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A demand control ventilation system is an integral part of a building’s ventilation design. It adjusts outside ventilation air based on the number of occupants and the ventilation demands that those occupants create.
The high energy consumption from residential buildings provides them large potential to participate in demand response programs. To design appropriate demand response programs for residential buildings, it is important for electric utilities to know the energy consumption characteristics for different types of households so that utilities can send requests to the groups with a higher possibility to successfully respond. In this paper, we develop a load model to generate synthetic load profiles for different types of households incorporating demographical information including Current Population Survey data set and American Time Use Survey data set. The details of each data set and the details of the load models are presented. The synthetic household load profiles are generated by the load model and clustered into different groups based on state, age, number of occupants, income level, and city of the household. The average energy consumption characteristics for different groups of households are analyzed and compared, which will help electric utilities issue demand response signals to appropriate households.
The high energy consumption from residential buildings provides them large potential to participate in demand response programs. To design appropriate demand response programs for residential buildings, it is important for electric utilities to know the energy consumption characteristics for different types of households so that utilities can send requests to the groups with a higher possibility to successfully respond. In this paper, we develop a load model to generate synthetic load profiles for different types of households incorporating demographical information including Current Population Survey data set and American Time Use Survey data set. The details of each data set and the details of the load models are presented. The synthetic household load profiles are generated by the load model and clustered into different groups based on state, age, number of occupants, income level, and city of the household. The average energy consumption characteristics for different groups of households are analyzed and compared, which will help electric utilities issue demand response signals to appropriate households.
A system and method of detecting occupants in a building automation system environment using image based occupancy detection and position determinations. In one example, the system includes an image processing occupancy sensor that detects the number and position of occupants within a space that has controllable building elements such as lighting and ventilation diffusers. Based on the position and location of the occupants, the system can finely control the elements to optimize conditions for the occupants, optimize energy usage, among other advantages.
The authors present smoothed perturbation analysis (SPA) estimators for the derivative of a number of occupancy-related functions in serial queuing networks with finite buffer spaces. The functions are the average number of customers at a network as seen by an arrival, the probability that a customer is blocked at a particular queue, and the probability that a customer leaves a queue empty. In all three cases, the variable is a parameter of the distribution of service times at one of the queues. The derivative estimators considered are very simple and flexible, and they easily lend themselves to analysis of unbiasedness. Unlike most of the established SPA estimators, the present ones are not based on the computation of hazard rates.
Occupancy profile is one of the driving factors behind discrepancies between the measured and simulated energy consumption of buildings. The frequencies of occupants leaving their offices and the corresponding durations of absences have significant impact on energy use and the operational controls of buildings. This study used statistical methods to analyze the occupancy status, based on measured lighting-switch data in five-minute intervals, for a total of 200 open-plan (cubicle) offices. Five typical occupancy patterns were identified based on the average daily 24-hour profiles of the presence of occupants in their cubicles. These statistical patterns were represented by a one-square curve, a one-valley curve, a two-valley curve, a variable curve, and a flat curve. The key parameters that define the occupancy model are the average occupancy profile together with probability distributions of absence duration, and the number of times an occupant is absent from the cubicle. The statistical results also reveal that the number of absence occurrences decreases as total daily presence hours decrease, and the duration of absence from the cubicle decreases as the frequency of absence increases. The developed occupancy model captures the stochastic nature of occupants moving in and out of cubicles, and can be used to generate a more realistic occupancy schedule. This is crucial for improving the evaluation of the energy saving potential of occupancy based technologies and controls using building simulations. Finally, to demonstrate the use of the occupancy model, weekday occupant schedules were generated and discussed.
The Image Processing Occupancy Sensor, or IPOS, is a novel sensor technology developed at the National Renewable Energy Laboratory (NREL). The sensor is based on low-cost embedded microprocessors widely used by the smartphone industry and leverages mature open-source computer vision software libraries. Compared to traditional passive infrared and ultrasonic-based motion sensors currently used for occupancy detection, IPOS has shown the potential for improved accuracy and a richer set of feedback signals for occupant-optimized lighting, daylighting, temperature setback, ventilation control, and other occupancy and location-based uses. Unlike traditional passive infrared (PIR) or ultrasonic occupancy sensors, which infer occupancy based only on motion, IPOS uses digital image-based analysis to detect and classify various aspects of occupancy, including the presence of occupants regardless of motion, their number, location, and activity levels of occupants, as well as the illuminance properties of the monitored space. The IPOS software leverages the recent availability of low-cost embedded computing platforms, computer vision software libraries, and camera elements.
In the past decade, different sensing mechanisms and algorithms have been developed to detect or estimate indoor occupancy. One of the most recent advancements is using networked sensor nodes to create a more comprehensive occupancy detection system where multiple sensors can identify human presence within more expansive areas while delivering enhanced accuracy compared to a system that relies on stand-alone sensor nodes. The present work reviews the studies from 2012 to 2022 that use networked sensor nodes to detect indoor occupancy, focusing on PIR-based sensors. Methods are compared based on pivotal ADPs that play a significant role in selecting an occupancy detection system for applications such as Health and Safety or occupant comfort. These parameters include accuracy, information requirement, maximum sensor failure and minimum observation rate, and feasible detection area. We briefly describe the overview of occupancy detection criteria used by each study and introduce a metric called “sensor node deployment density” through our analysis. This metric captures the strength of network-level data filtering and fusion algorithms found in the literature. It is hinged on the fact that a robust occupancy estimation algorithm requires a minimal number of nodes to estimate occupancy. This review only focuses on the occupancy estimation models for networked sensor nodes. It thus provides a standardized insight into networked nodes’ occupancy sensing pipelines, which employ data fusion strategies, network-level machine learning algorithms, and occupancy estimation algorithms. This review thus helps determine the suitability of the reviewed methods to a standard set of application areas by analyzing their gaps.
An important feature of modern GPU architectures is variable occupancy. Occupancy measures the ratio between the number of active threads on a GPU and the maximum number of threads the GPU hardware can schedule. High occupancy allows a large number of threads to run simultaneously and hide memory latency, but may increase resource contention. Low occupancy has less resource contention, but is also less capable of latency hiding. Occupancy tuning is an important and challenge problem. A program running at different occupancy levels can have three to four times difference in running time. There has been limited exploration in GPU program occupancy tuning. We introduce Orion, the first GPU occupancy tuning framework.
A measurement of the flavor asymmetry of the antiquarks ($\bar{d}$ and $\bar{u}$) in the proton is described in this thesis. The proton consists of three valence quarks, sea quarks, and gluons. Antiquarks in the proton are sea quarks. They are generated from the gluon splitting: g → q + $\bar{q}$. According to QCD (Quantum Chromodynamics), the gluon splitting is independent of quark flavor. It suggests that the amounts of $\bar{d}$ and $\bar{u}$ should be the same in the proton. However, the NMC experiment at CERN found that the amount of $\bar{d}$ is larger than that of $\bar{u}$ in the proton using the deep inelastic scattering in 1991. This result is obtained for $\bar{d}$ and $\bar{u}$ integrated over Bjorken x. Bjorken x is the fraction of the momentum of the parton to that of the proton. The NA51 experiment (x ~ 0.2) at CERN and E866/NuSea experiment (0.015 < x < 0.35) at Fermilab measured the flavor asymmetry of the antiquarks ($\bar{d}$/$\bar{u}$) in the proton as a function of x using Drell–Yan process. The experiments reported that the flavor symmetry is broken over all measured x values. Understanding the flavor asymmetry of the antiquarks in the proton is a challenge of the QCD. The theo- retical investigation from the first principle of QCD such as lattice QCD calculation is important. In addition, the QCD effective models and hadron models such as the meson cloud model can also be tested with the flavor asymmetry of antiquarks. From the experimental side, it is important to measure with higher accuracy and in a wider x range. The SeaQuest (E906) experiment measures $\bar{d}$/$\bar{u}$ at large x (0.15 < x < 0.45) accurately to understand its behavior. The SeaQuest experiment is a Drell–Yan experiment at Fermi National Accelerator Laboratory (Fermilab). In the Drell–Yan process of proton-proton reaction, an antiquark in a proton and a quark in another proton annihilate and create a virtual photon, which then decays into a muon pair (q$\bar{q}$ → γ* → µ + µ - ). The SeaQuest experiment uses a 120 GeV proton beam extracted from Fermilab’s Main Injector. The proton beam interacts with hydrogen and deuterium targets. The SeaQuest spectrometer detects the muon pairs from the Drell–Yan process. The $\bar{d}$/$\bar{u}$ ratio at 0.1 < x < 0.58 is extracted from the number of detected Drell–Yan muon pairs. After the detector construction, commissioning run and detector upgrade, the SeaQuest experiment started the physics data acquisition from 2013. We finished so far three periods of physics data acquisition. The fourth period is in progress. The detector construction, detector performance evaluation, data taking and data analysis for the flavor asymmetry of the antiquarks $\bar{d}$/$\bar{u}$ in the proton are my contribution to SeaQuest. The cross section ratio of Drell–Yan process in p- p and p-d reactions is obtained from dimuon yields. In the experiment with high beam intensity, it is important to control the tracking efficiency of charged particles through the magnetic spectrometer. The tracking efficiency depends on the chamber occupancy, and the appropriate method for the correction is important. The chamber occupancy is the number of hits in drift chambers. A new method of the correction for the tracking efficiency is developed based on the occupancy, and applied to the data. This method reflects the real response of the drift chambers. Therefore, the systematic error is well controlled by this method. The flavor asymmetry of antiquarks is obtained at 0.1 < x < 0.58. At 0.1 < x < 0.45, the result is $\bar{d}$/$\bar{u}$ > 1. The result at 0.1 < x < 0.24 agrees with the E866 result. The result at x > 0.24, however, disagrees with the E866 result. The result at 0.45 < x < 0 the statistical errors. u¯ results extracted from experiments are used to investigate the validity of the theoretical models. The present experimental result provides the data points in wide x region. It is useful for understanding the proton structure in the light of QCD and effective hadron models. The present result has a practical application as well. Antiquark distributions are important as inputs to simulations of hadron reactions such as W± production in various experiments. The new knowledge on antiquark distributions helps to improve the precision of the simulations.
This paper introduces a ridesharing model useful for examining the role of ridesharing in advancing Urban Air Mobility (UAM) operations. Although the success of UAM will likely require a host of new technologies in aircraft design, airspace management, autonomy, and more, operational innovations like ridesharing may also be key, especially to lower operating costs and attract a broader market. The ridesharing algorithm introduced, termed the Passenger Aggregation Network with Very Efficient Listing (PANVEL) model, estimates trip numbers and passenger occupancy aboard particular aircraft within a UAM network in a metropolitan area. This algorithm is applied within a transportation computational framework to determine the impact of ridesharing on mode choice, passenger travel patterns, and transportation costs. The algorithm aggregates passengers traveling between identical origin-destination pairs, factoring in individual-specific metrics, such as value of time and alternative mode options, to generate high load factors on UAM aircraft. The computational framework simulates passenger mode-choice selections using an effective-cost approach to determine the number of ridesharing UAM-preferred trips, and results with ridesharing are representative of a rough upper bound on UAM ridership potential for a given metro area. A case study in the Cleveland metro area is presented to demonstrate the algorithm and assess ridesharing potential.
This presentation introduces a ridesharing model useful for examining the role of ridesharing in advancing Urban Air Mobility (UAM) operations. Although the success of UAM will likely require a host of new technologies in aircraft design, airspace management, autonomy, and more, operational innovations like ridesharing may also be key, especially to lower operating costs and attract a broader market. The ridesharing algorithm introduced, termed the Passenger Aggregation Network with Very Efficient Listing (PANVEL) model, estimates trip numbers and passenger occupancy aboard particular aircraft within a UAM network in a metropolitan area. This algorithm is applied within a transportation computational framework to determine the impact of ridesharing on mode choice, passenger travel patterns, and transportation costs. The algorithm aggregates passengers traveling between identical origin-destination pairs, factoring in individual-specific metrics, such as value of time and alternative mode options, to generate high load factors on UAM aircraft. The computational framework simulates passenger mode-choice selections using an effective-cost approach to determine the number of ridesharing UAM-preferred trips, and results with ridesharing are representative of a rough upper bound on UAM ridership potential for a given metro area. A case study in the Cleveland metro area is presented to demonstrate the algorithm and assess ridesharing potential.
Abstract Low‐energy spectra of single‐molecule magnets (SMMs) are often described by Heisenberg Hamiltonians. Within this formalism, exchange interactions between magnetic centers determine the ground‐state multiplicity and energy separation between the ground and excited states. In this contribution, we extract exchange coupling constants ( J ) for a set of iron (III) binuclear and tetranuclear complexes from all‐electron calculations using non‐collinear spin‐flip time‐dependent density functional theory (NC‐SF‐TDDFT). For 12 binuclear complexes with J ‐values ranging from −6 to −132 cm −1 , our benchmark calculations using the short‐range hybrid ω PBEh functional and 6‐31G(d,p) basis set agree well with the experimentally derived values (mean absolute error of 4.7 cm −1 ). For the tetranuclear SMMs, the computed J constants are within 6 cm −1 from the experimentally derived values. We explore the range of applicability of the Heisenberg model by analyzing bonding patterns in these Fe(III) complexes using natural orbitals (NO), their occupations, and the number of effectively unpaired electrons. The results illustrate the efficiency of the spin‐flip protocol for computing the exchange couplings and the utility of the NO analysis in assessing the validity of effective spin Hamiltonians.
In magic-angle twisted bilayer graphene, the moiré superlattice potential gives rise to narrow electronic bands that support a multitude of many-body quantum phases. Further richness arises in the presence of a perpendicular magnetic field, where the interplay between moiré and magnetic length scales leads to fractal Hofstadter subbands. In this strongly correlated Hofstadter platform, multiple experiments have identified gapped topological and correlated states, but little is known about the phase transitions between them in the intervening compressible regimes. Here we simultaneously unveil sequences of broken-symmetry Chern insulators and resolve sharp phase transitions between competing states with different topological quantum numbers and different occupations of the spin-valley flavour. Our measurements determine the energy spectrum of interacting Hofstadter subbands in magic-angle twisted bilayer graphene and map out the phase diagram of flavour occupancy. In addition, we observe full lifting of the degeneracy of the zeroth Landau levels together with level crossings, indicating moiré valley splitting. We propose a unified flavour polarization mechanism to understand the intricate interplay of topology, interactions and symmetry breaking as a function of density and applied magnetic field in this system.
We present a scalable machine learning (ML) model to predict local electronic properties such as on-site electron number and double occupation for disordered correlated electron systems. Our approach is based on the locality principle, or the nearsightedness nature, of many-electron systems, which means local electronic properties depend mainly on the immediate environment. A ML model is developed to encode this complex dependence of local quantities on the neighborhood. We demonstrate our approach using the square-lattice Anderson-Hubbard model, which is a paradigmatic system for studying the interplay between Mott transition and Anderson localization. We develop a lattice descriptor based on the group-theoretical method to represent the on-site random potentials within a finite region. The resultant feature variables are used as input to a multilayer fully connected neural network, which is trained from data sets of variational Monte Carlo (VMC) simulations on small systems. We show that the ML predictions agree reasonably well with the VMC data. Our work underscores the promising potential of ML methods for multiscale modeling of correlated electron systems.
One of the challenges of high granularity calorimeters, such as that to be built to cover the endcap region in the CMS Phase-2 Upgrade for HL-LHC, is that the large number of channels causes a surge in the computing load when clustering numerous digitized energy deposits (hits) in the reconstruction stage. In this article, we propose a fast and fully parallelizable density-based clustering algorithm, optimized for high-occupancy scenarios, where the number of clusters is much larger than the average number of hits in a cluster. The algorithm uses a grid spatial index for fast querying of neighbors and its timing scales linearly with the number of hits within the range considered. We also show a comparison of the performance on CPU and GPU implementations, demonstrating the power of algorithmic parallelization in the coming era of heterogeneous computing in high-energy physics.
Although the determination of site occupancies is often a major goal in Rietveld refinement studies, the accurate refinement of site occupancies is exceptionally challenging due to the many correlations and systematic errors that have a hidden impact on the final refined occupancy parameters. Through the comparison of results independently obtained from neutron and synchrotron powder diffraction, improved approaches capable of detecting occupancy defects with an exceptional sensitivity of 0.1% (absolute) in the class of layered NMC Li-ion battery cathode materials have been developed. A new method of visualizing the diffraction parameter space associated with crystallographic site scattering power through the use of f* diagrams is described and this method is broadly applicable to ternary compounds. The f* diagrams allow the global minimum fit to be easily identified and also permit a robust determination of the number and type of occupancy defects within a structure. Through a comparison of neutron and X-ray diffraction results, a systematic error in the synchrotron results was identified using f* diagrams for a series of NMC compounds. Using neutron diffraction data as a reference, this error was shown to specifically result from problems associated with the neutral oxygen X-ray atomic form factor and could be eliminated by using the ionic O 2- form factor for this anion while retaining the neutral form factors for cationic species. The f* diagram method offers a new opportunity to experimentally assess the quality of atomic form factors through powder diffraction studies on chemically related multi-component compounds.
Abstract not provided