ASU Scholarship Showcase

The estimation of energy demand (by power plants) has traditionally relied on historical energy use data for the region(s) that a plant produces for. Regression analysis, artificial neural network and Bayesian theory are the most common approaches for analysing these data. Such data and techniques do not generate reliable results. Consequently, excess energy has to be generated to prevent blackout; causes for energy surge are not easily determined; and potential energy use reduction from energy efficiency solutions is usually not translated into actual energy use reduction. The paper highlights the weaknesses of traditional techniques, and lays out a framework to improve the prediction of energy demand by combining energy use models of equipment, physical systems and buildings, with the proposed data mining algorithms for reverse engineering. The research team first analyses data samples from large complex energy data, and then, presents a set of computationally efficient data mining algorithms for reverse engineering. In order to develop a structural system model for reverse engineering, two focus groups are developed that has direct relation with cause and effect variables. The research findings of this paper includes testing out different sets of reverse engineering algorithms, understand their output patterns and modify algorithms to elevate accuracy of the outputs.

Small and medium office buildings consume a significant parcel of the U.S. building stock energy consumption. Still, owners lack resources and experience to conduct detailed energy audits and retrofit analysis. We present an eight-steps framework for an energy retrofit assessment in small and medium office buildings. Through a bottom-up approach and a web-based retrofit toolkit tested on a case study in Arizona, this methodology was able to save about 50% of the total energy consumed by the case study building, depending on the adopted measures and invested capital. While the case study presented is a deep energy retrofit, the proposed framework is effective in guiding the decision-making process that precedes any energy retrofit, deep or light.

Commercial buildings’ consumption is driven by multiple factors that include occupancy, system and equipment efficiency, thermal heat transfer, equipment plug loads, maintenance and operational procedures, and outdoor and indoor temperatures. A modern building energy system can be viewed as a complex dynamical system that is interconnected and influenced by external and internal factors. Modern large scale sensor measures some physical signals to monitor real-time system behaviors. Such data has the potentials to detect anomalies, identify consumption patterns, and analyze peak loads. The paper proposes a novel method to detect hidden anomalies in commercial building energy consumption system. The framework is based on Hilbert-Huang transform and instantaneous frequency analysis. The objectives are to develop an automated data pre-processing system that can detect anomalies and provide solutions with real-time consumption database using Ensemble Empirical Mode Decomposition (EEMD) method. The finding of this paper will also include the comparisons of Empirical mode decomposition and Ensemble empirical mode decomposition of three important type of institutional buildings.

There are many data mining and machine learning techniques to manage large sets of complex energy supply and demand data for building, organization and city. As the amount of data continues to grow, new data analysis methods are needed to address the increasing complexity. Using data from the energy loss between the supply (energy production sources) and demand (buildings and cities consumption), this paper proposes a Semi-Supervised Energy Model (SSEM) to analyse different loss factors for a building cluster. This is done by deep machine learning by training machines to semi-supervise the learning, understanding and manage the process of energy losses. Semi-Supervised Energy Model (SSEM) aims at understanding the demand-supply characteristics of a building cluster and utilizes the confident unlabelled data (loss factors) using deep machine learning techniques. The research findings involves sample data from one of the university campuses and presents the output, which provides an estimate of losses that can be reduced. The paper also provides a list of loss factors that contributes to the total losses and suggests a threshold value for each loss factor, which is determined through real time experiments. The conclusion of this paper provides a proposed energy model that can provide accurate numbers on energy demand, which in turn helps the suppliers to adopt such a model to optimize their supply strategies.

The development of non-volatile logic through direct coupling of spontaneous ferroelectric polarization with semiconductor charge carriers is nontrivial, with many issues, including epitaxial ferroelectric growth, demonstration of ferroelectric switching and measurable semiconductor modulation. Here we report a true ferroelectric field effect—carrier density modulation in an underlying Ge(001) substrate by switching of the ferroelectric polarization in epitaxial c-axis-oriented BaTiO₃ grown by molecular beam epitaxy. Using the density functional theory, we demonstrate that switching of BaTiO₃ polarization results in a large electric potential change in Ge. Aberration-corrected electron microscopy confirms BaTiO₃ tetragonality and the absence of any low-permittivity interlayer at the interface with Ge. The non-volatile, switchable nature of the single-domain out-of-plane ferroelectric polarization of BaTiO₃ is confirmed using piezoelectric force microscopy. The effect of the polarization switching on the conductivity of the underlying Ge is measured using microwave impedance microscopy, clearly demonstrating a ferroelectric field effect.

The emission properties of GeSn heterostructure pin diodes have been investigated. The devices contain thick (400–600 nm) Ge_1-y Sn_y i-layers spanning a broad compositional range below and above the crossover Sn concentration y_c where the Ge_1-y Sn_y alloy becomes a direct-gap material. These results are made possible by an optimized device architecture containing a single defected interface thereby mitigating the deleterious effects of mismatch-induced defects. The observed emission intensities as a function of composition show the contributions from two separate trends: an increase in direct gap emission as the Sn concentration is increased, as expected from the reduction and eventual reversal of the separation between the direct and indirect edges, and a parallel increase in non-radiative recombination when the mismatch strains between the structure components is partially relaxed by the generation of misfit dislocations. An estimation of recombination times based on the observed electroluminescence intensities is found to be strongly correlated with the reverse-bias dark current measured in the same devices.

The compositional dependence of the lowest direct and indirect band gaps in Ge_1-ySn_y alloys has been determined from room-temperature photoluminescence measurements. This technique is particularly attractive for a comparison of the two transitions because distinct features in the spectra can be associated with the direct and indirect gaps. However, detailed modeling of these room temperature spectra is required to extract the band gap values with the high accuracy required to determine the Sn concentration y_c at which the alloy becomes a direct gap semiconductor. For the direct gap, this is accomplished using a microscopic model that allows the determination of direct gap energies with meV accuracy. For the indirect gap, it is shown that current theoretical models are inadequate to describe the emission properties of systems with close indirect and direct transitions. Accordingly, an ad hoc procedure is used to extract the indirect gap energies from the data. For y < 0.1 the resulting direct gap compositional dependence is given by ΔE₀ = −(3.57 ± 0.06)y (in eV). For the indirect gap, the corresponding expression is ΔE_ind = −(1.64 ± 0.10)y (in eV). If a quadratic function of composition is used to express the two transition energies over the entire compositional range 0 ≤ y ≤ 1, the quadratic (bowing) coefficients are found to be b₀ = 2.46 ± 0.06 eV (for E0) and b_ind = 1.03 ± 0.11 eV (for E_ind). These results imply a crossover concentration y_c = $0.073 [+0.007 over -0.006], much lower than early theoretical predictions based on the virtual crystal approximation, but in better agreement with predictions based on large atomic supercells.

Earth-abundant sustainable inorganic thin-film solar cells, independent of precious elements, pivot on a marginal material phase space targeting specific compounds. Advanced materials characterization efforts are necessary to expose the roles of microstructure, chemistry, and interfaces. Herein, the earth-abundant solar cell device, Cu₂ZnSnS_(4-x)Se_x, is reported, which shows a high abundance of secondary phases compared to similarly grown Cu₂ZnSnSe₄.

The electronic band structure of MoS₂, MoSe₂, WS₂, and WSe₂, crystals has been studied at various hydrostatic pressures experimentally by photoreflectance (PR) spectroscopy and theoretically within the density functional theory (DFT). In the PR spectra direct optical transitions (A and B) have been clearly observed and pressure coefficients have been determined for these transitions to be: α_A = 2.0 ± 0.1 and α_B = 3.6 ± 0.1 meV/kbar for MoS₂, α_A = 2.3 ± 0.1 and α_B = 4.0 ± 0.1 meV/kbar for MoSe₂, α_A = 2.6 ± 0.1 and α_B = 4.1 ± 0.1 meV/kbar for WS₂, α_A = 3.4 ± 0.1 and α_B = 5.0 ± 0.5 meV/kbar for WSe₂. It has been found that these coefficients are in an excellent agreement with theoretical predictions. In addition, a comparative study of different computational DFT approaches has been performed and analyzed. For indirect gap the pressure coefficient have been determined theoretically to be −7.9, −5.51, −6.11, and −3.79, meV/kbar for MoS₂, MoSe₂, WS₂, and WSe₂, respectively. The negative values of this coefficients imply a narrowing of the fundamental band gap with the increase in hydrostatic pressure and a semiconductor to metal transition for MoS₂, MoSe₂, WS₂, and WSe₂, crystals at around 140, 180, 190, and 240 kbar, respectively.

Binary transition metal dichalcogenide monolayers share common properties such as a direct optical bandgap, spin-orbit splittings of hundreds of meV, light–matter interaction dominated by robust excitons and coupled spin-valley states. Here we demonstrate spin-orbit-engineering in Mo[_(1-x)]W_xSe₂ alloy monolayers for optoelectronics and applications based on spin- and valley-control. We probe the impact of the tuning of the conduction band spin-orbit spin-splitting on the bright versus dark exciton population. For MoSe₂ monolayers, the photoluminescence intensity decreases as a function of temperature by an order of magnitude (4–300 K), whereas for WSe₂ we measure surprisingly an order of magnitude increase. The ternary material shows a trend between these two extreme behaviors. We also show a non-linear increase of the valley polarization as a function of tungsten concentration, where 40% tungsten incorporation is sufficient to achieve valley polarization as high as in binary WSe₂.