Energy Consumption Parameters Research Articles

Prediction of Energy Consumption in Horizontal Roughing Process of Hot Rolling Strip Based on TDADE Algorithm

The steel industry is the key industry of energy consumption. The optimization of rolling process parameters is an effective measure to reduce and optimize energy consumption. Accurate energy consumption prediction has an indispensable guiding function in the planning of process parameters. However, the traditional energy consumption prediction and machine learning methods have been unable to fit the needs of high precision and reliability. Taking the horizontal roughing process (HRP) of hot rolling process as the research object, this paper mainly introduces an energy consumption prediction mechanism model (ECPMM) based on a roller adaptive wear strategy by analyzing the strip forming mechanism and rolling process. Then, two directions adaptive differential evolution (TDADE) algorithm is proposed based on a novel adaptive strategy. This algorithm has better convergence speed and global optimization ability than other optimization algorithms in the parameter optimization of ECPMM. We carried out comparative experiments on five machine learning algorithms. The results show that the ECPMM optimized by TDADE is superior to the five machine learning algorithms in prediction accuracy and stability. Finally, we conducted ablation experiments on the energy consumption characteristics of the HRP process, which turns out that using a smaller reduction and roller radius can reduce energy consumption. Summarizing the above experimental results suggests that the ECPMM has high accuracy and robustness, and satisfies the requirements of practical application. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —It is necessary to carry out the deep analysis and research of energy consumption prediction involved in the process planning of strip hot rolling, the inspiration of this article is stem from this point. The commonly used energy consumption prediction methods include mechanism modeling and machine learning, but both have disadvantages, like low prediction accuracy and black box, which must have to be performed to solve by adapting the production demand of high reliability. In this paper, the combination of the mechanism model and data-driven method is used to realize the accurate prediction of energy consumption and improve the reliability of the model. To the best of our knowledge, this is the first paper about the research on energy consumption prediction of hot rolling process based on mechanism model and data-driven, especially in data size, time granularity, algorithm design, and prediction performance. Such research is helpful to guide engineers to design rolling energy consumption. We will dive a bit deeper into learning the energy consumption and process parameters during the rolling process in future studies.

Technological process and laws of change of energy consumption parameters of spinning enterprises

Textile industrial enterprises are composed of three main sectors, which differ significantly from each other in terms of energy capacity, and include - spinning, weaving and finishing departments. In comparison to other departments, spinning technological processes consumes the most electricity. It is necessary to use modern energy-saving devices and to improve technological processes in accordance with the requirements of the times. Building an energy balance based on the analysis of the enterprise's technological processes, as well as forecasting electricity consumption, is one of the urgent issues. In this article, the issues of energy saving and efficiency improvement and forecasting in spinning departments are considered.

Effective Energy Consumption Parameters in Residential Buildings Using Building Information Modeling

Effective Energy Consumption Parameters in Residential Buildings Using Building Information Modeling

ОЦЕНКА ЭКОЛОГИЧЕСКОЙ БЕЗОПАСНОСТИ И ЭНЕРГЕТИЧЕСКОЙ ЭФФЕКТИВНОСТИ ТРАНСПОРТНЫХ СРЕДСТВ, ОБСЛУЖИВАЮЩИХ МАРШРУТЫ ГОРОДСКОГО НАЗЕМНОГО ТРАНСПОРТНОГО КОМПЛЕКСА

The urban passenger transport complex is the most important part of the infrastructure of modern cities and provides a number of key functions. The condition for the effective operation of the transport complex is the balanced development of the subsystems included in its structure, which in turn necessitates the formation of a methodological framework that determines the conditions for such development. Based on the indicated provisions, the purpose of the study is formulated, which is to increase the efficiency of the operation of vehicles serving the routes of the urban ground passenger transport complex, based on the results of an assessment of their environmental safety and energy efficiency. To achieve this goal, it is necessary to solve a number of tasks: – study and analysis of the content of research papers, regulatory and technical documents in the field of environmental safety assessment of energy efficiency of vehicles; – development of a methodology for assessing and comparative analysis of environmental safety and energy efficiency, vehicles serving urban public transport routes; – assessment of environmental safety of various categories of vehicles serving the routes of the urban ground passenger transport complex; – generalization and analysis of the results obtained, formulation of conclusions and practical recommendations. During the study, an approach was used that assumes: modeling of energy consumption parameters of vehicles implementing various technologies of fuel and energy supply and a comparative assessment of indicators determining their energy efficiency and environmental safety in traffic conditions on urban passenger routes. Based on the implemented approach, the methods of system and statistical analysis of the initial information, mathematical modeling and methods of multi-criteria evaluation of the results are used in the work. The main result of the study is the numerical values of the indicators of energy efficiency and environmental safety of vehicles implementing various technologies of fuel and energy supply in the conditions of urban ground passenger transport complex, which is the scientific novelty of the study. The use of the obtained values in assessing the efficiency of passenger transportation by vehicles of various categories allows for the formation of a fleet of vehicles based on the results of a multi-criteria analysis. One of the directions of further research involving the use of the results obtained is the development of a comprehensive methodology for determining the optimal structure and coordinated formation of subsystems that make up the urban ground passenger transport complex.

Development of Deep Learning for Power Energy Optimization in the Industrial Robot System

This paper established power consumption modeling and motion estimation optimization of industrial robots. We also studied factors affecting the use of electrical energy, such as friction, torque, and electric current. The energy consumption parameters of each coupling can be quantified through the Deep Learning (DL) technique, Scaled Conjugate Gradient (SCG) estimation, or Simulation and experimentation based on the movement posture of a given robot dynamic model to control the robot operation. The robot dynamic model parameters can be identified and expressed in mathematical equations. Electrical energy consumption estimates were analyzed using the SCG technique to compare with the Nonlinear Least Squares (NLS) method using a large dataset of approximately 60,000 samples. The results showed accurate parameter prediction and electrical energy consumption estimation of the robot locomotion pose. The maximum errors in the SCG and NLS methods were 0.89% and 1.54%, respectively. It indicated that the electric energy consumption model using the SCG estimation method is more efficient than the NLS method.

Deep Transfer Learning for Detecting Electric Vehicles Highly-Correlated Energy Consumption Parameters

Deep Transfer Learning for Detecting Electric Vehicles Highly-Correlated Energy Consumption Parameters

Effect of Anterioposterior Weight-Shift Training with Visual Biofeedback in Patients with Step Length Asymmetry after Subacute Stroke.

Step length asymmetry is a characteristic feature of gait in post-stroke patients. A novel anterioposterior weight-shift training method with visual biofeedback (AP training) was developed to improve the forward progression of the trunk. This study aimed to investigate the effect of AP training on gait asymmetries, patterns, and gait-related function in subacute stroke patients. Forty-six subacute stroke patients were randomly assigned to the AP training group or the control group. The AP training group received conventional gait training and AP training five times per week for 4 weeks. The control group received the same intensity of conventional gait training with patient education for self-anterior weight shifting. Plantar pressure analysis, gait analysis, energy consumption, and gait-related behavioral parameters were assessed before and after training. The AP training group showed significant improvement in step length asymmetry, forefoot contact area and pressure, Berg balance scale score, and Fugl-Meyer assessment scale of lower extremity score compared to the control group (p < 0.05). However, there was no significant between-group difference with respect to energy cost and kinetic and kinematic gait parameters. In conclusion, AP training may help improve the asymmetric step length in stroke patients, and also improve anterior weight shifting, balance, and motor function in subacute stroke survivors.

Optimal data transmission for decentralized IoT and WSN based on Type-2 Fuzzy Harris Hawks Optimization

Due to the widespread availability of services and smart devices, the decentralized internet of things and wireless sensor network-based systems (DIoT and WSN) have drawn the attention of academics and researchers and are still under constant development. Many challenges face the DIoT and WSN networks and need to be solved. The main issue among these challenges is reducing energy consumption in order to increase network lifetime. Energy-efficient routing protocols and the clustering method are the best solutions to this problem. In this paper, Type-2 Fuzzy Harris Hawks Optimization (T2FHHO) is used to choose the best path (route) from the source (cluster head (CH)) to the destination nodes (base station (BS)). Along with this, we propose a new fitness function to find the next hop based on residual energy, distance, traffic, and buffer size. Then, we apply the T2FHHO method to get the best CH. This protocol seeks to increase the long-term reward earned by each node. The best path in terms of the minimum cost obtained using the proposed method is found. The two main contributions of this study are as follows: firstly, we present a new efficient routing approach that minimizes costs and maximizes efficiency. Secondly, we apply the T2FHHO to the network parameters of energy consumption, network lifetime, and convergence curves. The results indicate that T2FHHO outperforms competing methods. The proposed method holds the promise of significantly advancing the state of the art in energy-efficient routing for DIoT and WSN networks, with significant implications in various sectors and applications, ensuring extended network lifetimes and sustainable IoT implementations.

Enhancing energy efficiency through hourly assessments of passive interventions in educational-office buildings: A case study in a Mediterranean climate

This research explores the impact of passive interventions on various energy consumption parameters and their interdependence to enhance the energy efficiency of a building. While overall outcomes from envelope interventions using passive strategies are positive, a closer examination on an hourly basis reveals certain drawbacks that impede improvements in energy efficiency. A combination of methods is employed, utilizing the gray-box method for input data collection and the white-box method for sensitivity analysis to assess the impact of proposed interventions on building envelope individually and in combination. The building's energy consumption reveals that the combined impact of the passive strategies results in an approximately 82% reduction in gas consumption and a 2% reduction in electric consumption. Sensitivity analysis underscores the necessity of hourly investigations, introducing a research dimension to the study. Hourly assessments expose side effects that hinder the rate of improvement in the envelope retrofitting scenario. Statistical analysis of consumer parameters highlights the significance of the cooling system's hourly consumption, particularly in mitigating undesirable effects during shoulder seasons. By minimizing these side effects, the energy consumption of the cooling system in the envelope retrofitting scenario represent a notable 41.44% reduction, a substantial improvement from the initial 16.16%.

Energy consumption modeling and parameter identification based on system decomposition of welding robots

Energy consumption modeling and parameter identification based on system decomposition of welding robots

An Overview of Manet Power Management Approaches

One of the primary issues with MANET is power optimization and utilization because it relies on the node's internal battery power to operate the wireless network. The performance of the MANET is also affected by one of the parameters of energy consumption and utilization. Each operation in the MANET requires some amount of energy to complete. This article elaborated on MANET power management from its inception to the present, as well as doing comparison research to recommend new methods for improving MANET power utilization. This study examines MANET power management options in terms of numerous parameter metrics, including Mobility Aware, Clustering, Topology, Transmission Range, QOS, and link-based. Finally, the methodologies used in MANET power management and performance factor improvement were summarised. To surpass all performance indicators in MANET utilization, new manipulative tactics are necessary. The innovative method is the most effective.

Analysis of energy consumption parameters in wheat harvesting using grain harvesters

Abstract. The paper analyses the energy consumption parameters of three grain harvesters for harvesting wheat. It has been found from the studies that the nominal throughput of the two harvester models K1 and K2 is very close, 7.88 kg/s on average, while for the third harvester K3 it is 9.33 kg/s or 16% higher than the two harvesters. In addition, the direct energy consumption of the observed three harvester models has been established, which averaged 312.57 MJ/t. The consumption of diesel fuel for the harvest of 1 ton of wheat with the harvesters has been determined, and the average consumption was 7.46 kg/t. The average total energy consumption of the three grain harvesters has been determined (1.19 GJ/t), ranging from 1.15 GJ/t to 1.24 GJ/t. From the obtained results, it can be seen that the lowest result has been obtained with harvester K3, since it has the largest working width of the header and the unloading of the grain hopper takes place while moving.

An Investigation of the Use of Microwaves and Airborne Ultrasound in the Convective Drying of Kale: Process Efficiency and Product Characteristics

This study evaluated different hybrid drying modes, combining traditional convective drying with microwave radiation and airborne ultrasound for the dehydration of green leafy vegetables. The central composite design method was used to analyze the impact of microwave and ultrasonic waves on kinetics, energy consumption, and various quality parameters, like color, ascorbic acid, polyphenol, carotenoid, and chlorophyll content in Brassica oleracea, var. acephala. The results of the applied experimental design, i.e., the surface response methodology, showed that the application of microwaves and ultrasound decreased the drying time considerably and enhanced the moisture evaporation from the kale leaves, significantly improving the drying rate and energy efficiency. The drying rate increase demonstrated varying results with changes in air temperature. Specifically, ultrasound resulted in a 69–100% increase, microwaves in a 430–698% increase, and a combination of ultrasound and microwaves in a 463–950% increase at 70 and 50 °C, respectively. Specific energy consumption decreased by 42–51% for ultrasound, 80–87% for microwaves, and 82–90% for ultrasound and microwaves at 70 and 50 °C, respectively. The drying parameters were also found to be better at a higher temperature, but the increase in the drying rate caused by microwaves and ultrasound was notably lower. Moreover, the analysis of the key kinetic parameters and material qualities led to the conclusion that the synergistic action of microwave- and ultrasound-assisted convection contributes to better drying effectiveness and product quality, demonstrating greater retention of vitamin C, phenolics, and natural dyes of up to 90%.

Parametric design of a residential building system through solar energy potential: the case of Guelma, Algeria

To evaluate the potential of retrofitting roofs of residential buildings in Algeria with building integrated photovoltaics (BIPV). A case study of a family residential building in the city of Guelma, was selected to receive building-integrated photovoltaic (BIPV) systems as a retrofit to their roofs using Rhinoceros software and plugins Grasshopper, Ladybug, and Honeybee, adding photovoltaic panels on the roof taking into account the study of solar radiation analysis on the building, and meteorological data from Meteonorm Software 7.3. The method used, is based on measured data for final energy consumption (yearly average of 14600 kWh), building morphology, and photovoltaic module parameters. The results of the renovation of roofs with building-integrated photovoltaic solutions are presented for the existing residential building, it is also shown that a small photovoltaic area on the roofs can meet the demand of the buildings. The results identify the aspects that need to be improved in the development of future design tools and highlight the importance of integrating parametric design software and simulation tools for BIPV systems at the preliminary design stage, to ensure good building performance, both in terms of comfort and energy performance.

Multi-objective optimisation for energy saving and high efficiency production oriented multidirectional turning based on improved fireworks algorithm considering energy, efficiency and quality

To promote energy saving, high efficiency and quality production in mechanical manufacturing industry, a large number of studies have been conducted on parameters optimisation, especially for turning. However, most of the approaches have focused on single objective model and unidirectional turning (UDT). This paper presents a multi-objective parameter optimisation method for energy consumption, cutting time and surface roughness using new process of forward-and-reverse multidirectional turning (MDT). Firstly, the composition characteristics of cutting parameters for energy consumption, cutting time and surface roughness of MDT are analyzed to establish the corresponding models through nonlinear regression fitting, respectively. Then taking the minimum energy consumption, cutting time and surface roughness as the optimisation objectives, the improved fireworks algorithm can generate nearly 100 groups of optimal solutions. The most suitable processing parameters solution needs to be selected according to different machining requirements. In application scenarios, most relative errors of the models are within 10%. Comparing the machining performance of MDT and UDT under the same cutting parameters, the processing efficiency is increased by 25.97% and energy consumption is reduced by 12.38%, using the MDT. This study provides a multi-objective optimisation approach and models for MDT to reduce energy consumption, improve production efficiency and quality.

Quantum-inspired particle swarm optimization for efficient IoT service placement in edge computing systems

With the advancement of the 5G networks, edge computing (EC) assisted Internet of Things (IoT) based applications demand real-time computation and high-volume data-intensive services. Due to the heterogeneity and limited resources of the edge nodes (ENs), and dynamic resource demand of the IoT applications, it is challenging to place the IoT services into the available ENs by ensuring performance measurements on quality of services (QoS). In this paper, a novel quantum-inspired particle swarm optimization-based service placement (QPSO-SP) algorithm is proposed for EC environment. The QPSO-SP is intended to achieve desired service placement while optimizing throughput, energy consumption, delay, and computation load of the system. Quantum particle (QP) is designed to represent a complete solution for IoT service placement in an EC environment. Decoding of the QP is done by using a novel double-hashing technique. The fitness function uses throughput, delay, energy consumption, and load balancing parameters. Extensive simulation is performed and comparison is done with the standard existing algorithms. The parametric study, Taguchi method is conducted. The statistical analysis, ANOVA, followed by Friedman test is also done. The simulation results indicate that the proposed QPSO-SP outperforms existing works in terms of energy consumption, delay, throughput, and load balancing.

Energy refurbishment planning of Italian school buildings using data-driven predictive models

In the current practice, the design of energy refurbishment interventions for existing buildings is typically addressed by performing time-consuming software-based numerical simulations. However, this approach may be not suitable for preliminary assessment studies, especially when large building portfolios are involved. Therefore, this research work aims at developing simplified data-driven predictive models to estimate the energy consumption of existing school buildings in Italy and support the decision-making process in energy refurbishment intervention planning at a large scale. To accomplish this, an extensive database is assembled through comprehensive on-site surveys of school buildings in Southern Italy. For each school, a Building Information Modelling (BIM) model is developed and validated considering real energy consumption data. These BIM models serve in the design of suitable energy refurbishment interventions. Moreover, a comprehensive parametric investigation based on refined energy analyses is carried out to significantly improve and integrate the dataset. To derive the predictive models, firstly the most relevant parameters for energy consumption are identified by performing sensitivity analyses. Based on these findings, predictive models are generated through a multiple linear regression method. The suggested models provide an estimation of the energy consumption of the “as-built” configuration, as well as the costs and benefits of alternative energy refurbishment scenarios. The reliability of the proposed simplified relationships is substantiated through a statistical analysis of the main error indices. Results highlight that the building's shape factor (i.e., the ratio between the building's envelope area and its volume) and the area-weighted average of the thermal properties of the building envelope significantly affect both the energy consumption of school buildings and the achievable savings through retrofitting interventions. Finally, a framework for the preliminary design of energy refurbishment of buildings, based on the implementation of the herein developed predictive model, is proposed and illustrated through a worked example application.Worth noting that, while the proposed approach is currently limited to school buildings, the methodology can conceptually be extended to any building typology, provided that suitable data on energy consumption are available.

CR-NBEER: Cooperative-Relay Neighboring-Based Energy Efficient Routing Protocol for Marine Underwater Sensor Networks

This paper proposes a Cooperative-Relay Neighboring-Based Energy-Efficient Routing (CR-NBEER) protocol with advanced relay optimization for MUSN. The utilization of the relay nodes, among all other sensor nodes, makes it possible to achieve node-to-node deployment. The proposed method focuses only on cooperation and relay optimization schemes. Both schemes have previously been implemented, and thus the proposed method represents the extended version of the Neighboring-Based Energy-Efficient Routing (NBEER) protocol. Path loss, end-to-end delay, packet delivery ratio, and energy consumption parameters were considered as part of the performance evaluation. The average performance was revealed based on simulations, where the overall average EED of Co-UWSN was measured to be 35.5 ms, CEER was measured to be 26.7 ms, NBEER was measured to be 27.6 ms, and CR-NBEER was measured to be 19.3 ms. Similarly, the overall EC of Co-UWSN was measured to be 10.759 j, CEER was measured to be 8.694 j, NBEER was measured to be 8.309 j, and CR-NBEER was measured to be 7.644 j. The overall average PDR of Co-UWSN was calculated to be 79.227%, CEER was calculated to be 66.73.464%, NBEER was calculated to be 85.82%, and CR-NBEER was calculated to be 94.831%. The overall average PL of Co-UWSN was calculated at 137.5 dB, CEER was calculated at 230 dB, NBEER was calculated at 173.8 dB, and CR-NBEER was calculated at 79.9 dB. Based on the simulations and evaluations, it was observed that the cooperation and relay optimization scheme outperformed previous schemes.

CNN sensor based motion capture system application in basketball training and injury prevention

Basketball is a high-intensity sport, and sports injuries often occur. Therefore, how to monitor the sports status of basketball players in real time, discover and prevent the occurrence of sports injuries in time, has become an urgent problem for athletes and coaches. In this paper, a motion capture system based on CNN sensor is proposed. Through the application of sensor, real-time monitoring of athletes' motion state, the system can collect athletes' movement track, speed, acceleration, stride frequency, heart rate, energy consumption and other parameters in real time. By analyzing the movement data, it can timely warn and deal with the occurrence of sports injuries. In this paper, convolutional neural network (CNN) is used to process and analyze motion data, so that the motion capture system has higher precision and accuracy. Through the training and optimization of CNN, the system can identify and analyze motion data more accurately, and improve the performance and effect of the motion capture system. The motion capture system based on CNN sensor can realize real-time monitoring and prevention of sports injuries for basketball players, provide more comprehensive, scientific and real-time sports data for athletes and coaches, help them better conduct training and adjust tactics, and improve the competitive level and safety of basketball games.

Developing a data-driven hydraulic excavator fuel consumption prediction system based on deep learning

With the aggravation of the global energy crisis, fuel consumption has become a key indicator for excavator manufacturers and policymakers. However, traditional experimental and theoretical approaches, which are complicated to calculate and dependent on the laboratory environment, are difficult to be applied in engineering. A data-driven prediction system consisting of data acquisition, feature decomposition, fuel consumption prediction, and performance evaluation modules, is developed to achieve excavator fuel consumption prediction during operation. The characteristic parameters of excavator energy consumption are collected through the established data acquisition module and the superfluous noise is eliminated by the feature decomposition module. A prediction model based on Informer is proposed in the fuel consumption prediction module to solve the drawback that the neural network model cannot capture key information in long series data in parallel, thus improving the prediction accuracy. Finally, the effectiveness of different prediction methods is verified by the performance prediction module.