Records with Subject: Numerical Methods and Statistics
Showing records 1 to 25 of 1497. [First] Page: 1 2 3 4 5 Last
Simulating Power Generation from Photovoltaics in the Polish Power System Based on Ground Meteorological Measurements—First Tests Based on Transmission System Operator Data
Jakub Jurasz, Marcin Wdowikowski, Mariusz Figurski
March 31, 2023 (v1)
Keywords: artificial neural networks, national power system, photovoltaics
The Polish power system is undergoing a slow process of transformation from coal to one that is renewables dominated. Although coal will remain a fundamental fuel in the coming years, the recent upsurge in installed capacity of photovoltaic (PV) systems should draw significant attention. Owning to the fact that the Polish Transmission System Operator recently published the PV hourly generation time series in this article, we aim to explore how well those can be modeled based on the meteorological measurements provided by the Institute of Meteorology and Water Management. The hourly time series of PV generation on a country level and irradiation, wind speed, and temperature measurements from 23 meteorological stations covering one month are used as inputs to create an artificial neural network. The analysis indicates that available measurements combined with artificial neural networks can simulate PV generation on a national level with a mean percentage error of 3.2%.
Selection of Temporal Lags for Predicting Riverflow Series from Hydroelectric Plants Using Variable Selection Methods
Hugo Siqueira, Mariana Macedo, Yara de Souza Tadano, Thiago Antonini Alves, Sergio L. Stevan Jr, Domingos S. Oliveira Jr, Manoel H.N. Marinho, Paulo S.G. de Mattos Neto,  João F. L. de Oliveira, Ivette Luna, Marcos de Almeida Leone Filho, Leonie Asfora Sarubbo, Attilio Converti
March 31, 2023 (v1)
Keywords: autoregressive model, bio-inspired metaheuristics extreme learning machines neural networks, monthly forecasting, wrapper
The forecasting of monthly seasonal streamflow time series is an important issue for countries where hydroelectric plants contribute significantly to electric power generation. The main step in the planning of the electric sector’s operation is to predict such series to anticipate behaviors and issues. In general, several proposals of the literature focus just on the determination of the best forecasting models. However, the correct selection of input variables is an essential step for the forecasting accuracy, which in a univariate model is given by the lags of the time series to forecast. This task can be solved by variable selection methods since the performance of the predictors is directly related to this stage. In the present study, we investigate the performances of linear and non-linear filters, wrappers, and bio-inspired metaheuristics, totaling ten approaches. The addressed predictors are the extreme learning machine neural networks, representing the non-linear approaches, an... [more]
A Bibliometric Analysis of Carbon Labeling Schemes in the Period 2007−2019
Rui Zhao, Dingye Wu, Sebastiano Patti
March 31, 2023 (v1)
Keywords: bibliometric analysis, carbon label, carbon labeling scheme, CiteSpace, purchase intention, willingness to pay
Carbon labeling schemes enable consumers to be aware of carbon emissions regarding products or services, to help change their purchasing behaviors. This study provides a bibliometric analysis to review the research progress of carbon labeling schemes during the period 2007−2019, in order to provide insight into its future development. Number of publications, countries of publications, authors, institutions, and highly cited papers are included for statistical analysis. The CiteSpace software package is used to visualize the national collaboration, keywords co-appearance, and aggregation. The results are given as follows: (1) there are 175 articles published in the pre-defined period, which shows a gradual increase, with a peak occurred in 2016; (2) carbon labeling schemes are mainly applied to grocery products, and gradually emerged in construction and tourism. (3) Existing studies mainly focus on examination of utility of carbon labeling schemes, by conducting surveys to investigate i... [more]
Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods
Ahmad Almaghrebi, Fares Aljuheshi, Mostafa Rafaie, Kevin James, Mahmoud Alahmad
March 31, 2023 (v1)
Keywords: charging behavior, charging demand, data-driven, Machine Learning, Plug-in Electric Vehicle, public charging stations
Plug-in Electric Vehicle (PEV) user charging behavior has a significant influence on a distribution network and its reliability. Generally, monitoring energy consumption has become one of the most important factors in green and micro grids; therefore, predicting the charging demand of PEVs (the energy consumed during the charging session) could help to efficiently manage the electric grid. Consequently, three machine learning methods are applied in this research to predict the charging demand for the PEV user after a charging session starts. This approach is validated using a dataset consisting of seven years of charging events collected from public charging stations in the state of Nebraska, USA. The results show that the regression method, XGBoost, slightly outperforms the other methods in predicting the charging demand, with an RMSE equal to 6.68 kWh and R2 equal to 51.9%. The relative importance of input variables is also discussed, showing that the user’s historical average demand... [more]
Air Temperature Forecasting Using Machine Learning Techniques: A Review
Jenny Cifuentes, Geovanny Marulanda, Antonio Bello, Javier Reneses
March 31, 2023 (v1)
Keywords: air temperature forecasting, Artificial Intelligence, Machine Learning, neural networks, support vector machines
Efforts to understand the influence of historical climate change, at global and regional levels, have been increasing over the past decade. In particular, the estimates of air temperatures have been considered as a key factor in climate impact studies on agricultural, ecological, environmental, and industrial sectors. Accurate temperature prediction helps to safeguard life and property, playing an important role in planning activities for the government, industry, and the public. The primary aim of this study is to review the different machine learning strategies for temperature forecasting, available in the literature, presenting their advantages and disadvantages and identifying research gaps. This survey shows that Machine Learning techniques can help to accurately predict temperatures based on a set of input features, which can include the previous values of temperature, relative humidity, solar radiation, rain and wind speed measurements, among others. The review reveals that Deep... [more]
A Method of DC Arc Detection in All-Electric Aircraft
Teng Li, Zhijie Jiao, Lina Wang, Yong Mu
March 29, 2023 (v1)
Keywords: arc fault detection, convolutional neural network, discrete wavelet transform, more electric aircraft, time–frequency analysis
Arc faults in an aircraft’s power distribution system (PDS) often leads to cable and equipment damage, which seriously threatens the personal safety of the passengers and pilots. An accurate and real-time arc fault detection method is needed for the Solid-State Power Controller (SSPC), which is a key protection equipment in a PDS. In this paper, a new arc detection method is proposed based on the improved LeNet5 Convolutional Neural Network (CNN) model after a Time−Frequency Analysis (TFA) of the DC currents was obtained, which makes the arc detection more real-time. The CNN is proposed to detect the DC arc fault for its advantage in recognizing more time−frequency joint details in the signals; the new structure also combines the adaptive and multidimensional advantages of the TFA and image intelligent recognition. It is confirmed by experimental data that the combined TFA−CNN can distinguish arc faults accurately when the whole training database has been repeatedly trained 3 to 5 time... [more]
A Method for Predicting the Remaining Useful Life of Lithium-Ion Batteries Based on Particle Filter Using Kendall Rank Correlation Coefficient
Diju Gao, Yong Zhou, Tianzhen Wang, Yide Wang
March 29, 2023 (v1)
Keywords: lithium-ion battery, NARX neural network, particle filter (PF), remaining useful life (RUL)
With the wide application of lithium batteries, battery fault prediction and health management have become more and more important. This article proposes a method for predicting the remaining useful life (RUL) of lithium-ion batteries to avoid a series of safety problems caused by continuing to use the battery after reaching its service life threshold. Since the battery capacity is not easy to obtain online, we propose that some measurable parameters should be used in the battery discharge cycle to estimate battery capacity. Then, the estimated capacity is used to replace the measured value of the particle filter (PF) based on the Kendall rank correlation coefficient (KCCPF) to predict the RUL of the lithium batteries. Simulation results show that the proposed method has high prediction accuracy, stability, and practical value.
Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network
Anthony Faustine, Lucas Pereira
March 29, 2023 (v1)
Keywords: activation current, appliance recognition, Convolutional Neural Network, distance similarity matrix, fryze power theory, multi-label learning, Non-intrusive Load Monitoring, V-I trajectory
The advance in energy-sensing and smart-meter technologies have motivated the use of a Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end-use appliances by analyzing the data streams coming from these devices. NILM offers an electricity consumption pattern of individual loads at consumer premises, which is crucial in the design of energy efficiency and energy demand management strategies in buildings. Appliance classification, also known as load identification is an essential sub-task for identifying the type and status of an unknown load from appliance features extracted from the aggregate power signal. Most of the existing work for appliance recognition in NILM uses a single-label learning strategy which, assumes only one appliance is active at a time. This assumption ignores the fact that multiple devices can be active simultaneously and requires a perfect event detector to recognize the appliance. In this paper proposes the Convolutional Neural... [more]
Why Should We Use Residual Thermodynamics for Calculation of Hydrate Phase Transitions?
Bjørn Kvamme, Jinzhou Zhao, Na Wei, Wantong Sun, Mojdeh Zarifi, Navid Saeidi, Shouwei Zhou, Tatiana Kuznetsova, Qingping Li
March 29, 2023 (v1)
Keywords: hydrate, phase transitions, statistical mechanics, thermodynamic properties
The formation of natural gas hydrates during processing and transport of natural has historically been one of the motivations for research on hydrates. In recent years, there has been much focus on the use of hydrate as a phase for compact transport of natural gas, as well as many other applications such as desalination of seawater and the use of hydrate phase in heat pumps. The huge amounts of energy in the form of hydrates distributed in various ways in sediments is a hot topic many places around the world. Common to all these situations of hydrates in nature or industry is that temperature and pressure are both defined. Mathematically, this does not balance the number of independent variables minus conservation of mass and minus equilibrium conditions. There is a need for thermodynamic models for hydrates that can be used for non-equilibrium systems and hydrate formation from different phase, as well as different routes for hydrate dissociation. In this work we first discuss a resid... [more]
Numerical Investigation of PEMFC Short-Circuit Behaviour Using an Agglomerate Model Approach
Carsten Cosse, Marc Schumann, Florian Grumm, Daniel Becker, Detlef Schulz
March 29, 2023 (v1)
Keywords: agglomerate model, pemfc, short-circuit, transient behaviour
With increasing interest in clean energy generation in the transportation sector, increasing attention has been given to polymer-electrolyte-membrane fuel cells as viable power sources. One issue, the widespread application of this technology faces, is the insufficient knowledge regarding the transient behaviour of fuel cells, for instance, following a short-circuit event. In this paper, an agglomerate model is presented and validated, which enables the transient simulation of short-circuit events to predict the resulting peak current and discharge of the double layer capacity. The model allows for the incorporation of detailed morphological and compositional information regarding all fuel cell components. This information is used to calculate the reaction rate, diffusional and convectional species transfer, and the momentum transport. It can be shown that the charge in the double layer capacitance of the fuel cell is key to predicting the peak current and its charge is dependent on th... [more]
Partial Discharge Pattern Recognition Based on 3D Graphs of Phase Resolved Pulse Sequence
Simeng Song, Yong Qian, Hui Wang, Yiming Zang, Gehao Sheng, Xiuchen Jiang
March 29, 2023 (v1)
Keywords: histogram of oriented gradient, partial discharge, pattern recognition, phase resolved pulse sequence
Partial discharge (PD) is an important phenomenon that reflects the insulation condition of electrical equipment. In order to protect the safety of power grids, it is of significance to diagnose the type of insulation defects inside the equipment accurately and early through PD pattern recognition. In this article, phase resolved pulse sequence (PRPS) graphs in 3D were constructed by the PD pulse data of the gas-insulated switchgear (GIS) acquired, then the histogram of oriented gradient (HOG) features were extracted directly from the 3D PRPS graphs, and finally the attribute selective Naïve Bayes classifier was used to recognize the discharge pattern. In addition, this method was compared with two traditional methods, i.e., the statistical method and the grayscale gradient co-occurrence matrix method, from three aspects. The result shows that 3D PRPS graphs have different morphology characteristics in vision under different defects, and the similarity among different voltages applied... [more]
Modeling Three-Dimensional Anisotropic Structures of Reservoir Lithofacies Using Two-Dimensional Digital Outcrops
Yiming Yan, Liqiang Zhang, Xiaorong Luo
March 29, 2023 (v1)
Keywords: Junggar Basin, multiple-point geostatistics, parametric sensitivity analysis, Qingshuihe formation, reservoir lithofacies anisotropic structures model, simulation sequence, training image
Reservoir heterogeneity is a key geological problem that restricts oil and gas exploration and development of clastic rocks from the early to late stages. Existing reservoir heterogeneity modeling methods such as multiple-point geostatistics (MPS) can accurately model the two-dimensional anisotropic structures of reservoir lithofacies. However, three-dimensional training images are required to construct three-dimensional reservoir lithofacies anisotropic structures models, and the method to use reservoir heterogeneity model of fewer-dimensional to obtain a three-dimensional model has become a much-focused research topic. In this study, the outcrops of the second member of Qingshuihe Formation (K1q2) in the northwestern margin of the Junggar Basin, which are lower cretaceous rocks, were the research target. The three-dimensional reservoir heterogeneity model of the K1q2 outcrop was established based on the unmanned aerial vehicle (UAV) digital outcrops model and MPS techniques, and the... [more]
Source Diagnosis of Solid Oxide Fuel Cell System Oscillation Based on Data Driven
Xiaowei Fu, Yanlin Liu, Xi Li
March 29, 2023 (v1)
Keywords: data-driven, Granger causality, kernel principal component analysis, oscillation root cause diagnosis, solid oxide fuel cell, transfer entropy
The solid oxide fuel cell (SOFC) is a new energy technology that has the advantages of low emissions and high efficiency. However, oscillation and propagation often occur during the power generation of the system, which causes system performance degradation and reduced service life. To determine the root cause of multi-loop oscillation in an SOFC system, a data-driven diagnostic method is proposed in this paper. In our method, kernel principal component analysis (KPCA) and transfer entropy were applied to the system oscillation fault location. First, based on the KPCA method and the Oscillation Significance Index (OSI) of the system process variable, the process variables that were most affected by the oscillations were selected. Then, transfer entropy was used to quantitatively analyze the causal relationship between the oscillation variables and the oscillation propagation path, which determined the root cause of the oscillation. Finally, Granger causality (GC) analysis was used to v... [more]
A Probabilistic Statistical Method for the Determination of Void Morphology with CFD-DEM Approach
Yuanxiang Lu, Sihan Liu, Xinru Zhang, Zeyi Jiang, Dianyu E
March 29, 2023 (v1)
Keywords: CFD-DEM, dynamic stability, packed bed, probability method, void morphology
Voids that are formed by gas injection in a packed bed play an important role in metallurgical and chemical furnaces. Herein, two-phase gas−solid flow in a two-dimensional packed bed during blast injection was simulated numerically. The results indicate that the void stability was dynamic, and the void shape and size fluctuated within a certain range. To determine the void morphology quantitatively, a probabilistic method was proposed. By statistically analyzing the white probability of each pixel in binary images at multiple times, the void boundaries that correspond to different probability ranges were obtained. The boundary that was most appropriate with the simulation result was selected and defined as the well-matched void boundary. Based on this method, the morphologies of voids that formed at different gas velocities were simulated and compared. The method can help us to express the morphological characteristics of the dynamically stable voids in a numerical simulation.
A Methodology for Energy Load Profile Forecasting Based on Intelligent Clustering and Smoothing Techniques
Jamer Jiménez Mares, Loraine Navarro, Christian G. Quintero M., Mauricio Pardo
March 29, 2023 (v1)
Keywords: artificial neural networks, clustering, demand forecasting, time series analysis
The electrical sector needs to study how energy demand changes to plan the maintenance and purchase of energy assets properly. Prediction studies for energy demand require a high level of reliability since a deviation in the forecasting demand could affect operation costs. This paper proposed a short-term forecasting energy demand methodology based on hierarchical clustering using Dynamic Time Warp as a similarity measure integrated with Artificial Neural Networks. Clustering was used to build the typical curve for each type of day, while Artificial Neural Networks handled the weather sensibility to correct a preliminary forecasting curve obtained in the clustering stage. A statistical analysis was carried out to identify those significant factors in the prediction model of energy demand. The performance of this proposed model was measured through the Mean Absolute Percentage Error (MAPE). The experimental results show that the three-stage methodology was able to improve the MAPE, reac... [more]
A New Strategy for Improving the Accuracy of Aircraft Positioning Using DGPS Technique in Aerial Navigation
Kamil Krasuski, Dariusz Popielarczyk, Adam Ciećko, Janusz Ćwiklak
March 29, 2023 (v1)
Keywords: accuracy, DGPS, flight test, GNSS base stations, position errors, weighted mean model
In this paper a new mathematical algorithm is proposed to improve the accuracy of DGPS (Differential GPS) positioning using several GNSS (Global Navigation Satellites System) reference stations. The new mathematical algorithm is based on a weighting scheme for the following three criteria: weighting in function of baseline (vector) length, weighting in function of vector length error and weighting in function of the number of tracked GPS (Global Positioning System) satellites for a single baseline. The algorithm of the test method takes into account the linear combination of the weighting coefficients and relates the position errors determined for single baselines. The calculation uses a weighting scheme for three independent baselines denoted as (1A,2A,3A). The proposed research method makes it possible to determine the resultant position errors for ellipsoidal BLh coordinates of the aircraft and significantly improves the accuracy of DGPS positioning. The analysis and evaluation of t... [more]
Online Predictive Maintenance Monitoring Adopting Convolutional Neural Networks
Christian Gianoglio, Edoardo Ragusa, Paolo Gastaldo, Federico Gallesi, Francesco Guastavino
March 29, 2023 (v1)
Keywords: convolutional neural networks, partial discharges, predictive maintenance
Thermal, electrical and mechanical stresses age the electrical insulation systems of high voltage (HV) apparatuses until the breakdown. The monitoring of the partial discharges (PDs) effectively assesses the insulation condition. PDs are both the symptoms and the causes of insulation aging and—in the long term—can lead to a breakdown, with a burdensome economic loss. This paper proposes the convolutional neural networks (CNNs) to investigate and analyze the aging process of enameled wires, thus predicting the life status of the insulation systems. The CNNs training does not require any kind of assumption of how the factors (e.g., voltage, frequency and temperature) contribute to the life model. The experiments confirm that the proposal obtains better estimations of the life status of twisted pair specimens concerning existing solutions, which are based on strong hypotheses about the life model dependency on the factors.
Evaluation Model of Operation State Based on Deep Learning for Smart Meter
Qingsheng Zhao, Juwen Mu, Xiaoqing Han, Dingkang Liang, Xuping Wang
March 29, 2023 (v1)
Keywords: deep learning, energy load forecasting, operation state, recurrent neural networks, smart grid, smart meter, transfer learning
The operation state detection of numerous smart meters is a significant problem caused by manual on-site testing. This paper addresses the problem of improving the malfunction detection efficiency of smart meters using deep learning and proposes a novel evaluation model of operation state for smart meter. This evaluation model adopts recurrent neural networks (RNN) to predict power consumption. According to the prediction residual between predicted power consumption and the observed power consumption, the malfunctioning smart meter is detected. The training efficiency for the prediction model is improved by using transfer learning (TL). This evaluation uses an accumulator algorithm and threshold setting with flexibility for abnormal detection. In the simulation experiment, the detection principle is demonstrated to improve efficient replacement and extend the average using time of smart meters. The effectiveness of the evaluation model was verified on the actual station dataset. It has... [more]
Torque Analysis for Rotational Devices with Nonmagnetic Rotor Driven by Magnetic Fluid Filled in Air Gap
Gui-Hwan Kim, Hong-Soon Choi
March 29, 2023 (v1)
Keywords: magnetic fluid, magnetic torque, pressure, virtual work principle
In magnetomechanical applications, it is necessary to calculate the magnetic force or torque of specific objects. If the magnetic fluid is involved, the force and torque also include the effect of pressure caused by the fluid. The standard method is to solve the Navier−Stokes equation. However, obtaining magnetic body force density is still under controversy. To resolve this problem, this paper shows that the calculation of the torque of these applications should not only use the magnetic force calculation method, but also consider the mechanical pressure using an indirect approach, such as the virtual work principle. To illustrate this, we use an experimental motor made of a nonmagnetic rotor immersed in a magnetic fluid. Then, we show that the virtual work principle in appropriate approach can calculate the output torque of the nonmagnetic rotor due to pressure of the magnetic fluid. Numerical analysis and experimental results show the validity of this approach. In addition, we also... [more]
Aggregated World Energy Demand Projections: Statistical Assessment
Ignacio Mauleón
March 29, 2023 (v1)
Keywords: energy demand projections, future demand and supply breaks, intervals of uncertainty, long historical time series, statistical estimation
The primary purpose of this research is to assess the long-range energy demand assumption made in relevant Roadmaps for the transformation to a low-carbon energy system. A novel interdisciplinary approach is then implemented: a new model is estimated for the aggregated world primary energy demand with long historical time series for world energy, income, and population for the years 1900−2017. The model is used to forecast energy demand in 2050 and assess the uncertainty-derived risk based on the variances of the series and parameters analysed. The results show that large efficiency savings—up to 50% in some cases and never observed before—are assumed in the main Roadmaps. This discrepancy becomes significantly higher when even moderate uncertainty assumptions are taken into account. A discussion on possible future sources of breaks in current patterns of energy supply and demand is also presented, leading to a new conclusion requiring an active political stance to accelerate efficienc... [more]
Cooking Fuel Usage in Sub-Saharan Urban Households
Ting Meng, Wojciech J. Florkowski, Daniel B. Sarpong, Manjeet Chinnan, Anna V. A. Resurreccion
March 28, 2023 (v1)
Keywords: cooking fuel choice, income, location keyword, ordered probit, probability change, survey
This study models the frequency use of wood, charcoal, liquid gas, electricity, and kerosene in urban households in Ghana and supplements the literature on cooking fuel choices. The modeling is based on survey data collected in several major Ghanaian cities. Survey results indicate that charcoal and liquid gas are frequently used in meal preparation, while the frequency use of firewood, kerosene, and electricity is limited. Frequency use is estimated using the ordered probit technique. Five cooking fuel use equations identify income, socio-demographic characteristics, and location of urban residents as influencing the frequency use. Statistically significant effects measure probability changes in each of the four fuel categories. Income and education increase the probability of often or very often of using liquid gas or electricity to cook. The effect of being employed by the government is similar but less consistent. Age, household size, and marital status are linked to frequency use,... [more]
Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images
Parisa Asadi, Lauren E. Beckingham
March 28, 2023 (v1)
Keywords: 3D imaging of shale samples, 3D X-ray computed tomography, feed-forward neural network, Mancos, Marcellus, random forest, U-Net convolutional neural network
X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional... [more]
Analysis of Yield Potential and Regional Distribution for Bioethanol in China
Jingying Fu, Jinshuang Du, Gang Lin, Dong Jiang
March 28, 2023 (v1)
Keywords: bioethanol, feedstock, regional distribution, yield
Bioethanol will play a significant role in energy structure adjustment and greenhouse gas mitigation in the future, especially in the transport sector. As bioethanol production with grain crops may become obsolete due to food security concerns, the Chinese government has advocated the development of non-grain bioethanol. According to the current actual situation of bioethanol development and China’s Liquid Biofuel Development Roadmap, we defined three stages of bioethanol development. We focused on the assessment of bioethanol feedstock resources and bioethanol yield potential in different stages using a comprehensive evaluation system integrating statistical methods, crop growth process models, and geographic information system (GIS) techniques. The considered feedstocks included corn, sweet sorghum, cassava, switchgrass, crop straw, and forest residues. The spatial−temporal characteristics of the regional bioethanol distribution were discussed. The results indicate that the total res... [more]
Improvement of the Combustion Completeness of Hydrogen Jet Flames within a Mesoscale Tube under Zero Gravity
Junjie Hong, Ming Zhao, Lei Liu, Qiuxiang Shi, Xi Xiao, Aiwu Fan
March 28, 2023 (v1)
Keywords: air entrainment ratio, combustion efficiency, flame shape, flame tip opening, micro-jet flame
Microjet hydrogen flames can be directly used as micro heat sources or can be applied in micro propulsion systems. In our previous study, under zero gravity and without an active air supply, the combustion completeness of hydrogen jet flames within a mesoscale tube with an inner diameter of 5 mm was very low. In this study, we were dedicated to improving the combustion efficiency by using a convergent nozzle (tilt angle was around 68°) instead of the previous straight one, and the exit diameter was 0.8 or 0.4 mm. The numerical results demonstrate that the maximum combustion efficiency in the case of d= 0.8 mm was only around 15%; however, the peak value for the case of d = 0.4 mm was around 36%. This happened because with d = 0.4 mm, the fuel jet velocity was around four times that of the d = 0.8 mm case. Hence, the negative pressure in the combustor of d = 0.4 mm decreased to a much lower level compared to that of d = 0.8 mm, which led to an enhancement of the air entrainment ratio. H... [more]
Electric Heating Load Forecasting Method Based on Improved Thermal Comfort Model and LSTM
Jie Sun, Jiao Wang, Yonghui Sun, Mingxin Xu, Yong Shi, Zifa Liu, Xingya Wen
March 28, 2023 (v1)
Keywords: attention mechanism, electric heating, load forecasting, LSTM neural network, thermal comfort
The accuracy of the electric heating load forecast in a new load has a close relationship with the safety and stability of distribution network in normal operation. It also has enormous implications on the architecture of a distribution network. Firstly, the thermal comfort model of the human body was established to analyze the comfortable body temperature of a main crowd under different temperatures and levels of humidity. Secondly, it analyzed the influence factors of electric heating load, and from the perspective of meteorological factors, it selected the difference between human thermal comfort temperature and actual temperature and humidity by gray correlation analysis. Finally, the attention mechanism was utilized to promote the precision of combined adjunction model, and then the data results of the predicted electric heating load were obtained. In the verification, the measured data of electric heating load in a certain area of eastern Inner Mongolia were used. The results sho... [more]
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