Estimated Energy Expenditure Research Articles

Estimation of Energy Consumption and CO2 Emissions of the Water Supply Sector: A Seoul Metropolitan City (SMC) Case Study

A model that computes the per-unit process energy consumption, energy intensity, CO2 emission, and CO2 intensity of water treatment plants is developed. This model is used to estimate the total energy consumption of six water treatment plants in Seoul Metropolitan City (SMC), which is comprised 80–85% for finished water pumping, 6–10% for ozone disinfection, 2–4% for rapid mixing, and 1–3% for non-process loads. The model results are validated against actual data for 2020 and 2021. The net energy consumption considering renewable energy production and use is then calculated, and the corresponding level of CO2 emissions is predicted. Four scenarios based on the projected water requirements for the year 2045 were evaluated as follows: increased energy efficiency in finished water pumping (Scenario 1), increased renewable energy production in water treatment plants (Scenario 2), increased energy efficiency in raw water pumping (Scenario 3), and reduced water supply per capita (Scenario 4). Compared to a baseline do-nothing scenario (Scenario 0), the net energy consumption is reduced by 3.57%, 2.61%, 3.42%, and 4.67% for Scenarios 1–4, respectively. Scenario 4, which is a water-driven approach, is best for reducing CO2 emissions, while Scenario 1 and 3, which are energy-driven approaches, are more effective at reducing CO2 intensity.

Data-driven energy efficient speed planning for battery electric industrial vehicles: Forklift as a case study

Battery electric industrial vehicles (BEIVs) offer significant environmental protection and decarbonization potential for rapidly growing logistics and transportation sectors in China. However, the limited driving range remains a major obstacle to the widespread adoption of BEIVs. Eco-driving is an effective solution that can improve energy efficiency and extend the driving range of vehicles by controlling driving behavior and speed. Therefore, this paper introduces an energy efficient speed planning method for BEIVs, in which data-driven models are developed to estimate energy consumption. Real-world driving data collected from a BEIV are used to model the energy consumption by curve fitting, random forest, and LSTM. The speed planning process is considered a Markov Decision Process, which is solved using Dynamic Programming and Q-learning, respectively. The case study of a battery electric forklift shows that speed planning can reduce energy consumption by 13%–27% compared to time-optimal speed, while only increasing the driving time by 10%–20%.

Estimated Energy Expenditure in Youth While Playing Active Video Games: A Systematic Review.

Sedentary behavior and inadequate energy expenditure are serious global public health concerns among youngsters. The exponential growth in technology emerges as a valuable opportunity to foster physical activity, particularly through active video games. We performed a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines in PubMed, Web of Science, Cochrane, and Scopus to provide a comprehensive view of the literature on energy expenditure levels among adolescents while playing active video games. Among the 574 manuscripts identified at the first screening stage, 23 were retained for analysis. Ten studies were characterized by longitudinal and thirteen by cross-sectional designs. The results showed that short-term active video games elicited energy expenditure values comparable to moderate-intensity physical activity (3-6 METs). However, in intervention programs (with at least six weeks) the results indicate no significant effects of active video games on youngsters' energy expenditure levels and physical activity profiles between baseline and follow-up assessments. Overall, active video games based on sports and dance were the most used, and boys tended to achieve higher energy expenditure than girls. The diversity of methods implemented limits comparing results and drawing generalized conclusions. However, considering its attractiveness to youth, active video games might emerge as a complementary tool to traditional physical activities promoted in schools and local communities. Details regarding gender differences and contradictory results of longitudinal approaches should be considered in future research based on standardized methods.

China's local-level monthly residential electricity power consumption monitoring

Timely implementation of electricity power consumption (EPC) analysis and evaluation helps assess energy-saving potential and develop energy management plans. Yet, timely EPC data of subnational regions are usually public unavailable, especially for developing countries. Lacking timely and local EPC data makes it difficult to satisfy the requirements for local governments to quickly monitor energy consumption and establish early warning mechanisms. As a solution, this study establishes a quick accounting method for local residential EPC utilizing nighttime light data, based on which we investigate the variations in residential EPC before and after the COVID-19 pandemic. Our method has demonstrated robust performance in estimation and proven its reliability. Compared to previous studies, the proposed methodology has four advantages. First, it monitors local-level residential EPC data at a monthly scale, which allows for observing EPC statistics at a higher frequency. Second, it separates residential EPC from urban and rural sectors, which is useful for analyzing urban-rural dichotomy energy consumption patterns. Third, it provides a less costly and time-consuming approach to estimating energy consumption over space and time. Fourth, it covers almost the whole China (except Tibet, Hongkong, Macao, and Taiwan due to data unavailability), providing useful information for achieving national energy-saving goals. Based on the methodology, we see great potential for developing “global-country-region” energy monitoring through satellite image data service globally.

IMU-Based Energy Expenditure Estimation for Various Walking Conditions Using a Hybrid CNN-LSTM Model.

In ubiquitous healthcare systems, energy expenditure estimation based on wearable sensors such as inertial measurement units (IMUs) is important for monitoring the intensity of physical activity. Although several studies have reported data-driven methods to estimate energy expenditure during activities of daily living using wearable sensor signals, few have evaluated the performance while walking at various speeds and inclines. In this study, we present a hybrid model comprising a convolutional neural network (CNN) and long short-term memory (LSTM) to estimate the steady-state energy expenditure under various walking conditions based solely on IMU data. To implement and evaluate the model, we performed level/inclined walking and level running experiments on a treadmill. With regard to the model inputs, the performance of the proposed model based on fixed-size sequential data was compared with that of a method based on stride-segmented data under different conditions in terms of the sensor location, input sequence format, and neural network model. Based on the experimental results, the following conclusions were drawn: (i) the CNN-LSTM model using a two-second sequence from the IMU attached to the lower body yielded optimal performance, and (ii) although the stride-segmented data-based method showed superior performance, the performance difference between the two methods was not significant; therefore, the proposed model based on fixed-size sequential data may be considered more practical as it does not require heel-strike detection.

Energy Consumption Displays in Electric Vehicles: Differential Effects on Estimating Consumption and Experienced Energy Dynamics Awareness.

The effects of three prototypical designs of energy consumption displays on energy-specific situation awareness were examined. Energy efficiency is crucial for the sustainability of technical systems. However, without accurate situation awareness of energy dynamics (energy dynamics awareness, EDA) it can be challenging for humans to optimize the use of energy resources of electric vehicles (EVs) through their behavior. We examined three prototypical energy display designs that varied by their informational value to support EDA. Furthermore, we investigated the differential effects on EDA measured by (1) a newly constructed scale (experienced EDA), (2) estimating energy consumption, and (3) identifying efficient trips in an online experiment. Participants (N = 82) watched standardized driving scenes (videos) of EV trips presenting the energy displays. We found a strong effect of display type on experienced EDA, with the trace display being the most supportive. The EDA scale showed excellent internal consistency. The consumption estimation and efficient trip identification indicators were not affected by the display type. The study indicates that experienced EDA is immediately affected by displays with higher information value, but performance might need more time and training. More research is needed to investigate the cognitive processes related to EDA and to examine how distinct display elements enhance EDA. Results from this research can be used as guidance for the design of energy displays, especially in EVs. The EDA scale can be used as an evaluation measure in the human-centered design process of energy displays.

Model-based, fully simulated, system-level power consumption estimation of IoT devices

Internet of things (IoT) gaining more importance due to its crucial role in pervasive computing and also Industry 4.0. Since the number of IoT devices is scaling up to multiple dozens of billions, the importance of energy efficiency is significantly increased. With the consideration of huge variety of IoT device hardware and software, a comprehensive model and estimation methodology on energy consumption is necessary as an enabler. IoT devices are also frequently updated, upgraded and maintained because of the evolving nature of the requirements and market demands. Each and every such operation has an effect on the power consumption and arose the necessity for a new energy consumption modeling and estimation. This process is applicable for development of IoT devices, as well as the maintenance phase. Since the variety of designs is unlimited, and battery capacity is usually fixed, or a cost factor, a generic, fully simulated, model-based energy consumption estimation of IoT devices is crucial. In this study, we aim to address this problem via proposing fully simulated, model-based, system-level power estimation approaches, as well as their success rate in typical real-life scenarios. It can be seen that the proposed methodology has high accuracy over %97. For the realization of the best-proposed approach, we used Open Virtual Platform (OVP) as an instruction set accurate simulator.

Finite-time stochastic control for complex dynamical systems: The estimate for control time and energy consumption

Controlling complex dynamical systems has garnered significant attention within academic circles in recent decades. While most existing works have focused on closed-loop control schemes with infinite-time durations, this paper introduces a novel finite-time, closed-loop stochastic controller that considers control time, energy consumption, and their dependence on system parameters. This stochastic control technique not only enables finite-time control in chaotic dynamical systems but also facilitates finite-time synchronization in unidirectionally coupled systems. Importantly, our proposed scheme offers several advantages over existing deterministic finite-time controllers, particularly in terms of time and energy consumption. Through numerical experiments utilizing random ecosystems, neural networks, and Lorenz systems, we provide evidence of the effectiveness of our analytical results. We anticipate that this stochastic scheme will find wide-ranging applications in the control of complex dynamical systems and in achieving network synchronization.

Real Time Estimation of Energy Expenditure during Physical Activity Using Ensemble Methods

Energy Expenditure (EE) estimation is crucial in health monitoring, as well as in fitness and athletic performance. Monitoring energy expenditure is a key aspect of weight management for individuals while athletes need to optimize their energy intake and expenditure to maximize efficiency in training and recovery. It is a widely researched subject and, depending on the setting, is commonly measured either by direct/indirect calorimetry or with algorithms largely based on heart rate tracking and/or movement tracking using accelerometers. The approach presented here utilizes heart rate, 3-axis acceleration data and breathing rate produced by Zephyr BioHarnessTM, as well as demographic information to produce an accurate (R2 = 95.7%, RMSE = 0.01 Kcal/s) real time EE estimation during walking and running. This methodology could be generalized to other activities using other transmit capable ergo physiological monitoring devices.

Filippov Systems With Nondifferentiable and Unbounded Delays: Periodicity, Stabilization, and Energy Consumption Estimation

Filippov Systems With Nondifferentiable and Unbounded Delays: Periodicity, Stabilization, and Energy Consumption Estimation

Genetic algorithms for optimizing the layout of wireless charging networks

This study explores the improvement of wireless charging network configurations for electric cars (EVs) using genetic algorithms, with the goal of increasing charging efficiency and network performance. The network optimization process takes into account the starting characteristics of include their geographical coordinates, power capacity, and beginning energy levels. Examination of the distance matrix exposes diverse distances between nodes, which impact energy consumption and charging efficiency. The energy consumption estimates between pairs of nodes illustrate the charging needs across the network, revealing that nodes that are farther away have greater energy consumption. The use of genetic algorithms yields a wide range of layouts that are assessed based on their fitness ratings, indicating the excellence of configurations in terms of coverage and connection. Percentage change study demonstrates the modifications in power capacity and node energy levels after optimization, showing prospective improvements in charging capabilities and efficiency. The correlation between node location and energy use is apparent, as nodes in closer proximity demonstrate decreased energy utilization. The convergence of fitness scores demonstrates the algorithm's effectiveness in achieving solutions that are very close to ideal, resulting in significant improvements in charging coverage and energy efficiency. The study highlights the effectiveness of genetic algorithms in improving wireless charging networks, providing valuable information on spatial optimization tactics, energy use patterns, and the resulting improvements in network performance. These results have implications for creating wireless charging infrastructures that are more efficient and long-lasting, in order to satisfy the changing needs of electric car charging networks.

Experimental study of hydrodynamics in a vortex contact apparatus

The outcomes of experimental investigations into ascertaining the hydraulic resistance of a vortex apparatus, featuring swirling gas and liquid flows under diverse design and operational conditions, are showcased. These examinations on pressure loss in vortex-type apparatuses facilitate the estimation of energy consumption for activities such as dust removal, contact heat exchange, gas absorption purification, and other processes conducted within gas-liquid systems. Notably, it has been observed that the hydraulic resistance of the scrutinized vortex apparatus is markedly lower compared to the pressure drop encountered in nozzle-type and plate-type apparatuses. Furthermore, it does not surpass the resistance exhibited by high-efficiency vortex-type apparatuses of alternative designs. The hydraulic resistance of hollow vortex apparatuses, equipped with tangential swirlers, is contingent upon variables such as gas phase velocity, liquid flow rate, apparatus design parameters, and the physical properties of the working media. Through the meticulous analysis of experimental data, calculation dependencies have been derived to determine the resistance coefficients of both dry and irrigated vortex apparatuses. These dependencies prove to be practical and convenient from an applied standpoint.

Bi-Objective Job-Shop Scheduling Considering Human Fatigue in Cobotic Order Picking Systems: A Case of an Online Grocer

Increasing online retail has resulted in increased automation in order picking systems, leading to new challenges and opportunities in task scheduling. The job-shop scheduling problem is an optimization problem essential in such systems, but existing JSP literature often overlooks workplace fatigue, which harms employees’ well-being and costs U.S. employers up to €127 billion annually. In this work, we propose fatigue consideration in the job-shop scheduling problem in a cobotic order picking system to mitigate its negative effects. We present a new bi-objective mixed integer nonlinear programming problem formulation that considers worker fatigue and productivity during schedule optimisation. To put the results of simulated optimisation in perspective, we experimentally validate the fatigue model and scheduling results in a real operation. The mathematical model finds solutions that conventional single-objective optimisation cannot, allowing fractional fatigue distribution improvements more than 4x larger than the decrease in productivity they require in 53% of the considered virtual cases. The experiments show that our predictive fatigue model has an average RMSE of 2.20 kcal/min in estimating energy expenditure rates compared to heart rate measurements. It also shows a low correlation, meaning it is unfit for application. On the other hand, fatigue-conscious schedules show no clear benefit regarding measured and perceived fatigue. However, the scheduling model could also use heart rate measurements that do not show these inaccuracies. Our study highlights the need to further develop and validate the mathematical formulation and fatigue model and extend to other human factors and indirect fatigue effects.

Estimating Energy Consumption for Bangladesh Transportation Sector: Fuel Energy Computation Method

Estimating Energy Consumption for Bangladesh Transportation Sector: Fuel Energy Computation Method

Predictive machine learning for optimal energy management in sustainable transportation systems

This study explores the use of predictive machine learning techniques to enhance energy management in sustainable transportation systems, with a specific emphasis on electric vehicles (EVs). The analysis of EV specifications has shown a wide variety of battery capacities, ranging from 55 kWh to 75 kWh. These capacities have a direct impact on the energy storage capacity and the possible driving range of the vehicles. The range of vehicle weights, ranging from 1400 kg to 1700 kg, emphasized the possible effects on energy consumption rates and overall efficiency. The performance capabilities were shown with maximum speeds ranging from 160 km/h to 200 km/h. The energy consumption rates ranged from 0.18 kWh/km to 0.25 kWh/km, suggesting different levels of efficiency. An analysis of energy management data revealed that the lengths traveled varied from 180 km to 220 km, while the average speeds ranged from 50 km/h to 60 km/h. These variations directly affected the rates at which energy was used. The vehicles exhibited higher efficiency metrics by attaining energy consumption rates of 4.0 km/kWh to 5.6 km/kWh. The analysis of ambient variables indicated temperature fluctuations ranging from 20°C to 30°C, as well as a variety of terrain types that impact driving conditions and energy requirements. Predictive machine learning models demonstrated high accuracies, with Mean Absolute Error (MAE) values ranging from 0.13 to 0.18 kWh/km, Root Mean Squared Error (RMSE) values ranging from 0.18 to 0.22 kWh/km, and R-squared (R^2) scores ranging from 0.80 to 0.88. These results emphasize the need of using predictive machine learning to estimate energy consumption, optimize energy management systems, and address sustainable transportation concerns in order to improve the efficiency and sustainability of electric vehicles.

Introducing fine grained energy consumption variables into a public passenger transport simulation in SUMO

Improving energy efficiency in public passenger transport is key to achieving sustainability goals. In many developing countries, public passenger transport relies heavily on fossil fuels, which necessitates a strong focus on reducing emissions from energy consumption. Surprisingly, few studies have addressed this issue in the context of daily operations, with most research focusing on operational and infrastructural changes. This paper aims to fill this gap by introducing detailed energy consumption variables into a public transport simulation model using SUMO, a tool known for its seamless integration with Python. The development of two Python scripts underpins this endeavour. The first script facilitates the automatic generation of scenarios, allowing the manipulation of parameters such as station spacing, line length, passenger capacity and waiting times, among others. The second script is dedicated to estimating energy consumption for each scenario. Together, these tools allow us to calculate average energy consumption for different scenarios and modes of public transport. As a result, they provide valuable insights for the formulation of public policies aimed at saving energy in different public transport environments and lay the foundation for the integration and development of intelligent transport systems and simulation-based digital twins.

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.

Review of Energy-Related Machine Learning Applications in Drying Processes

Drying processes are among the most energy-intensive industrial processes. There is a need for development of the efficient methods needed for estimating, measuring, and reducing energy use. Different machine learning algorithms might provide some of the answers to these issues in a faster and less costly way, without the need for time-consuming and expensive experiments done at different scales of the dryers. The aim of this paper was to provide a comprehensive overview of machine learning applications for addressing energy-related challenges by exploration of different energy types and energy reduction opportunities. Also, the analysis of the applied algorithms, their specific applications and a critical evaluation of the obtained results are provided. The paper is based on the necessity of the improvements in energy use needed for drying related on the existing data. The overview of the ways for such achievements, and a general classification of machine learning algorithm are the background of the paper. The methods used are the machine learning techniques employed in different energy-related issues for drying processes. The paper focuses on the applications of artificial neural networks and other machine learning algorithms and models for different energy-related issues, including different energy types applications, challenges associated with energy consumption, and opportunities for energy reduction. Not only the applied algorithms, but also their specific applications, and the statistical analysis of the obtained results are also overviewed. Finally, a critical evaluation of the findings highlighting the potentials of machine learning algorithms in addressing energy-related challenges (such as estimation of energy consumption, opportunities for energy reduction, and use of different energy sources) is provided. The presented analysis underscored the effectiveness of machine learning applications for these purposes.

Research of Computer Sensor Technology in the Examination of Energy Metabolism of Preschool Children with Muscle Inactivity

At present, the accelerometer has gradually become a general-purpose instrument that can objectively measure human movement, and has been gradually applied to the research on the human movement of preschool children. Among them, the accelerometer is used to measure the human movement of preschool children. methodological research. It mainly includes: determination of exercise intensity threshold, estimation of exercise energy consumption, estimation of exercise energy consumption, etc. This article compares the parameters, functional characteristics and evaluation indicators of the three most common accelerometers used for physical activity measurement in preschool children. The purpose is to provide some methodological references for relevant researchers in the field of children's physical activity.

Measured versus estimated energy requirement in hospitalized patients

Failure to identify a patient's energy requirement has a variety of consequences both physiological and economical. Previous studies have shown that predictive formulas, including the Harris Benedict equation (HB), both over- and underestimates energy requirement in severely ill patients and healthy younger adults, compared to the golden standard, indirect calorimetry (IC). The comparison between measured and estimated energy requirements in hospitalized patients in regular wards is underreported. The aim of this study was to assess the agreement between measured energy requirements and requirements estimated by HB in the individual hospitalized patients, and to investigate whether those findings were associated with other specific patient characteristics. IC (n=86) was used to measure resting energy expenditure (REE) and bioimpedance analysis (BIA) (n=67) was used for body composition in patients admitted to Aalborg University Hospital. Furthermore, height, weight, body mass index, calf circumference, while information regarding hospital ward, vital values, dieticians estimated energy requirements and blood samples were collected in the patients' electronic medical records. Bland-Altman plots, multiple linear regression analysis, and Chi2 tests were performed. On average a difference between IC compared with the HB (6.2%), dietitians' estimation (7.8%) and BIA (4.50%) was observed (p<0.05). Association between REE and skeletal muscle mass (SMM) (R2=0.58, β=149.0kJ), body fat mass (BFM) (R2=0.51, β=59.1kJ), and weight (R2=0.62, β=45.6kJ) were found (p<0.05). A positive association between measured REE and HB were found in the following variables (p<0.05): CRP, age, surgical patients, and respiratory rate. This study found a general underestimation of estimated energy expenditure compared to measured REE. A positive correlation between measured REE and SMM, BRM and weight was found. Lastly, the study found a greater association between CRP, age, surgical patients, and respiratory rate and a general greater than ±10% difference between measured and estimation of energy requirements.