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Short-Term Forecasting of Thermostatic and Residential Loads Using Long Short-Term Memory Recurrent Neural Networks
Internet of Things (IoT) devices in smart grids enable intelligent energy management for grid managers and personalized energy services for consumers. Investigating a smart grid with IoT devices requires a simulation framework with IoT devices modeling. However, there lack comprehensive study on the modeling of IoT devices in smart grids. This paper investigates the IoT device modeling of a thermostatic load and implements the recurrent neural networks model for short-term load forecasting in this IoT-based thermostatic load. The recurrent neural network structure is leveraged to build a load forecasting model on temporal correlation. The temporal recurrent neural network layers including long short-term memory cells are employed to learn the data from both the simulation platform and New South Wales residential datasets. The simulation results are provided for demonstration.
Infrared Emission from Kilonovae: The Case of the Nearby Short Hard Burst GRB 160821B
In this paper, we present constraints on Ks-band emission from one of the nearest short hard gamma-ray bursts, GRB 160821B, at z = 0.16, at three epochs. We detect a red relativistic afterglow from the jetted emission in the first epoch but do not detect any excess kilonova emission in the second two epochs. We compare upper limits obtained with Keck I/MOSFIRE to multi-dimensional radiative transfer models of kilonovae, that employ composition-dependent nuclear heating and LTE opacities of heavy elements. We discuss eight models that combine toroidal dynamical ejecta and two types of wind and one model with dynamical ejecta only. We also discuss simple, empirical scaling laws of predicted emission as a function of ejecta mass and ejecta velocity. Our limits for GRB 160821B constrain the ejecta mass to be lower than 0.03 M ⊙ for velocities greater than 0.1 c. At the distance sensitivity range of advanced LIGO, similar ground-based observations would be sufficiently sensitive to the full range of predicted model emission including models with only dynamical ejecta. Finally, the color evolution of these models shows that I–K color spans 7–16 mag, which suggests that even relatively shallow infrared searches for kilonovae could be as constraining as optical searches.
Glass formation and short-range order structures in the BaS + La 2 S 3 + GeS 2 system
Infrared (IR) optical materials have enabled a broad range of optical sensing and measurement applications in the mid-wave and long-wave IR. Many IR transmitting glasses are based on covalently-bonded selenides and tellurides, such as As 2 Se 3 and GeTe 2 , which typically have relatively low glass transition temperatures (T g ) on the order of 200 to 350 °C. Many applications have working temperatures above the T g of these materials, which compels the development of new IR materials. This work studies the underlying short-range order (SRO) structure and glass formability of a new family of ionically-bonded sulfide glasses, xBaS + yLa 2 S 3 + (1-x-y)GeS 2 , to develop high T g optical materials with a broad IR transmission range. These sulfide glasses were produced by melting sulfide materials inside evacuated and sealed carbon-coated silica ampoules at 1150 °C for 12 hours and quenching to room temperature to form glass. Glass samples were then characterized by IR and Raman spectroscopies and differential thermal analysis (DTA). It was found that by increasing the modifier concentration, the predominantly Ge 4 SRO units, the superscript defines the number of bridging sulfur (BS) ions in the tetrahedral network found in GeS 2 glasses, are ultimately converted to Ge 0 units at > 40 mol% network modifier content through the generation of non-bridging sulfur (NBS) ions. These molecular ionic units still form a glassy network, with some of the highest reported T g values to date for a pure sulfide glass. This suggests that this composition has strong ionic bonds between negatively-charged tetrahedral SRO units and the positively-charged modifier cations. While the glass network is depolymerized in the high modifier content glasses though the formation of a high concentration of molecular ionic Ge 0 SRO groups, they are, nevertheless, homogeneous glassy materials that exhibit the largest T g and ΔT (difference between crystallization temperature, T c , and T g ) values of glasses in this system, making them the optimal glasses for high T g IR optical components, including, potentially, refractory IR optical fibers.
Mechanical property degradation and microstructural evolution of cast austenitic stainless steels under short-term thermal aging
Mechanical testing and microstructural characterization were performed on short-term thermally aged cast austenitic stainless steels (CASS) to understand the severity and mechanisms of thermal-aging degradation experienced during extended operation of light water reactor (LWR) coolant systems. Four CASS materials – CF3, CF3M, CF8, and CF8M – were thermally aged for 1500 hours at 290 °C, 330 °C, 360 °C, and 400 °C. All four alloys experienced insignificant change in strength and ductility properties but a significant reduction in absorbed impact energy. The primary microstructural and compositional changes during thermal aging were spinodal decomposition of the δ-ferrite into α/ α`, precipitation of G-phase in the δ-ferrite, segregation of solute to the austenite/ ferrite interphase boundary, and growth of M23C6 carbides on the austenite/ferrite interphase boundary. These changes were shown to be highly dependent on chemical composition, particularly the concentration of C and Mo, and aging temperature. A comprehensive model is being developed to correlate the microstructural evolution with mechanical behavior and simulation for predictive evaluations of LWR coolant system components.
Rapid extraction of short-lived isotopes from a buffer gas cell for use in gas-phase chemistry experiments, Part II: On-line studies with short-lived accelerator-produced radionuclides
We report a novel combination of advanced gas-chromatography and detection systems coupled to a buffer-gas cell was characterized on-line to allow gas-phase chemical studies of accelerator-produced short-lived α -decaying mercury, francium, and astatine isotopes. These were produced in 40 Ar- and 48 Ca-induced nuclear fusion–evaporation reactions, subsequently isolated in the recoil separators MARS at Texas A&M University, USA, and TASCA at GSI Darmstadt, Germany, before being thermalized in a buffer-gas-stopping cell. From the latter, the nuclear reaction products were extracted into gas-phase chromatographic systems, suitable for registering α-decaying short-lived radionuclides, such as isotopes of superheavy elements. Efficiencies of 21(3)% for 204-209 Fr were reached for the extraction into the optimized miniCOMPACT gas-chromatography setup, indicating that this technique enables the identification of isotopes of volatile as well as non-volatile elements. These studies guide the path towards chemical investigations of superheavy elements beyond flerovium, which are out of reach with currently used setups.
Role of Short-Range Chemical Ordering in (GaN) 1– x (ZnO) x for Photodriven Oxygen Evolution
(GaN) 1–x (ZnO) x (GZNO) is capable of visible-light driven water splitting, but its bandgap at x ≤ 0.15 (>2.7 eV) results in poor visible-light absorption. Unfortunately, methods to narrow its bandgap by incorporating higher ZnO concentrations are accompanied by extensive Urbach tailing near the absorption-edge, which is indicative of structural disorder or chemical inhomogeneities. We evaluated whether this disorder is intrinsic to the bond-length distribution in GZNO or is a result of defects introduced from the loss of Zn during nitridation. Here, the synthesis of GZNO derived from layered double hydroxide (LDH) precursors is described which minimizes Zn loss and chemical inhomogeneities and enhances visible-light absorption. The average and local atomic structures of LDH-derived GZNO were investigated using X-ray and neutron scattering and are correlated with their oxygen evolution rates. An isotope-contrasted neutron-scattering experiment was conducted in conjunction with reverse Monte Carlo (RMC) simulations. We showed that a bond-valence bias in the RMC refinements reproduces the short-range ordering (SRO) observed in structure refinements using isotope-contrasted neutron data. The findings suggest that positional disorder of cation–anion pairs in GZNO partially arises from SRO and influences local bond relaxations. Furthermore, particle-based oxygen evolution reactions (OERs) in AgNO 3 solution reveal that the crystallite size of GZNO correlates more than positional disorder with oxygen evolution rate. In conclusion, these findings illustrate the importance of examining the local structure of multinary photocatalysts to identify dominant factors in particulate-based photodriven oxygen evolution.
Local radiative feedbacks over the Arctic based on observed short-term climate variations
We compare various radiative feedbacks in response to short-term climate variations estimated from reanalysis, satellite, and global climate model datasets using the combined Kernel-Gregory approach over the Arctic (60-90°N). Except for the shortwave cloud feedback, all feedbacks are positive over the Arctic. Lapse rate feedback is comparable with surface albedo feedback. Relative to the tropics (30°S-30°N), the temperature feedback (lapse rate feedback plus Planck feedback deviation from its global mean) is the largest contributor to Arctic amplification among all feedbacks, followed by surface albedo feedback. The choice of meteorological fields has a small impact on the cloud feedback estimate, but different top-of-the-atmosphere clear-sky fluxes datasets can result in a wide spread of estimated cloud feedbacks. The net cloud feedback over the Arctic is positive due to a larger positive in longwave than the negative shortwave feedback, but has the largest uncertainty. Relative to the tropics, the longwave cloud feedback largely contributes to the Arctic amplification.
The Short Life of Upvalley Wind in a High‐Altitude Valley in the Colorado Rocky Mountains
Thermally driven upvalley (UV) wind in the upper East River Valley in the Colorado Rocky Mountains often unexpectedly stops in midmorning and reverses back to downvalley (DV) wind. We use a comprehensive observational data set for a nearly two‐year long period to analyze the wind system and boundary layer evolution in this high‐altitude valley and determine the reason for this early wind reversal. Days with short UV wind predominantly occur during the warm season when the valley floor is free of snow and the convective boundary layer (CBL) grows well above the height of the surrounding ridges. UV wind persists throughout the day only on a few days during the warm season. We link differences in valley wind evolution to wind direction at upper levels at and above ridge height and propose forced channeling mechanisms to describe coupling between valley and upper‐level wind when the CBL grows above ridge height. The frequency distribution of upper‐level wind direction is such that channeling in the DV direction is favored, which explains the predominance of days with short UV wind. The deep CBL is supported by the presence of a deep weakly stably stratified residual layer with high aerosol content, which is regularly present over the mountain range during the warm season. On days when the CBL does not grow above ridge height, for example, when the valley floor is covered by snow, thermally driven UV wind is able to persist throughout the day independent of upper‐level wind direction.
Short-range ordering in a battery electrode, the ‘cation-disordered’ rocksalt Li 1.25 Nb 0.25 Mn 0.5 O 2
We demonstrate short-range ordering in Li-ion battery material Li 1.25 Nb 0.25 Mn 0.5 O 2 , and identify its local structure and correlation length—which is sensitive to synthesis conditions and has important consequences for the material's electrochemistry.
A crystallographic approach to the short-range ordering problem in V[subscript 1;#8722;x]Mo[subscript x]O[subscript 2] (0.50 ;#8804; x ;#8804; 0.60)
Abstract not provided
Demonstration of a time-integrated short line of sight neutron imaging system for inertial confinement fusion
The Neutron Imaging System (NIS) is an important diagnostic for understanding implosions of deuterium-tritium capsules at the National Ignition Facility. While the detectors for the existing system must be positioned 28 m from the source to produce sufficient imaging magnification and resolution, recent testing of a new short line of sight neutron imaging system has shown sufficient resolution to allow reconstruction of the source image with quality similar to that of the existing NIS on a 11.6 m line of sight. The new system used the existing pinhole aperture array and a stack of detectors composed of 2 mm thick high-density polyethylene converter material followed by an image plate. In these detectors, neutrons enter the converter material and interact with protons, which recoil and deposit energy within the thin active layer of the image plate through ionization losses. The described system produces time-integrated images for all neutron energies passing through the pinhole. We present details of the measurement scheme for this novel technique to produce energy-integrated neutron images as well as source reconstruction results from recent experiments at NIF.
Conceptual moderator studies for the Spallation Neutron Source short-pulse second target station
Candidate moderator configurations for a short-pulse second target station (STS) at the Oak Ridge National Laboratory Spallation Neutron Source (SNS) have been identified using a global optimizer framework built around the MCNPX particle transport code. Neutron brightness metrics were selected as the figure-of-merit. We assumed that STS would use one out of six proton pulses produced by an SNS accelerator upgraded to operate at 1.3 GeV proton energy, 2.8 MW power and 60 Hz repetition rate. The simulations indicate that the peak brightness can be increased by a factor of 5 and 2.5 on a per proton pulse basis compared to the SNS first target station for both coupled and decoupled para-hydrogen moderators, respectively. Additional increases by factors of 3 and 2 were demonstrated for coupled and decoupled moderators, respectively, by reducing the area of neutron emission from 100 × 100 mm{sup 2} to 20 × 20 mm{sup 2}. This increase in brightness has the potential to translate to an increase of beam intensity at the instruments’ sample positions even though the total neutron emission of the smaller moderator is less than that of the larger. This is especially true for instruments with small samples (beam dimensions). The increased fluxes in the STS moderators come at accelerated poison and de-coupler burnout and higher radiation-induced material damage rates per unit power, which overall translate into lower moderator lifetimes. A first effort was undertaken to group decoupled moderators into a cluster collectively positioning them at the peak neutron production zone in the target and having a three-port neutron emission scheme that complements that of a cylindrical coupled moderator.
Radial direct bandgap p-i-n GaNP microwire solar cells with enhanced short circuit current
We report the demonstration of dilute nitride heterostructure core/shell microwire solar cells utilizing the combination of top-down reactive-ion etching to create the cores (GaP) and molecular beam epitaxy to create the shells (GaNP). Systematic studies of cell performance over a series of microwire lengths, array periods, and microwire sidewall morphologies examined by transmission electron microscopy were conducted to shed light on performance-limiting factors and to optimize the cell efficiency. We show by microscopy and correlated external quantum efficiency characterization that the open circuit voltage is degraded primarily due to the presence of defects at the GaP/GaNP interface and in the GaNP shells, and is not limited by surface recombination. Compared to thin film solar cells in the same growth run, the microwire solar cells exhibit greater short circuit current but poorer open circuit voltage due to greater light absorption and number of defects in the microwire structure, respectively. The comprehensive understanding presented in this work suggests that performance benefits of dilute nitride microwire solar cells can be achieved by further tuning of the epitaxial quality of the underlying materials.
When do short-range atomistic machine-learning models fall short?
In this work, we explore the role of long-range interactions in atomistic machine-learning models by analyzing the effects on fitting accuracy, isolated cluster properties, and bulk thermodynamic properties. Such models have become increasingly popular in molecular simulations given their ability to learn highly complex and multi-dimensional interactions within a local environment; however, many of them fundamentally lack a description of explicit long-range interactions. In order to provide a well-defined benchmark system with precisely known pairwise interactions, we chose as the reference model a flexible version of the Extended Simple Point Charge (SPC/E) water model. Our analysis shows that while local representations are sufficient for predictions of the condensed liquid phase, the short-range nature of machine-learning models falls short in representing cluster and vapor phase properties. These findings provide an improved understanding of the role of long-range interactions in machine learning models and the regimes where they are necessary.
Quantitative characterization of short-range orthorhombic fluctuations in FeSe through pair distribution function analysis
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Short-Term Load Forecasting Considering EV Charging Loads with Prediction Interval Evaluation
Short-term load forecasting plays a critical role in power system planning and operation. Along with the electrification of various loads, electricity demands are becoming increasingly hard to predict. Notably, the recent rise in electric vehicles (EVs) has further contributed to this unpredictability. To address this issue, this paper proposes a probabilistic load forecasting strategy utilizing Gaussian process regression, structured in a day-ahead manner. While many works focus on deterministic prediction, probabilistic forecasting offers additional insights into variability and uncertainty, enabling more flexible and reliable operation for power systems. To enhance the accuracy of the load forecasting model, the inputs include features related to EV charging habits as well as commonly used weather information. The load forecasting results are evaluated using various metrics, including conventional ones that assess the accuracy of point forecasts, as well as additional metrics that test the reliability of prediction intervals. The proposed load forecasting method is finally tested on real residential power consumption data and EV charging data sampled from real-world sources. The results prove that the new features can greatly improve the performance of the load forecasting method.