Energy Consumption Curves Research Articles

Impact resistance study of lime slag–stabilised steel slag mixes

ABSTRACT Given the transient nature of impact loading, exploring the microscopic damage characteristics and damage mechanisms of materials through indoor tests is difficult. In this study, the damage morphology and energy dissipation characteristics of lime slag–stabilised steel slag (LSSS) mixes under vertical impact loading is explored using ABAQUS software, a finite element model of LSSS for falling hammer impact test is established, the validity of finite element calculation is verified by comparing the results of indoor tests, and the strain characteristics, deflection changes, and energy transformation process of LSSS under vertical impact loading are analysed. Results show that the LSSS tensile and compressive strains under impact loading are roughly divided into low strain rate, precracking, and cracking phases with a rapid increase in strain. Under the same impact energy conditions, the LSSS bottom tensile strain, span deflection, drop hammer kinetic energy conversion time, and LSSS plastic energy consumption curve rising slope are sensitive to drop hammer mass. Thus, improvement in drop hammer mass will prolong the impact loading of the drop hammer–LSSS system internal energy conversion time, and the LSSS strain peak range to the central concentration, enhancing span deflection and bottom deformation.

Quantifying the impact of electricity pricing on electric vehicle user behavior: a V2G perspective for smart grid development

ABSTRACT This research introduces an innovative analytical framework for studying the behavior of electric vehicles (EVs) in power grids. The approach primarily examines the dynamic State of Charge (SOC) and energy reserves of EV batteries. By thoroughly examining patterns of EV charging demand and operational states (charging, discharging, idling, and waiting) across various time intervals, including day and night periods, this study aims to provide a comprehensive understanding of EV dynamic behavior in power networks. The proposed model goes beyond traditional assessments by incorporating human behavioral patterns and responsiveness to economic signals, specifically investigating the impact of electricity pricing on user behavior. The core analysis centers on exploring the Vehicle-to-Grid (V2G) concept, viewing EVs not only as energy consumers but also as potential contributors to grid stability and efficiency. The study delves into how the interplay of dynamic EV behavioral characteristics and owner responses to pricing influences the overall energy consumption curve of the grid, thereby shaping the capability of EVs in providing grid support services. To quantitatively validate the effectiveness of the model, a detailed case study is conducted, offering empirical evidence of its practical utility. The research findings are quantified, highlighting the significant implications for decision-makers, grid managers, and stakeholders in the energy sector. The paper emphasizes the main outcomes, demonstrating how the proposed analytical framework can empower stakeholders in making informed decisions about policy and infrastructure development. The robust methodological approach presented in this paper serves as a valuable tool for evaluating the impacts of adopting V2G, providing strategic insights for a more sustainable and economically viable paradigm.

Effect of Cyclic Loading Treatment on the Compression Energy and Creep Properties of Machine-Harvested Seed Cotton

Machine-harvested seed cotton was taken as the research object to further clarify its creep performance, minimize its power consumption during the loading process, and obtain a better loading method. The uniaxial compression creep test was carried out using the Instron universal material test bench to apply cyclic loading treatment. The test factors included cyclic loading times, cyclic stress peak, and cyclic loading frequency. The energy consumption curve of the machine-harvested seed cotton during cyclic loading was obtained through OriginPro 2019b software, and its energy change law was analyzed. Creep strain was divided into two parts, namely, initial creep strain and creep strain increment, to elucidate the creep mechanism. The Burgers model was chosen to describe the creep strain increment. Results show that machine-harvested seed cotton exhibits energy consumption hysteresis during cyclic loading. The compression energy rapidly decreases with increasing cyclic loading times and then stabilizes. Meanwhile, the compression energy increases with increasing cyclic stress peak and cyclic loading frequency. The creep strain mechanism is also the same, which first rapidly increases and then levels off. Cyclic loading times, cyclic stress peak, and cyclic loading frequency have different effects on creep strain increment, instantaneous elastic modulus, hysteresis elastic modulus, viscosity coefficient, delay time, and relative deformation index. Finally, disregarding power consumption and interaction, extending the cyclic loading time, and increasing the cyclic stress peak while simultaneously minimizing the cyclic loading frequency can reduce the relative deformation index in the creeping stage. Accordingly, the deformation retention ability in the creep is improved, but the compression energy in the cyclic loading increases. The results can provide theoretical and data support for studying the theoretical basis of the rheological properties of machine-harvested seed cotton, the design of seed cotton baling devices, and the study of bale (mold) forming quality.

Research on the double-layer clustering method of residential energy use characteristics under the background of energy system energy savings and carbon reduction

Accurate differentiation of energy consumption information of residential users is of great significance for load planning, scheduling, operation and management of power system, and is the basic premise for realizing intelligent perception of energy system and energy saving and carbon reduction. Considering that the conventional single-layer clustering method has limited clustering stability and clustering effect, this paper takes the key family feature factors as the modified feature quantity of quadratic clustering, and proposes a study of user energy characteristics based on double-layer clustering and modification. Firstly, the user’s energy consumption data is collected and pre-processed, and the user’s energy consumption curve is clustered and analyzed by using the integrated clustering algorithm based on voting and the advantages of each member algorithm. Then, the key family characteristic factors are obtained, and the results of one-layer clustering and key family characteristic factors are combined to carry out two-layer clustering of the same category of users in the form of questionnaire survey. Finally, the nonlinear mapping capability of Support Vector Machine (SVM) is used to reverse correct the results of the one-layer clustering. The actual algorithm data of the residents’ demand response experiment in a southeastern province are compared. The results show that compared with the single-layer clustering algorithm, the proposed method can accurately distinguish the energy consumption characteristics and adjustable potential of different users, and correct the wrong clustering results in the single-layer clustering. The clustering stability and clustering effect have been effectively improved.The example results show that the clustering results modified by SVM can better mine and distinguish user energy characteristics, and can be used to solve the problem of the current demand response clustering algorithm not being able to comprehensively and objectively describe the participation willingness and response-ability of residential users in the implementation process. It can also provide a basis for peak shaving and power grid frequency regulation.

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.

Photovoltaic capacity dynamic tracking model predictive control strategy of air-conditioning systems with consideration of flexible loads

Building photovoltaic (PV) power generation is intermittent, volatile, random, and uncontrollable; thus, the use of solar grid-connected power generation can lead to a series of problems, such as grid voltage fluctuations and power imbalance. As a typical flexible load, the scheduling and regulation of the heating, ventilation, and air conditioning (HVAC) system load can help a grid-connected solar grid achieve balanced and flexible operation. This study proposes a PV capacity dynamic tracking model predictive control strategy for air-conditioning systems with flexible loads. This strategy aims to effectively address the issue of unstable grid-connected output of solar PV systems. In particular, a solar radiation prediction model based on a long short-term memory neural network optimized by the grey wolf optimization algorithm was established, which effectively improved the model prediction accuracy. The cost function of HVAC flexible load control considers the law of solar PV output and the human thermal comfort model. A genetic algorithm (GA) is used to obtain the optimal control parameters for dynamic regulation. The GA was employed to efficiently and effectively optimize the control of flexible HVAC loads, considering the variability of the solar PV output and ensuring human thermal comfort. A dynamic regulation model was developed for a typical office building with a grid-connected solar PV power generation system. TRNSYS was utilized for verification based on the actual measured data and relevant meteorological parameters of the case building. A new evaluation metric, volatility, was proposed to evaluate net load fluctuation. The simulation platform based on the physical building yield results indicated a strong resemblance between the energy consumption curve under the predicted control conditions using the flexible model and the PV generation curve. Additionally, the net load volatility was measured to be 2.63. Compared with the predicted control condition without the flexible model, the net load volatility of the grid was reduced by 47.08%, and the energy-saving rate reached 10.89% in the summer.

A Method for Identifying Energy Consumption of Machine Tools Based on Integrated Models

The uncertainty of the dynamic change process of expenditure of energy from machine tools seriously hinders the development of an intelligent and green manufacturing industry. In response to the multifaceted impact of identification methods in the process of energy consumption change, the difficulty of extracting high-quality features, and the complexity and nonlinearity of energy consumption curves, an identification method for predicting the energy consumption of machine tools based on integrated models was proposed. Taking CNC milling as an example, the model of energy consumption based on different cutting periods was established, which was then trained and predicted by using the preprocessing signal based on Random forest (RF). Meanwhile, the RF was used to identify the cutting stages and realize the energy consumption classification prediction. The advantage and availability of this method have been demonstrated through practical cases, and it has been verified that this recognition method can accurately predict different operating states and energy consumption of each stage on machine tools.

Micro-Grid Day-Ahead Stochastic Optimal Dispatch Considering Multiple Demand Response and Electric Vehicles

Multiple demand responses and electric vehicles are considered, and a micro-grid day-ahead dispatch optimization model with photovoltaic is constructed based on stochastic optimization theory. Firstly, an interruptible load model based on incentive-based demand response is introduced, and a demand response mechanism for air conditioning load is constructed to implement an optimal energy consumption curve control strategy for air conditioning units. Secondly, considering the travel demand and charging/discharging rules of electric vehicles, the electric vehicle optimization model is built. Further, a stochastic optimization model of micro-grid with demand response and electric vehicles is developed because of the uncertainty of photovoltaic power output. Finally, the simulation example verifies the effectiveness of the proposed model. The simulation results show that the proposed model can effectively tackle the uncertainty of photovoltaic, as well as reduce the operating cost of micro-grid. Therefore, the effective interaction between users and electric vehicles can be realized.

Cryoslash as an effective pre-treatment to obtain nanofibrillated cellulose using ultra-fine friction grinder with kraft pulp

Abstract Approaches to obtain nanocellulose have been extensively suggested, and among the most competitive methodologies includes mechanical defibrillation, which results in a nanocellulose gel suspension that can be used in a variety of applications. However, standard procedures results in inhomogeneous fibre sizes due to its physical approach. Pre-treatments have been suggested, such as enzymes, but their cost is a disadvantage. In this work, we suggest the use of cryoslash for bleach kraft pulp using a simple methodology of embedding the dry kraft pulp in distilled water following by freezing at −80 °C, which was slashed using a common blender and submitted to an ultra-fine friction grinder. Samples were studied at various grinding steps 5, 15, 30, 60 and 120. Overall, the energy difference required to defibrillate with cryoslash was ∼3.26 kWh/kg at 30 steps and ∼6 at 120 steps, which was similar to enzymatic pre-treatment with nanofibrils significantly smaller. Cryoslash presented a unique C–H in plane structure from nanocellulose, related to a decrease in average size of fibrils, while crystallinity and thermal properties of nanocellulose remained stable at 60 steps, but mechanical properties increased until 120 steps along with the transmittance values related to the energy consumption curve.

Research on group distribution leakage analysis method for RSM

Side channel attack is a new and simple attack idea, and its proposal has created a significant threat to the security of cryptographic devices. With the continuous development of deep learning techniques, deep learning-based side channel attacks have enabled the attack techniques to reach new heights, but the attack efficiency and training time have become new problems. In order to improve the attack efficiency, researchers have proposed a large number of leakage analysis methods, which can effectively extract the intervals with significant leakage in the energy consumption curve and reduce the training parameters and data volume. In order to deal with side channel attacks, researchers have proposed various resistance strategies, among which masking protection is the simplest and widely used way. RSM rotating S-box masking scheme is a particularly important masking protection scheme, which uses a first-order masking scheme to randomize the leakage energy consumption of sensitive information using random masks, thus making the algorithm resistant to first-order side channel attacks. For this type of masking algorithm, this paper proposes a new leakage analysis algorithm, namely "Group Distribution Difference" (GDD). This algorithm is based on the distribution difference of energy consumption, and the energy consumption frequency in the group is calculated instead of the probability distribution. The KL distance is used to calculate the difference between groups and find out the leakage interval, so as to achieve an efficient attack.

Motion Planning of an Inchworm Robot Based on Improved Adaptive PSO

Focusing on the motion energy consumption of a self-developed inchworm robot’s peristaltic gait, based on the “error tracking” of cubic polynomial programming in Cartesian space and seventh polynomial programming in joint space, we propose an optimal motion planning method of energy consumption considering both kinematic and dynamic constraints. Firstly, we offer a mathematical description of the energy consumption and space curve similarity operator. Secondly, we describe the mathematical models of the robot trajectory and path that were established in terms of their dynamics and kinematics. Then, we propose a motion planning method based on improved adaptive particle swarm optimization (PSO) to accelerate the convergence speed of the algorithm and ensure the accuracy of the model calculation. Finally, we outline the simulation test carried out to measure the inchworm-like robot’s creeping gait. The results show that the motion path obtained by using the planning method proposed in this paper is the one with the least energy consumption by the robot among all the comparison paths. Moreover, compared with other algorithms, it was found that the result obtained by using the algorithm proposed in this paper is the one with the shortest solution time and the lowest energy consumption under the same iteration times. The calculation results verify the feasibility and effectiveness of the planning method.

Application of Big Data Technology in Ship-to-Shore Quay Cranes at Smart Port

As more and more container terminals are becoming intelligent, different kinds of sensors are widely installed at different locations of the cranes and collect a large amount of data. In order to effectively utilize and manage these huge amounts of actual working data of different sensors and grasp the status of the terminal, this article proposes a data processing framework that integrates the crane load, energy consumption, crane trolley speed and crane gearbox vibration signals of the container terminal. Firstly, the load spectrum of the crane load is calculated by the non-parametric density estimation method in probabilistic statistics and the energy consumption curves are obtained. Secondly, the driving cycle of the crane trolley speed are constructed by drawing on the method in the transportation field. Finally, the vibration signals of the crane gearbox are used for anomaly detection by unsupervised methods; at the same time, clustering results can also be used as the basis for extracting typical vibration signals and removing redundant data.

Structural Safety Analysis of Cantilever External Shading Components of Buildings under Extreme Wind Environment

The high intensity of solar radiation and long sunshine time in the Turpan area lead to the necessity of sunshade construction. Sunshade components can effectively block direct solar radiation and the secondary heating of buildings. Through the analysis of the importance and sensitivity of sunshade components, it was found that the importance of sunshade components accounts for the largest proportion of multi-parameters, and the sensitivity of sunshade components accounts for about 60% of the total. At the same time, the change in sunshade length has an important influence on the proportion of air conditioning energy consumption and space comfort when the sunshade length reached the 0.6 m–0.8 m range. The energy consumption curve of air conditioning no longer decreased and tended to be horizontal, which showed that a sunshade could effectively reduce the energy consumption of air conditioning, while the PMV comfort curve gradually increased and tended to be horizontal, indicating that a sunshade could effectively improve indoor comfort; therefore, a sunshade could reduce direct solar radiation, reduce the energy consumption of air conditioning and improve indoor thermal comfort. In view of the extremely harsh climate characteristics of Turpan, although Turpan needs to carry out shading design, as a typical wind-sensitive component, the structural safety of the visor under the action of an extreme wind environment is the primary focus of designers. The design requires wind loads as control loads. Based on the ANSYS Workbench platform, this study used the fluid–structure coupling technology to calculate and solve for the wind load stress and strain of a horizontal sunshade and a vertical sunshade in a cantilevered external sunshade of different buildings orientations. In this study, by solving for the maximum principal stress and maximum principal elastic strain under 10 working conditions, the results showed that the maximum principal stress of the sun visor under all working conditions was 0.39 MPa, which is much smaller than the tensile strength of C25 concrete. The calculated maximum principal elastic strain of the sun visor was 0.12 × 10−4, which is much smaller than the maximum strain value of concrete. Therefore, the wind load under this research condition had no great influence on the structural safety of the concrete sunshade, which proves the structural feasibility of the building sunshade member in the Turpan area, and provides a reference for the future practical engineering of cantilever members in the Turpan area.

Stochastic evolution characteristics of fracture process zone in concrete

In this study, the stochastic characteristics of the fracture process zone (FPZ) of concrete are analysed. The impacts of specimen size on the critical crack opening displacement, the distribution of cohesive stress, and the critical length of FPZ are studied, and calculations are performed for the stochastic energy consumption curves of concrete. The dispersion of the variance of critical crack tip opening displacement CTODc and the critical length of FPZ are obtained. Combined with the existing experiments, comparative verification is performed. The results show that the critical crack tip opening displacement CTODc and critical length of FPZ increased with the specimen size, whereas the critical cohesive stress at the tip of the initial crack decreased with the specimen size. The dispersion of the variance of critical crack tip opening displacement CTODc and the critical length of FPZ increased with the specimen size.

An energy efficiency solution based on time series data mining algorithm—a case study of small hotel

Abstract This study aims to conduct data mining research on the time series energy consumption dataset of a small hotel. Earlier studies on data mining have demonstrated that cluster and association analysis had been commonly used methods today, while this has not yet been investigated under time series dimension. For that consequence, this article utilizes K-shape and Apriori algorithm coded by Python language to explore the time series energy consumption data of the small hotel in terms of the subentry and total energy consumption. Final results reveal that the energy consumption curve and association rules can effectively reflect the working characteristics of the small hotel. From the clustering results, the small hotel working feature and weather condition determine the time series energy consumption curve shape. As for association rules, there is a different chain relationship between the energy consumption of each subentry and total energy consumption of the small hotel, especially the consumption of heating gas and cooling, which mainly determines the changes in other energy consumption.

An energy efficiency solution based on time series data mining algorithm on elementary school building

Abstract The purpose of this study is to conduct data mining research on the time series energy consumption dataset of primary school buildings. Researches done so far mainly focused on cluster analysis and association analysis. In this article, python language is used as the carrier; Κ-shape algorithm and apriori algorithm are adapted to perform cluster analysis and association analysis on the time series energy consumption data of primary school buildings, from the perspective of the subentry and total energy consumption. The final result shows that the energy consumption curve and association rules obtained through clustering and association can effectively reflect the operating characteristics of primary school buildings. In terms of clustering, regardless of the subentry energy consumption or the total energy consumption, there are different characteristics of energy consumption patterns in winter and summer, which depend on the school schedule. With respect to association rules, there are different chain relationships between itemized total energy consumptions in the primary school building. In particular, heating natural gas consumption and cooling consumption mainly determine the changes in other energy consumption.

Admission Control in Home Energy Management Systems Using Theatre and Hybrid Actors

The goal of a Home Energy Management System (HEMS) is that of purposely shaping the cumulative energy consumption curves of domestic appliances by imposing suitable monitoring and control policies. The development of HEMS, like the development of general Cyber-Physical Systems (CPSs), is challenging, as it requires the exploitation of suitable methodological approaches which are able to deal jointly with the continuous and discrete behaviours of a CPS. In this paper, a methodological approach for HEMS is advocated which relies on the use of the Theatre actor system with hybrid actors. As a key feature, Theatre enables the same actor model to be used during the analysis, design, prototyping and implementation phases of the system. For property assessment, a Theatre model is reduced to Uppaal hybrid timed automata for analysis by statistical model checking. As a significant modelling example, a HEMS is proposed which implements an admission control strategy able to maintain the in-home energy consumption under a given threshold. Instead of reacting to an overload condition, the strategy is able to prevent an overload upfront by predicting the effect that the admission of a new load will have on the consumption curve of the whole system.

Impact of the acceleration intensity of a passenger car in a road test on energy consumption

The study analyses the effect of acceleration intensity on energy consumption and drive efficiency during a road test for a passenger vehicle. The acceleration was carried out from the assumed initial speed to the target speed determined appropriately for individual gear ratios. The tested speeds include values of 40-130 km/h. The intensity of the acceleration process resulted from the selected settings in the powertrain, such as the throttle opening with values from 30 to 100% and gearbox ratio selection. The tested mean accelerations covered the values from 0.2 to 1.5 m/s2. For individual sets of acceleration intensity, gearbox ratio and throttle opening, the unit energy consumption curves and the total efficiency courses of the powertrain system were determined. The average values of the powertrain efficiency obtained during the acceleration tests amounted to the range of 19.38–25.67%, while an increase in the average efficiency was found with a decrease in the gear ratio value and also the influence of the throttle opening was observed. The study assessed also the relationship between the average acceleration for the speed gaining phase and unit energy consumption, where the acceleration increment by 0.1 m/s2 causes a proportional increment in unit total energy consumption by about 0.15J·(kg·m)−1, regardless of the acceleration intensity.

Seismic behaviour of reinforced concrete frame structures with all steel assembled Q195 low yield buckling restrained braces

For the traditional buckling restrained braces (BRBs), the core plate is high yield strength steel, and the external restraint system is concrete filled steel tube), it has the disadvantages of high yield strength and large weight, which will produce a large additional effect on the structure. Under the same member deformation, the material with lower strength has larger plastic deformation and more seismic energy absorption, so it is easier to realize the idea that the BRBs is prior to frame structure failure So, an all steel assembled Q195 low yield point buckling restrained braces (LYBRBs), has been proposed in this paper, and the quasi-static low-cycle reciprocating load test was conducted. The results show that the failure mode of the LYBRBS is tensile failure, its hysteretic curve shows good symmetry, stability and fullness, and the LYBRBs has a good energy dissipation capacity. At the same time, based on a reinforced concrete frame (RCF) structure in the area of 8 degree seismic fortification intensity, the RCF structural model was established, and the dynamic characteristics of the RCF structure were compared by PKPM and SAP2000 software to verify the correctness of the model. On this foundation, push-over analysis of the RCF and RCF structure with the arrangement of the LYBRBs (LYBRB-RCF) is performed. The story drift ratio, the curves of the base shear force-top story drift and the plastic hinge condition obtained by push-over analysis are compared. In addition, the structural time history analysis was conducted by SAP2000 software under the action of frequent and rare earthquakes. The story drift ratio, story shear force, time history of the top displacement and acceleration, energy consumption and hysteretic curve of the LYBRBs were analysed.

An Adaptive Zero-Velocity Interval Detector Using Instep-Mounted Inertial Measurement Unit

The zero-velocity update (ZUPT) technique plays a key role in foot-mounted inertial navigation system, which can minimize the drift errors of low-cost inertial measurement unit (IMU) by resetting the velocity to zero when the foot is stationary. However, it is difficult to detect the zero-velocity interval (ZVI) in mixed locomotion patterns. This article presents a novel ZVI detector based on adaptive simulated energy consumption (SEC) curve. It can identify the ZVIs of various locomotion patterns, such as normal walking, fast walking, running, going upstairs, and downstairs. The SEC curve is generated based on the signal processing approach using the interlaced peak property of the IMU-measured angular rate and acceleration waveforms. Another benefit of the proposed method is that the IMU can be mounted on the instep without restrictions of orientation and position, which improves flexibility and convenience. Experiment results show that the proposed algorithm performs better than three other widely used methods in mixed gait patterns.