Actual Profile Research Articles

Machine learning based energy demand prediction

Energy demand prediction can benefit electricity consumers, distribution network service providers, and system operators. It heavily depends on auxiliary factors, such as weather parameters (e.g., ambient temperature), which makes this problem more complex. Moreover, for many industrial applications, instead of aiming for the highest prediction accuracy, a more easily understandable and interpretable model that can lead to higher accuracy against the baseline model is the priority. Therefore, this problem still requires more investigation, especially when there is a specified prediction baseline to be compared with. This paper proposes a machine learning (ML) based prediction framework that investigates how temperature combined with energy consumption and simple and interpretable ML methods can be used to provide more precise demand forecasts and thus baselines closer to actual load profiles. The proposed framework is tested on two different real-world energy demand datasets. The analysis shows that using a simple ML model, such as a polynomial regression model, results in a more accurate prediction than the current baselines used in the energy market. The proposed ML models are not black-box type models, and thus are easier to explain and interpret. The ML-based forecasted demand is used as a baseline for demand response (DR) and is compared with the existing baselines used in the demand response market of Australia’s national grid.

Considering field topography in the model-based assessment of a centrifugal spreader’s variable rate application accuracy

In the application of solid mineral fertilizers with twin-disc spreaders, accurate delivery of the prescribed rates is highly dependent on their dynamic performance. Additional to the effect of imprecision in geolocations and the delayed reaction of the spreader to the rate updates at the delineations of the task map, the field surface with inclined hills and undulating waves significantly influences the field application accuracy. In this paper, the effect of the determined field topography on application accuracy has been assessed. Necessary sensors such as the prism of a Total Station and an IMU (Inertial Measurement Unit) have been attached to a mobile sled to acquire the geo-referenced positions together with its real-time tilting information while following the actual field profile. A model for the spreader performance on a flat field surface, which had been developed by the authors in previous work, was improved by incorporating the measured tilt information corresponding to the measured field conditions. In addition, the improved model used the measured topography with inclinations and undulations to determine the landing location of each fertilizer particle. The influence of the determined field inclinations and undulations on the stationary spreading was assessed by comparing the distributions of the fertilizers in the transversal direction that are extracted from the spreading on the flat and measured field surfaces. The comparative analysis of the distributions indicated a significantly high value of 27.8% for a root-mean-squared deviation. The subsequent assessment of the actual field topography’s effect on the spreader’s dynamic performance showed that the measured field surface conditions have a significant effect on the application accuracy in terms of absolute deviation, with approx. 5% under- and approx. 3% over-application of the maximum prescribed amount.

Generating realistic load profiles in smart grids: An approach based on nonlinear independent component estimation (NICE) and convolutional layers

The utilization of energy consumption data is crucial for efficient operation and planning in smart grids. Nonetheless, certain obstacles need to be addressed, such as high computational costs, data security and privacy concerns, and significant expenses associated with installing smart meters across the electrical grid. To address these challenges, generating synthetic data has emerged as a promising approach, providing an opportunity to enhance energy efficiency, demand flexibility, and power grid operation. Therefore, this study proposes a nonlinear model of independent component estimation (NICE) with convolutional layers to produce realistic load profiles. This research aims to evaluate the potential of deep generative models (DGMs) through the characterization and quantification of electricity consumption profiles obtained from an actual smart grid on a university campus. The Kullback–Leibler divergence is used to evaluate the performance of the proposed model. Simulation results show that the proposed model can accurately capture the spatiotemporal correlation of actual samples, leading to synthetic load profiles that closely resemble actual profiles. The performance of the proposed NICE model is compared with a NICE model with dense layers, as well as with Generative Adversarial Networks (GAN) with dense layers, and GAN with convolutional layers (cGAN), all methods previously used in the literature to generate synthetic load profiles. It was observed that the proposed NICE model with convolutional layers leads to better results. This model produces more significant similarity between the probability distributions of actual and synthetic data, in addition to a more extraordinary ability to reproduce more realistic load variability curves.

The Effects of Online Social Identity Signals on Retailer Demand

Recent shifts in societal discourse have led digital platforms to support equity, inclusivity, and diversity by introducing identity-signaling features, for example, indicators of owner race or gender. In this work, we explore whether, when, and how using those features may impact retailer demand. We tackle this question via a multimethod study. We begin by conducting a controlled experiment on Prolific.co , presenting subjects with actual Google Places business profiles for a set of Black-owned restaurants in Chicago. We randomly vary the presence of Black- and women-owned labels in these profiles and assess subjects’ expectations of popularity and quality along various restaurant dimensions. Our results demonstrate that the Black-owned label, in particular, drives significant increases in all outcomes with the effects arising primarily from Black and democratically liberal subjects. Next, we conduct an archival analysis of the effect label adoption has on the physical foot traffic that retailers receive based on SafeGraph’s mobile patterns database. Our difference-in-differences estimations yield consistent results; we find evidence of a positive average effect on foot traffic volumes. Further, we show that a rise in visitors from liberal-leaning geographies drives these effects. We discuss implications for digital platform operators and retailers. This paper was accepted by Duncan Simester, marketing. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.01168 .

Mechanism that determines the economics of 100% renewable power systems

Many studies have evaluated the economic feasibility of 100% renewable power systems using the optimization approach, but the mechanisms determining the results remain unclear, making this issue still debatable. This study presents a mathematical formulation of the mechanism that only the demand and power generation profiles determine the optimal capacities of generation and storage and their trade-off relationship. Furthermore, this study demonstrates the comprehensive quantification of the corresponding relationships among the factor cost of technologies, their optimal capacities, and total system cost. Based on these findings, the study also shows that hybrid systems comprising multiple renewable energy sources and different types of storage, including long-duration energy storage, are critical to reducing the total system cost by using actual profile data for multiple years and regions in Japan. This suggests that large-scale deployment of current-level power-to-gas technologies, such as water electrolysis, can contribute to the economics of 100% renewable power systems.

FUNWAVE MODELLING OF BEACH INUNDATION IN RESPONSE TO MASS SAND NOURISHMENT: A STUDY USING SOME PROFILES FROM STOCKTON BEACH NSW

Mass sand nourishment is recognized as a means of restoring beach amenity and one that possibly offers long-term protection from further shoreline loss and inundation of adjacent urban areas. While large scale beach nourishment has been used elsewhere, examples in NSW are extremely limited. Model inundation results, based on an actual beach profile, suggest that nourishment alone provides little reduction to inundation rates and therefore inundation risks.

Energy-resolved pulse profiles of accreting pulsars: Diagnostic tools for spectral features

Aims. We introduce a method for extracting spectral information from energy-resolved light curves folded at the neutron star spin period (known as pulse profiles) in accreting X-ray binaries. Spectra of these sources are sometimes characterized by features superimposed on a smooth continuum, such as iron emission lines and cyclotron resonant scattering features. We address here the question on how to derive quantitative constraints on such features from energy-dependent changes in the pulse profiles. Methods. We developed a robust method for determining in each energy-selected bin the value of the pulsed fraction using the fast Fourier transform opportunely truncated at the number of harmonics needed to satisfactorily describe the actual profile. We determined the uncertainty on this value by sampling through Monte Carlo simulations a total of 1000 faked profiles. We rebinned the energy-resolved pulse profiles to have a constant minimum signal-to-noise ratio throughout the whole energy band. Finally we characterize the dependence of the energy-resolved pulsed fraction using a phenomenological polynomial model and search for features corresponding to spectral signatures of iron emission or cyclotron lines using Gaussian line profiles. Results. We apply our method to a representative sample of NuSTAR observations of well-known accreting X-ray pulsars. We show that, with this method, it is possible to characterize the pulsed fraction spectra, and to constrain the position and widths of such features with a precision comparable with the spectral results. We also explore how harmonic decomposition, correlation, and lag spectra might be used as additional probes for detection and characterization of such features.

Experimental and numerical investigation of a single-phase microchannel flow under axially non-uniform heat flux

The design of microchannel based heat sinks for advanced electronic component continues to receive research interest, especially with the ever-increasing density of advanced chips with concomitant increase in heat generation. One aspect of microchannel heat sink design objectives is the optimum employment of non-uniform heat dissipation through the channel length. Two dominant schools in the performance optimization of non-uniform heat sinks are: 1) entropy minimization and 2) thermal resistance minimization. These two approaches usually result in contradictory conclusions: one recommending the use of increasing heat flux, the other recommending a decreasing heat flux. The current study seeks to explain this discrepancy by experimentally and numerically studying the heat transfer mechanisms in a single microchannel tube under the effect of non-uniform heat flux. This study presents an experimental framework capable of generating a wide range of pre-specified heat flux profiles over a single microchannel, mimicking actual heat flux profiles observed in thermo-electronic devices. We employ three flux profiles: 1) hotspots located at different locations with uniform background heat flux, 2) linearly ascending heat flux, and 3) linearly descending heat flux. The results show good agreement between numerical simulations and experimental measurements and provide insights into the microchannel tube's fundamental heat transfer mechanisms. Based on these insights, the study provides design guidelines to enhance microchannel heat sink performance under non-uniform axial heat fluxes. Finally, the discrepancy between entropy and heat resistance minimization approaches is explained.

The influence of ship stability in waves on naval vessel operational profiles

Ensuring a widely safe operational profile of naval vessels is one of the leading aspects of the design process. With reference to weather conditions, this kind of vessels often cannot avoid heavy situations to guarantee the continuity and the effectiveness of service. For this reason, an applied research investigation aimed to define guidance in operations is deemed as a worthwhile activity. In particular, the performance in waves under the perspective of intact stability has been considered in this work. The IMO second-generation intact stability criteria have been identified as a suitable tool for these investigations: as a result, there is evidence that operational guidance can be developed for a set of representative naval vessels, following the relevant guidelines. Outcomes have been represented by means of comprehensive polar diagrams addressing the intact stability failures modes. Bearing in mind the actual operational profile, results have been analysed and discussed considering how ship and operating decisions in a seaway condition may affect the performance in terms of ship stability.

High‐resolution reservoir stochastic modelling based on optimized estimation of vertical autocorrelation

AbstractThe high‐resolution model of elastic properties is of great significance for fine reservoir characterization and precise oil and gas exploration. However, it is difficult to obtain a satisfactory high‐resolution reservoir model with the existing technologies. In this paper, a novel high‐resolution stochastic modelling strategy based on the fast Fourier transform moving average is proposed. In this strategy, several structural parameters are optimized to improve the rationality of the stochastic model, including vertical autocorrelation length, horizontal autocorrelation length, roughness factor and angle parameter. Among them, the optimization of the vertical autocorrelation length is crucial for vertical high‐resolution modelling. To this end, a nonlinear optimal inversion strategy of the vertical autocorrelation length is designed based on the idea of minimizing the spectral Jensen–Shannon divergence between the modelling result and the logging curve. However, nonlinear inversion is usually unstable, so it is necessary to introduce a regularization operator in the inversion to improve the stability. Considering that the heterogeneity of the subsurface medium is consistent or gradual within the stratums, but discontinuous and abrupt at the interfaces, edge‐preserving regularization is applied to obtain a blocky estimation of the vertical autocorrelation length. The optimal estimation experiment of the vertical autocorrelation length based on the measured logging data shows that the edge‐preserving regularization significantly improves the stability of the nonlinear optimal inversion, and blocky estimation results with sharp edges are obtained. Then, the optimized vertical autocorrelation length and the other structural parameters are applied to fast Fourier transform moving average modelling on an actual reservoir profile. The result shows that the resolution of the model is significantly improved, which realizes the fine reservoir characterization. In addition, the optimized structural parameters effectively constrain the small‐scale heterogeneity and ensure the rationality of the stochastic model.

DEVELOPMENT OF MANAGERIAL COMPETENCE OF HEADS OF EDUCATIONAL INSTITUTIONS IN CRISIS CONDITIONS

In the crisis conditions of challenges and restrictions caused by the Covid-19 pandemic and the martial law in Ukraine, it is important to ensure stability and efficiency in the management of educational institutions. It is possible due to the condition of development of managerial competence of managers. We consider the managerial competence of educational institutions leaders is a personal phenomenon. It combines psychological, theoretical and practical readiness for managerial activities in the specific conditions of the institution's existence. For the development of managerial competence, appropriate conditions are important, as well as an actual profile, the structure of which includes: development orientation; emotional intelligence; cooperation; ability to create a safe environment; organizational capacity. As a result of the actualization of the crisis situation, the public demand for managers-leaders is increasing, hence the need to analyze, develop a theory and use leadership strategies by the heads of general secondary education institutions, since they are the basis of the manifestation of their managerial competence in crisis conditions. Our research was organized during 2018/2021 (n 112.) It showed a contradiction between the need for development and the lack of managerial competence and leadership strategies among managers in the field of education. In order to develop this property, a program of psychological and pedagogical support was developed and implemented. The results of the program's implementation confirmed its effectiveness and made it possible to conclude that the development of managerial competence can effectively take place within the limits of individual trajectories of professional growth of heads of institutions in the field of education.

Effects of thickness profile and inner/outer of localized thin area defects on the remaining strength of pressure equipment

When encountering pressure equipment with a structural defect such as a local thinning defect (LTA), a critical question often arises regarding the safety of continued operation. This is primarily determined by the strength at the LTA zone, as it represents the weakest point of the structure. The remaining strength factor (RSF) is proposed as an indicator to evaluate the remaining strength and life of pressure equipment when a severe LTA is identified. The API 579/ASME FFS standard offers a series of practical engineering approaches for field engineers to follow, which have achieved great success. However, it has been found that the simpler Level 1 and Level 2 approaches sometimes fail to differentiate between LTAs on the inner or outer surface of the pressure equipment, yielding identical numerical processes and results. Moreover, the use of critical thickness profiles to replace the actual thickness profile in analysis can lead to overly optimistic estimation of the RSF, posing a potential danger when the RSF is close to the allowable limit. Key issues investigated in this study include: (a) discrepancies caused by the use of simplified critical thickness profiles or parabolic profiles, as API 579 suggested, on the RSF; (b) differences in RSFs when the same LTA is on the inner and outer wall; and (c) comparisons of membrane stress and bending stress, as well as their influences on the RSF at the cross-section of the vessel wall. It can be concluded with certainty that all simplified geometries (CTP and PTP) tend to underestimate the RSF and should be used with caution. Additionally, the actual remaining strength of the outer LTA was found to be slightly lower than that of the inner LTA of identical size. Therefore, when remaining life and derating for prolonged operation are of interest, finite element analysis on pressure equipment with the LTA is recommended.

A Novel Data-Driven Model for the Effect of Mood State on Thermal Sensation

Thermal comfort has an important role in human life, considering that people spend most of their lives in indoor environments. However, the necessity of ensuring the thermal comfort of these people presents an important problem, calculating the thermal comfort accurately. The assessment of thermal comfort has always been problematic, from past to present, and the studies conducted in this field have indicated that there is a gap between thermal comfort and thermal sensation. Although recent studies have shown an effort to take human psychology into account more extensively, these studies just focused on the physiological responses of the human body under psychological disturbances. On the other hand, the mood state of people is one of the most significant parameters of human psychology. Thus, this paper investigated the effect of occupants’ mood states on thermal sensation; furthermore, it introduced a novel “Mood State Correction Factor” (MSCF) to the existing thermal comfort model. To this aim, experiments were conducted at a mixed-mode building in a university between 15 August 2021 and 15 August 2022. Actual Mean Vote (AMV) and Profile of Mood States (POMS) were used to examine the effect of mood state on thermal sensation. The outcomes of this study showed that in the mood states of very pessimistic and very optimistic, the occupants felt warmer than the calculated one and the MSCFs are calculated as −0.125 and −0.114 for the very pessimistic and very optimistic mood states, respectively. It is worth our time to note that the experiments in this study were conducted during the COVID-19 Global Pandemic and the results of this study could differ in different cultural backgrounds.

Optimum Design of an Electric Vehicle Charging Station Using a Renewable Power Generation System in South Korea

In the context of global warming and fossil fuel depletion, electric vehicles (EVs) have become increasingly popular for reducing both carbon emissions and fossil fuel consumption. However, as the demand for EV charging power rises along with the expansion of EVs, conventional power plants require more fuel, and carbon emissions increase. This suggests that the goal of promoting EV adoption to mitigate climate change and reduce reliance on fossil fuels may face significant challenges. Therefore, there is a need to adopt renewable energy generation for EV charging stations to maximize the effectiveness of EV distribution in an eco-friendly way. This paper aims to propose an optimal renewable energy generation system for an EV charging station, with a specific focus on the use of an actual load profile for the station, the consideration of carbon emissions and economic evaluation, and the study of a specific case location in Korea. As a case study, an EV charging station in Korea was selected, and its renewable energy fractions (REF) of 0%, 25%, 50%, 75%, and 100% were considered for comparison of carbon emissions and economic evaluation with the help of HOMER software. In addition, the system with 25% REF was analyzed to find the best operating strategy considering the climate characteristics of the case site. The results show that the system configuration of PV/ESS is the most economical among all the REF cases, including PV, WT, and ESS, due to the meteorological characteristics of the site, and that the system with REF below 25% is the most optimal in economic terms and carbon emissions.

Phycotoxins in bivalves from the western Canadian Arctic: The first evidence of toxigenicity

This study presents the first evidence that a diverse suite of phycotoxins is not only being actively produced by the toxigenic algal communities in the Canadian Arctic waters, but is also entering the marine food web. We detected measurable amounts of Amnesic Shellfish Toxins (ASTs) and Paralytic Shellfish Toxins (PSTs), as well as trace amounts of other lipophilic toxin groups including pectenotoxins, yessotoxins, and cyclic imines, in bivalves collected from the Canadian Beaufort Sea in 2014 and 2018. There appear to be species-specific differences in accumulation and retention of AST by Arctic bivalves, with significantly higher concentrations recorded in Nuculanidae than Propeamussiidae, likely reflecting physiological and allometric differences. We further confirm the omnipresence of potentially toxic taxonomically-versatile phytoplankton communities in the western Canadian Arctic comprising Pseudo-nitzschia delicatissima group, P. obtusa, Dinophysis acuminata, Prorocentrum minimum, Alexandrium tamarense, and Gymnodinium spp. Although measurements of actual toxicity levels and profiles of these species at the time of sampling fall outside of the scope of this study, we show that high abundance and competitive success of known AST-producers, Pseudo-nitzschia spp., are possible in Canadian Arctic waters. In 2014, a strong dominance of Pseudo-nitzschia spp. was observed at a few shallow coastal stations, representing nearly 40% of the total phytoplankton cell abundances with > 106 cells/L at the depth of maximum chlorophyll a. We further describe oceanographic conditions conducive to high abundances of toxin-producing algae, indicating that temperature is likely a key factor. Even though measured AST and PST concentrations in bivalve tissue remained well below the Health Canada's levels at which monitored fisheries would close, i.e., 5% and 4%, respectively, their presence demonstrate that phycotoxin accumulation is occurring in food webs of the Canadian Beaufort Sea. Yet, the phycotoxin production controls and trophic transfer mechanisms remain unknown. Canadian Arctic marine ecosystems are rapidly changing and temperatures are expected to continue to increase. Given that these changes simultaneously affect multiple, and often co-occurring, species of primary producers, adaptive capacity is likely to play an important role in the structure of phytoplankton communities in the Canadian Arctic.

Development of a New Model for the Formation of Wax Deposits through the Passage of Crude Oil within the Well

Wax deposits related to flow assurance are a costly problem in oil production in many fields around the world. Modeling of this process is the main tool for creating and optimizing methods to deal with this problem. This paper considers a new empirical model for the formation of these deposits, based on the results of an array of laboratory studies, theoretical data and technological calculations are presented. The created technique takes into account the conditions of oil flow, data from laboratory studies, and the water cut of the product. The experience of the industrial operation of the technique showed a high convergence of the calculated and actual deposit profiles. Based on a comparison of the calculated and actual deposit profiles, it was concluded that the standard deviation of maximum wax thickness is 6.0%, and the depth with the greatest wax thickness is 3.5%, which is a fairly high result. The use of this technique makes it possible to optimize the depth of mechanical cleaning of the well, the installation of heating cables, as well as the parameters of hot flushing, which increases their efficiency and reduces the cost of combating the formation of wax deposition.

Supply–demand scenario generation of active distribution systems using conditional generative adversarial networks

The intermittency of distributed renewable energy resources (DRERs) and the fluctuation of load demand make the supply–demand scenario of active distribution systems significantly different from that of traditional radial distribution systems. Scenario generation is an effective tool for describing supply–demand uncertainty and is therefore of great significance for the planning and operation of active distribution systems with a high penetration of DRERs. This paper proposes a supply–demand scenario generation method for active distribution systems using conditional generation adversarial networks (CGANs). The proposed method is data-driven and utilizes neural networks to learn and represent distribution laws from data without revealing the expression probability distribution. Specifically, it first extracts the features information of historical scenarios as the conditional control vectors (CCVs), then embeds the CCVs to the input of the generator network and discriminator network to achieve conditional control. The proposed approach is verified by the actual time series wind power data and load profiles. Results show that the proposed method can efficiently generate supply–demand scenarios satisfying various statistical characteristics and capture complex spatial–temporal correlations.

Simultaneous and Global Optimizations of LNG-Fueled Hybrid Electric Ship for Substantial Fuel Cost, CO2, and Methane Emission Reduction

Natural gas (NG) is a promising low-carbon fuel to replace diesel for heavy-duty marine propulsion with prominent fuel cost and carbon dioxide equivalent (CDE) emissions reduction potential. However, using NG in compression ignition (CI) engines presents several inherent drawbacks. This research addresses the central issue of an NG-diesel CI engine with increased methane emissions, resulting in minor CDE emission reduction. The hydrocarbon (HC), carbon monoxide (CO), and nitrogen oxides (NOx) emissions are minimized in addition to fuel consumption and carbon dioxide emission reduction as in the current practice. The simultaneous optimizations of powertrain component sizes and the NG-engine hybrid electric propulsion control are introduced to minimize NG fuel consumption and CDE emissions globally. The top-level powertrain component sizing and bottom-level optimal power control and energy management are conducted simultaneously to achieve global optimization with balanced fuel cost and CDE emissions reductions. The new method reduced the fuel cost by about 75 percent and well-to-wake CDE emissions by about 40 percent over the traditional diesel-mechanical propulsion for the test ferry running under an actual operation profile. The research opened a new path to the global design and control optimization of the NG-diesel CI engine-powered hybrid electric marine propulsion system.

Capacitively coupled contactless conductivity detection to account for system-induced gradient deformation in liquid chromatography

The time required for method development in gradient-elution liquid chromatography (LC) may be reduced by using an empirical modelling approach to describe and predict analyte retention and peak width. However, prediction accuracy is impaired by system-induced gradient deformation, which can be especially prominent for steep gradients. As the deformation is unique to each LC instrument, it needs to be corrected for if retention modelling for optimization and method transfer is to become generally applicable. Such a correction requires knowledge of the actual gradient profile. The latter has been measured using capacitively coupled “contactless” conductivity detection (C4D), featuring a low detection volume (approximately 0.05 μL) and compatibility with very high pressures (80 MPa or more). Several different solvent gradients, from water to acetonitrile, water to methanol, and acetonitrile to tetrahydrofuran, could be measured directly without the addition of a tracer component to the mobile phase, exemplifying the universal nature of the approach. Gradient profiles were found to be unique for each solvent combination, flowrate, and gradient duration. The profiles could be described by convoluting the programmed gradient with a weighted sum of two distribution functions. Knowledge of the exact profiles was used to improve the inter-system transferability of retention models for toluene, anthracene, phenol, emodin, sudan-I and several polystyrene standards.

Accurate prediction of machining cycle times and feedrates with deep neural networks using BiLSTM

This paper presents a deep neural network-based approach to predict machining cycle (run) times along complex 3-axis machining part programs. CAD/CAM systems are utilized to generate part programs to machine parts with complex geometries and sculptured surfaces. The part program is processed by the numerical control (NC) unit of the machine tool to plan a feed profile. For such complex part programs, the actual (observed) feed profile and commanded (desired) one are significantly different. This paper uses bi-directional long short-term memory deep networks (bi-LSTM) to model the interpolation behavior of the NC systems to accurately predict how the feedrate profile is planned for any given part program. The G-line length, commanded feed, change in the feed direction through consecutive G-lines are used as a primary set of inputs to the network. The neural network is then trained by running series of toolpaths on the NC system and recording the resultant feedrate profile. Simulation and experimental studies are conducted on NC kernels to validate effectiveness of the proposed strategy. It is shown that machining cycle times on complex toolpaths can be predicted with > 94 % accuracy.