disaster- resilient infrastructure development, and the integration of nature-based solutions like urban green and blue spaces to mitigate flood impacts. By involving local communities in planning and preparedness efforts, particularly through citizen science initiatives, and engagement with underserved and disadvantaged communities, Nepal can better adapt to the growing risks posed by extreme rainfall and urban flooding and enhance long-term disaster resilience in rapidly urbanizing areas like Kathmandu Valley.

Kathmandu Valley↗
DOE OSTI2023

Incorporating Operational Uncertainties into the Dispatch of an Integrated Solar and Storage System

The economic assessment of hybrid energy systems (HES) pairing battery energy storage systems (BESSs) and photovoltaics (PV) is highly important for advancing their deployment in power systems. This paper presents an innovative assessment framework, including an optimal control policy for dispatch under uncertainty and procedures for exploring control parameters that maximize economic benefits. The proposed dispatch policy consists of two steps using system forecast information. The first step is to determine whether a BESS will be used within an operational scheduling time frame based on the probability of events and their thresholds. Once the dispatch of BESS is triggered, a model predictive control (MPC) is carried out in the second step for scheduling using the expected value of system information. By exercising this policy with different thresholds, one can explore the trade-offs between short-term benefits and battery lifetime, and identify an optimal threshold that maximizes the total economic benefits within the battery lifetime. An evaluation study in a real-world HES project is presented to illustrate the proposed framework. Compared with traditional optimal dispatch algorithms, the proposed method can significantly improve the economic benefits of an HES scheduled under forecast uncertainties.

Ma, Xu↗
DOE OSTI2026

AI/ML-Enhanced Wind Forecasts for Reducing Uncertainty in Prescribed Fire Planning

Prescribed fire is a vital tool for ecosystem management and wildfire risk reduction but its escalation is constrained by overly conservative burn windows because of uncertainties, for instance, in wind forecasts. This review describes the state of the art in weather product use by fire/smoke models and identifies three priority research gaps that artificial intelligence/machine learning (AI/ML) is well positioned to address: (1) spatial and temporal downscaling to meter-scale, sub-hourly wind fields; (2) bias correction for systematic model errors in complex terrain; and (3) robust uncertainty quantification to inform ensemble-based simulations. Emerging AI/ML techniques offer promising frameworks to address all three challenges. By providing high-resolution, bias-corrected, and probabilistic wind fields, AI/ML-enhanced forecasts will allow for expanded burn windows, improved ignition strategy design and a reduced reliance on expert intuition, especially when a prescribed fire is introduced into new areas.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Enhancing Solar Power Forecasting with Regularized Constrained Quantile Regression Averaging and Bootstrapping Techniques

Probabilistic solar power forecasting (SPF) plays an essential role in optimizing power-grid operations by quantifying the forecast uncertainty. To improve the accuracy and robustness of probabilistic SPF, this paper introduces the regularized constrained quantile regression averaging (rCQRA) method to combine outputs from multiple PSPF models. In addition, a bootstrapping method was used to quantify model uncertainty, providing insights into the reliability and significance of each ensemble component. To evaluate its efficacy, the proposed rCQRA method is used to integrate four PSPF methods. The resulting SPF models are trained and validated using a real-world six-year dataset from a rooftop solar plant in the USA. The performance of the proposed rCQRA method is evaluated and compared with two benchmark methods under three categories of weather conditions. It is shown that the rCQRA method has superior performance in its forecast reliability, sharpness, and accuracy.

Ensemble learning, probabilistic solar power forec↗