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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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At least 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A Roadmap for a Lightning Modeling Grand Challenge

This document is a roadmap for building an interconnected model of the physical processes that produce a lightning discharge, and its observable optical and radio signals. We call this a Lightning Modeling Grand Challenge, recognizing that significant effort and coordination of human and financial resources is required to realize the capability. The roadmap serves to outline the coordination of resources necessary to enable stitching together existing knowledge and model components to make a lightning prediction, and to test these predictions with observations. Such a capability does not currently exist. The roadmap is motivated not only by a spirit of scientific inquiry, but by practical challenges faced by US Federal and societal stakeholders. Advancements in lightning observations have outpaced our tests of integrated understanding, leaving many stakeholders unsure how to design their missions to properly detect and discriminate lightning, and unsure how to apply the sometimes-disagreeing lightning signals from diverse instruments. The time is right to connect existing theories and models to support stakeholders in understanding the signals they observe, for needs as diverse as climate monitoring, national security, weather forecasting, public safety, and protection of natural and built environments. The roadmap’s two main technical sections describe the components of a linked physical model, followed by a description of models of lightning signals and sensors that are driven by outputs from the physical model. The goal is to predict the time-varying physical properties of lightning that are self-consistent with the thunderstorm’s structure and dynamics. These lightning signals then propagate through the storm, with realistic dispersion and attenuation, to receivers on the ground or in space. At a high level, the model begins with weather (cloud) model output, including explicit prediction of the electrification of cloud particles. The cloud’s electrical structure drives a model of lightning physics, from initiation, through channel development, and discharges along those channels. Key lightning parameters, such as the temperature and currents in the channel, and their space and time distribution, are then used to produce optical and electromagnetic signal sources that propagate to modeled receivers. This architecture therefore generates a dataset suitable for comparison to existing and envisioned observing systems. The need for additional measurements and field campaigns to support model development is described. In each model sub-component, inputs, outputs, uncertainties, evaluation methods, and next steps are summarized, interleaved with references to the scientific literature. Identifying boundaries between the model sub-components aids in segmenting an integrated, complex model into practical work packages and system sub-components, allowing a diverse team to contribute and maintain the system. We estimate that at least five years of effort and a $\$$10M initial investment is necessary to make a significant step forward. Mechanisms to facilitate community coordination, including annual workshops and open-source code repositories, are described.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1991

A wall interference assessment/correction system

The Hackett method (a Wall Pressure Signature Method) was selected to be adapted for the 12 ft Wind Tunnel WIAC system. This method uses limited measurements of the static pressure at the wall, in conjunction with the solid wall boundary condition, to determine the strength and distribution of singularities representing the test article. The singularities are used in term for estimating wall interference at the model location. Hackett's method will have to be formulated for application to the unique geometry of the 12 ft tunnel. The WIAC code will be validated by conducting numerically simulated experiments rather than actual wind tunnel experiments. The simulations will be used to generate both free air and confined wind tunnel flow fields for each of the test articles over a range of test configurations. Specifically the pressure signature at the test section wall will be computed for the confined case to provide the simulated 'measured' data. These data will serve as the input for the WIAC method. The performance of the WIAC method then may be evaluated by comparing the corrected parameters with those for the free air simulation.

Lo, C. F.↗
NASA NTRS1986

Magnetostrophic balance in planetary dynamos - Predictions for Neptune's magnetosphere

With the purpose of estimating Neptune's magnetic field and its implications for nonthermal Neptune radio emissions, a new scaling law for planetary magnetic fields was developed in terms of externally observable parameters (the planet's mean density, radius, mass, rotation rate, and internal heat source luminosity). From a comparison of theory and observations by Voyager it was concluded that planetary dynamos are two-state systems with either zero intrinsic magnetic field (for planets with low internal heat source) or (for planets with the internal heat source sufficiently strong to drive convection) a magnetic field near the upper bound determined from magnetostrophic balance. It is noted that mass loading of the Neptune magnetosphere by Triton may play an important role in the generation of nonthermal radio emissions.

Curtis, S. A.↗
NASA NTRS1973

On Lidar Sounding of the Atmosphere to Estimate Static and Dynamic Characteristics of Aerosol Inhomogeneities

1. A possible application of intensity fluctuations of a pulse light signal reflected by atmospheric aerosols is analyzed by the correlation method to evaluate static (medium sizes, shape) and dynamic (speed and direction of movement, lifetime) characteristics of aerosol inhomogeneities. The aerosol inhomogeneities are assumed to be expanded, pressed, disintegrated and originated constantly in accordance with random laws, the set of inhomogeneities as a whole traveling together with air masses and having predominant movement in wind direction. It is shown that the characteristics of aerosol inhomogeneities considered can be expressed by the coefficients of the correlation function expansion of the reflected signal fluctuation intensity in Tailor series. 2/ Correlation systems for evaluating static and dynamic characteristics of driving objects can be divided into two types according to the kind and quantity of used information: the systems with coordinates of the information removal "points" to be fixed in space, and the systems with a parallel simultaneous information removal at discrete moments of time. The systems for determination of wind direction considered in are the examples of the first type system. However, the operating information removal for two points is insufficient to estimate completely static and dynamic characteristics of inhomogenities, their quantity ought to be increased up to three of them for two-dimensional problem and up to four of them for three-dimensional problem as it is usually done in the ionospheric studies. The second type systems are used for the investigation of a medium shape and speed of the clouds according to photographs made from satellites. These systems are also used for solution of navigation problems. The use of optical quantum generators with a scanning beam is seen to increase greatly the working information removal in comparison with the first type systems. Nevertheless, scanning rate is not sufficient sometimes in order· to consider a general picture of aerosol inhomogeneities to be stationary. In this connection the use of the systems of second type treatment becomes a matter of essential difficulty. 3. Aerosol inhomogeneities simulation has been carried out on the basis of the digital computer experiments with the aim of estimating static and dynamic characteristics of inhomogeneities by an optical beam in the atmosphere at different scanning procedures. The dependence of determination accuracy of these characteristics on the type of chosen laws of aerosol particle distribution in the atmosphere, the parameters of inhomogeneities geometry, their speed and the law of scanning have been obtained.

Zuev, V. E.↗
NASA NTRS1989

1960 Chile - New estimate of polar motion excitation

A recent reanalysis of the International Latitude Serivce (ILS) polar motion data-day has been processed using Kalman filtering techniques to generate the polar motion excitation function over the time-span from 1960 to 1965. The resulting excitation function has been examined for the effects of 1960 Chile in an attempt to determine experimentally how large earthquake affect polar motion. The resulting upper bound of about 75 x 10 to the 22nd N-m for a 10-deg dip (about 36 x 10 to the 22nd N-m for 20-deg dip) is consistent with results obtained from previous seismic studies, including a recent normal mode excitation result. Following future great earthquakes, monitoring of polar motion by space-based techniques such as VLBI should continue at high temporal resolution for several weeks in order to directly measure the rheological parameters of the upper mantle.

Slade, M. A.↗
DOE OSTI2023

Next Generation Fiber-Encapsulated Nanoscale Hybrid Materials for Direct Air Capture with Selective Water Rejection

Recently, NOHMs sorbents have been demonstrated to be highly stable and tunable for CO2 capture and have negligible vapor pressure, but their main drawbacks are high viscosity and high gas transfer limitation in their neat bulk phase. This project explored gas-assisted electrospinning processing coupled with promising encapsulation matrices such as Polymers with Intrinsic Microporosity (PIM-1) and polyacrylonitrile (PAN)/organopolysilizane (OPSZ) polymer/ceramic hybrid materials, which were demonstrated to be effective novel carriers for the active NOHMs sorbents. These encapsulation materials were evaluated by their thermal stability, hydrophobicity, permeability, and CO2 selectivity. The manufacturing parameters such as component composition and processing solvent were linked to bulk sorbent properties, e.g., surface area, pore volume, fiber thickness, capture capacity, capture kinetics, etc. These investigations resulted in fundamental knowledge and technical know-how that will be useful for scaling up the nanofiber mat contactor design. The encapsulation materials and design has been shown to not only increase the thermal oxidative stability of the NOHMs, but also yield significantly reduced pressure drop and increased performance of large modular air filters. 70 PAN: 30 OPSZ/NIPEI fibers exhibited the highest capture performance with 1.5 mmol CO2/g∙h kinetics the first hour under 100% CO2 condition, followed by 80 PAN: 20 OPSZ/NIPEI and 90 PAN: 10 OPSZ/NIPEI, while 90 PAN: 10 OPSZ/NIPEI fibers performed the best with 0.25 mmol CO2/g∙h of the initial kinetics under 400 ppm CO2 concentration. All the fabricated fibers demonstrated stable capture performances up to 15 cycles of adsorption and desorption. Using encapsulated NOHMs, the study found that the microwave could help regenerate the capture materials within 20 minutes without chemically degrading the materials. A high-level TEA/LCA study was performed to evaluate the feasibility of this technology at scale. These estimates show that the sorbent cost and environmental impact levels are comparable to other existing technologies. Overall, these findings increase the technological feasibility of using liquid-like, low vapor pressure NOHMs as a next generation sorbent for the direct air capture of CO2.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

ENSEMBLE KALMAN FILTERS FOR RELIABILITY ESTIMATION IN PERFUSION INFERENCE

We consider the solution of inverse problems in dynamic contrast–enhanced imaging by means of ensemble Kalman filters. Our quantity of interest is blood perfusion, i.e., blood flow rates in tissue. While existing approaches to compute blood perfusion parameters for given time series of radiological measurements mainly rely on deterministic, deconvolution–based methods, we aim at recovering probabilistic solution information for given noisy measurements. To this end, we model radiological image capturing as a sequential data assimilation process and solve it by an ensemble Kalman filter. Thereby, we recover deterministic results as an ensemble–based mean and are able to compute reliability information such as probabilities for the perfusion to be in a given range. Our target application is the inference of blood perfusion parameters in the human brain. A numerical study shows promising results for artificial measurements generated by a digital perfusion phantom.

Engineering↗
DOE OSTI2024

Physics informed neural network can retrieve rate and state friction parameters from acoustic monitoring of laboratory stick-slip experiments

Various machine learning (ML) and deep learning (DL) techniques have been recently applied to the forecasting of laboratory earthquakes from friction experiments. The magnitude and timing of shear failures in stick-slip cycles are predicted using features extracted from the recorded ultrasonic or acoustic emission (AE) signals. In addition, the Rate and State Friction (RSF) constitutive laws are extensively used to model the frictional behavior of faults. In this work, we use data from shear experiments coupled with passive acoustic (variance, kurtosis, and AE rate) interleaved with active source ultrasonic monitoring (transmitted wave amplitude) to develop physics-informed neural network (PINN) models incorporating the RSF law and AE rate generation equation with wave amplitude serving as a proxy for friction state variable. This PINN framework allows learning RSF parameters from stick-slip experiments rather than measuring them through a series of velocity step experiments. We observe that when the stick-slip cycles are irregular, the PINN models outperform the data-driven DL models. Transfer learning (TL) PINN models are also developed by pre-training on data collected at one normal stress level followed by forecasting shear failures and retrieving RSF parameters at other stress levels (i.e., with different recurrence intervals) after retraining on a limited amount of new data. Our findings suggest that TL models perform better compared to standalone models. Both standalone and TL PINN-estimated RSF parameters and their ground truth values show excellent agreements thus demonstrating that RSF parameters can be retrieved from laboratory stick-slip experiments using the corresponding acoustic data and that the transmitted wave amplitude provides a good representation of the evolving frictional state during stick-slips.

58 GEOSCIENCES↗
DOE OSTI2022

Error analysis of numerical methods for thick diffusive neutron transport problems on Shishkin mesh

A thin layer will develop at the boundary if the incoming angular flux is anisotropic in thick diffusive neutron transport problems. Solving such singularly perturbed problems, which have non-smooth solutions with singularity near the boundary, is computationally challenging. Standard finite difference schemes on a uniform mesh cannot yield ε-uniform convergence, where ε is a small parameter, while it can be achieved on a suitable piecewise-uniform Shishkin mesh. We present a formal error analysis of the diamond difference (DD) method and step difference (SD) method for solving the S{sub N} neutron transport equation. The analysis can be extended to other finite difference methods. Numerical results are presented to confirm the error estimates and the advantages of the Shishkin mesh. (author)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Simulating Dopant Diffusion in a Detalied Porous Structure

The National Ignition Facility (NIF) at Lawrence Livermore National Laboratory (LLNL) uses highpowered lasers to explore cutting edge fusion technology. Specialized optics, known as fiber lasers, generate and amplify the NIF’s high-powered lasers that facilitate fusion experiments. Fiber lasers are glass fibers that are doped with heavy elements to stimulate light emission. The fiber laser doping processing uses a predeposited layer of porous silica nanoparticles on the surface of the glass to absorb a dopant rich salt solution. Our project simulated the diffusion of dopants into porous silica with prescribed colloidal stacking geometries using COMSOL Multiphysics. With these simulations we identified the porosity and tortuosity of the porous nanoparticle structures as the primary parameters that will dictate how the dopants will diffusive into the porous surface of the glass. Porosity and Tortuosity are geometric parameters that can easily be input into more detailed simulations to estimate an effective diffusivity within the porous layer. Future work should account for surface reactions to accurately simulate the full-scale diffusion of the solution doping process.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Development of Carbon Flux Model Using ABI Data Over the Conterminous US

The satellite-driven carbon flux estimation has been playing important role to estimate continental-scale carbon budget. One of the biggest recent advances in the satellite-driven carbon flux modeling is utilization of high-frequent geostationary satellites to estimate diurnal cycle in carbon fluxes. The satellite based carbon flux estimation used the polar orbiting satellite sensors (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)), which allow us to observe target regions only once during the day. The new generation of geostationary satellite sensors provide frequent observations, often less than every 10 minutes. Here, we use GOES Advanced Baseline Imager (ABI) data to estimate hourly NEE over the conterminous US. We used the Terrestrial Observation Prediction System (TOPS) model for estimating hourly NEE. TOPS is a diagnostic ecosystem process model that simulates the fluxes of carbon and water through vegetation in response to climate variability. For the climate input, we developed hourly climate data using the same algorithm with NASA Earth Exchange Gridded Daily Meteorology (NEX-GDM) datasets based on machine learning techniques. The hourly climate data includes precipitation, maximum temperature, minimum temperature, dew point temperature, and solar radiation were derived from the Geostationary observations. The spatial patterns of ecosystem parameters used in TOPS are optimized using satellite Solar Induced Fluorescence (SIF) data. The high frequency GPP estimations from geostationary satellite sensors make it comparable to the instantaneous SIF data than daily GPP. We also used Ameriflux data for optimization of model parameters and the validation of the output. The derived data addresses the diurnal dynamics of carbon cycling at large scales and should help in reducing the uncertainties in carbon budget studies.

geostationary satellite↗
DOE OSTI2023

High-energy neutrino emission from magnetized jets of rapidly rotating protomagnetars

ABSTRACT Relativistic jets originating from protomagnetar central engines can lead to long duration gamma-ray bursts (GRBs) and are considered potential sources of ultra-high-energy cosmic rays and secondary neutrinos. We explore the propagation of such jets through a broad range of progenitors, from stars which have shed their envelopes to supergiants which have not. We use a semi-analytical spin-down model for the strongly magnetized and rapidly rotating protoneutron star (PNS) to investigate the role of central engine properties such as the surface dipole field strength, initial rotation period, and jet opening angle on the interactions and dynamical evolution of the jet-cocoon system. With this model, we determine the properties of the relativistic jet, the mildly relativistic cocoon, and the collimation shock in terms of system parameters such as the time-dependent jet luminosity, injection angle, and density profile of the stellar medium. We also analyse the criteria for a successful jet breakout, the maximum energy that can be deposited into the cocoon by the relativistic jet, and structural stability of the magnetized outflow relative to local instabilities. Lastly, we compute the high-energy neutrino emission as these magnetized outflows burrow through their progenitors. Precursor neutrinos from successful GRB jets are unlikely to be detected by IceCube, which is consistent with the results of previous works. On the other hand, we find that high-energy neutrinos may be produced for extended progenitors like blue and red supergiants, and we estimate the detectability of neutrinos with next generation detectors such as IceCube-Gen2.

Astronomy & Astrophysics↗
DOE OSTI2021

Deep Learning Based Approach to Integrate MyShake's Trigger Data with ShakeAlert for Faster and Robust EEW Alerts

Earthquake Early Warning (EEW) systems depend on the dense seismic network to make fast and accurate earthquake detections to issue useful early alerts. The recent development and implementation of the ShakeAlert system is relying on the west coast seismic network to detect and estimate the earthquake parameters in real-time. While working on making improvements on the current system, one potential approach is to include more seismic recordings from various sources, such as the low-cost smartphone seismic network, such as the MyShake network, or the Google Android smartphones, since these smartphone seismic networks have much more portable stations (phones) to potentially provide useful data to the system. This report summarizes the initial exploration of using a deep learning approach to combine the data from both traditional seismic stations and the smartphone data in California. Due to the heterogeneity nature of the data, we aggregate data from the phones (using simulation data), as well as that from traditional seismic stations to grid cells. By generating real-time triggering ratio grid cell maps, the designed deep learning algorithm can process the data from multiple sources and detect the earthquake faster than only using that from a traditional seismic network.

58 GEOSCIENCES↗
NASA NTRS2007

NASA Tech Briefs, December 2007

Topics include: Ka-Band TWT High-Efficiency Power Combiner for High-Rate Data Transmission; Reusable, Extensible High-Level Data-Distribution Concept; Processing Satellite Imagery To Detect Waste Tire Piles; Monitoring by Use of Clusters of Sensor-Data Vectors; Circuit and Method for Communication Over DC Power Line; Switched Band-Pass Filters for Adaptive Transceivers; Noncoherent DTTLs for Symbol Synchronization; High-Voltage Power Supply With Fast Rise and Fall Times; Waveguide Calibrator for Multi-Element Probe Calibration; Four-Way Ka-Band Power Combiner; Loss-of-Control-Inhibitor Systems for Aircraft; Improved Underwater Excitation-Emission Matrix Fluorometer; Metrology Camera System Using Two-Color Interferometry; Design and Fabrication of High-Efficiency CMOS/CCD Imagers; Foam Core Shielding for Spacecraft CHEM-Based Self-Deploying Planetary Storage Tanks Sequestration of Single-Walled Carbon Nanotubes in a Polymer PPC750 Performance Monitor Application-Program-Installer Builder Using Visual Odometry to Estimate Position and Attitude Design and Data Management System Simple, Script-Based Science Processing Archive Automated Rocket Propulsion Test Management Online Remote Sensing Interface Fusing Image Data for Calculating Position of an Object Implementation of a Point Algorithm for Real-Time Convex Optimization Handling Input and Output for COAMPS Modeling and Grid Generation of Iced Airfoils Automated Identification of Nucleotide Sequences Balloon Design Software Rocket Science 101 Interactive Educational Program Creep Forming of Carbon-Reinforced Ceramic-Matrix Composites Dog-Bone Horns for Piezoelectric Ultrasonic/Sonic Actuators Benchtop Detection of Proteins Recombinant Collagenlike Proteins Remote Sensing of Parasitic Nematodes in Plants Direct Coupling From WGM Resonator Disks to Photodetectors Using Digital Radiography To Image Liquid Nitrogen in Voids Multiple-Parameter, Low-False-Alarm Fire-Detection Systems Mosaic-Detector-Based Fluorescence Spectral Imager Plasmoid Thruster for High Specific-Impulse Propulsion Analysis Method for Quantifying Vehicle Design Goals Improved Tracking of Targets by Cameras on a Mars Rover Sample Caching Subsystem Multistage Passive Cooler for Spaceborne Instruments GVIPS Models and Software Stowable Energy-Absorbing Rocker-Bogie Suspensions

Source record↗
NASA NTRS2013

Automating Hyperspectral Data for Rapid Response in Volcanic Emergencies

In a volcanic emergency, time is of the essence. It is vital to quantify eruption parameters (thermal emission, effusion rate, location of activity) and distribute this information as quickly as possible to decision-makers in order to enable effective evaluation of eruption-related risk and hazard. The goal of this work was to automate and streamline processing of spacecraft hyperspectral data, automate product generation, and automate distribution of products. Visible and Short-Wave Infrared Images of volcanic eruption in Iceland in May 2010." class="caption" align="right">The software rapidly processes hyperspectral data, correcting for incident sunlight where necessary, and atmospheric transmission; detects thermally anomalous pixels; fits data with model black-body thermal emission spectra to determine radiant flux; calculates atmospheric convection thermal removal; and then calculates total heat loss. From these results, an estimation of effusion rate is made. Maps are generated of thermal emission and location (see figure). Products are posted online, and relevant parties notified. Effusion rate data are added to historical record and plotted to identify spikes in activity for persistently active eruptions. The entire process from start to end is autonomous. Future spacecraft, especially those in deep space, can react to detection of transient processes without the need to communicate with Earth, thus increasing science return. Terrestrially, this removes the need for human intervention.

Davies, Ashley G.↗
NASA NTRS2018

Impacts of Snow and Cloud Covers on Satellite-Derived PM 2.5 Levels

Satellite aerosol optical depth (AOD) has been widely employed to evaluate ground fine particle (PM 2.5 ) levels, whereas snow/cloud covers often lead to a large proportion of non-random missing AOD. As a result, the fully covered and unbiased PM 2.5 estimates will be hard to generate. Among the current approaches to deal with the data gap issue, few have considered the cloud-AOD relationship and none of them have considered the snow-AOD relationship. This study examined the impacts of snow and cloud covers on AOD and PM 2.5 and made full-coverage PM 2.5 predictions with the consideration of these impacts. To estimate the missing AOD, daily gap-filling models with snow/cloud fractions and meteorological covariates were developed using the random forest algorithm. By using these models in New York State, a daily AOD data set with a 1-km resolution was generated with a complete coverage. The“out-of-bag” R 2 of the gap-filling models averaged 0.93 with an interquartile range from 0.90 to 0.95. Subsequently, a random forest-based PM 2.5 prediction model with the gap-filled AOD and covariates was built to predict fully covered PM 2.5 estimates. A ten-fold cross-validation for the prediction model showed a good performance with an R 2 of 0.82. In the gap-filling models, the snow fraction was of higher significance in the snow season compared with the rest of the year. The prediction models fitted with/without the snow fraction also suggested the discernible changes in PM 2.5 patterns, further confirming the significance of this parameter. Compared with the methods without considering snow and cloud covers, our PM 2.5 prediction surfaces showed more spatial details and reflected small-scale terrain-driven PM 2.5 patterns. The proposed methods can be generalized to the areas with extensive snow/cloud covers and large proportions of missing satellite AOD for predicting PM 2.5 levels with high resolutions and complete coverage.

AOD↗
DOE OSTI2022

Photometric redshifts from SDSS images with an interpretable deep capsule network

ABSTRACT Studies of cosmology, galaxy evolution, and astronomical transients with current and next-generation wide-field imaging surveys like the Rubin Observatory Legacy Survey of Space and Time are all critically dependent on estimates of photometric redshifts. Capsule networks are a new type of neural network architecture that is better suited for identifying morphological features of the input images than traditional convolutional neural networks. We use a deep capsule network trained on ugriz images, spectroscopic redshifts, and Galaxy Zoo spiral/elliptical classifications of ∼400 000 Sloan Digital Sky Survey galaxies to do photometric redshift estimation. We achieve a photometric redshift prediction accuracy and a fraction of catastrophic outliers that are comparable to or better than current methods for SDSS main galaxy sample-like data sets (r ≤ 17.8 and zspec ≤ 0.4) while requiring less data and fewer trainable parameters. Furthermore, the decision-making of our capsule network is much more easily interpretable as capsules act as a low-dimensional encoding of the image. When the capsules are projected on a two-dimensional manifold, they form a single redshift sequence with the fraction of spirals in a region exhibiting a gradient roughly perpendicular to the redshift sequence. We perturb encodings of real galaxy images in this low-dimensional space to create synthetic galaxy images that demonstrate the image properties (e.g. size, orientation, and surface brightness) encoded by each dimension. We also measure correlations between galaxy properties (e.g. magnitudes, colours, and stellar mass) and each capsule dimension. We publicly release our code, estimated redshifts, and additional catalogues at https://biprateep.github.io/encapZulate-1.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

A Robust Segmented Mixed Effect Regression Model for Baseline Electricity Consumption Forecasting

Renewable energy production has been surging around the world in recent years. To mitigate the increasing uncertainty and intermittency of the renewable generation, proactive demand response algorithms and programs are proposed and developed to further improve the utilization of load flexibility and increase the efficiency of power system operation. One of the biggest challenges to efficient control and operation of demand response resources is how to forecast the baseline electricity consumption and estimate the load impact from demand response resources accurately. In this paper, we propose a mixed effect segmented regression model and a new robust estimate for forecasting the baseline electricity consumption in Southern California, USA, by combining the ideas of random effect regression model, segmented regression model, and the least trimmed squares estimate. Since the log-likelihood of the considered model is not differentiable at breakpoints, we propose a new backfitting algorithm to estimate the unknown parameters. The estimation performance of the new estimation procedure has been demonstrated with both simulation studies and the real data application for the electric load baseline forecasting in Southern California.

42 ENGINEERING↗