Average Energy Consumption Research Articles

Task offloading strategy and scheduling optimization for internet of vehicles based on deep reinforcement learning

Driven by the construction of smart cities, networks and communication technologies are gradually infiltrating into the Internet of Things (IoT) applications in urban infrastructure, such as automatic driving. In the Internet of Vehicles (IoV) environment, intelligent vehicles will generate a lot of data. However, the limited computing power of in-vehicle terminals cannot meet the demand. To solve this problem, we first simulate the task offloading model of vehicle terminal in Mobile Edge Computing (MEC) environment. Secondly, according to the model, we design and implement a MEC server collaboration scheme considering both delay and energy consumption. Thirdly, based on the optimization theory, the system optimization solution is formulated with the goal of minimizing system cost. Because the problem to be resolved is a mixed binary nonlinear programming problem, we model the problem as a Markov Decision Process (MDP). The original resource allocation decision is turned into a Reinforcement Learning (RL) problem. In order to achieve the optimal solution, the Deep Reinforcement Learning (DRL) method is used. Finally, we propose a Deep Deterministic Policy Gradient (DDPG) algorithm to deal with task offloading and scheduling optimization in high-dimensional continuous action space, and the experience replay mechanism is used to accelerate the convergence and enhance the stability of the network. The simulation results show that our scheme has good performance optimization in terms of convergence, system delay, average task energy consumption and system cost. For example, compared with the comparison algorithm, the system cost performance has improved by 9.12% under different task sizes, which indicates that our scheme is more suitable for highly dynamic Internet of Vehicles environment.

An intelligent energy minimization algorithm with virtual machine consolidation for sensor-based decision support system

Cloud computing has many benefits for businesses because of its distinctive qualities, including scalability, flexibility, on-demand service, and security. A good task scheduler is required to increase the efficiency of a cloud system, which performs numerous jobs simultaneously. On-demand access to resources that have been virtualized is made available as a service without the need for further waiting. For task scheduling issues based on a makespan limitation, energy consumption is decreased, significantly reducing energy cost. Additionally, the complexity of scheduling issues has increased primarily due to the application's lack of a makespan constraint. Unfortunately, reducing the energy cloud services use presents special research issues and difficulties. More precisely, because of the diversity of servers found in cloud centers, it is challenging to choose the best servers for cloud-based decision support systems to reduce energy usage. The presented approach is innovative and could be applied for complex applications while maintaining an average energy consumption for the running resources, which is a big challenge. The current process finds an optimized energy minimization in the cloud with a combination of Virtual Machine consolidation. The outputs were considered in terms of Energy, Virtual Machine Migration, Performance Degradation, Aggregated Ideal Time Factor, and Aggregated Overload Time Fraction. The obtained results in terms of the existing state of art approaches are far better than the competing approaches.

Food Security Analysis of Melon Farmer Households During the Covid-19 Pandemic in Tuban Regency

The impact of the coronavirus entering Indonesia is felt by various parties, including farmer households. In addition to the obligation of farmers to supply food for the entire community, they are also pressured to pay attention to the nutritional intake of their households. High-nutrition food is needed during the COVID-19 pandemic to maintain immunity, but it is difficult to fulfill during the pandemic due to the increasing prices of food. If production and sales of agricultural products decrease, the ability of households to consume food that is suitable in quantity and quality will also decrease. However, the agricultural sector in Indonesia shows positive growth. This study aimed to determine the household food security of melon farmers based on 1) food quantity, from EAR and PAR results, and 2) food quality, from Desirable Dietary Pattern scores. Determination of the research location purposively. Food consumption data were obtained from food recall. The results of the analysis based on the quantity aspect showed that the average actual energy consumption was 1,854.78 kcal/capita/day (88.3%), while protein consumption was 68.47 grams/capita/day (120.1%). The results of the analysis of the quality of food consumption are 81.5, it can be seen that the household food consumption of melon farmers from the aspect of quantity and quality has not been achieved.

Effects of Neuromuscular Electrical Stimulation on Fasting Blood Glucose

Muscle contraction during physical activity improves glucose metabolism and glycemic control by facilitating muscle glucose uptake and utilization. Neuromuscular electrical stimulation (NMES) is an involuntary alternative mode of muscle contraction and is widely applicable and suitable for individuals with sedentary lifestyle including people with physical functional limitations and other mobility impairments. Use of NMES has the potential to improve metabolic health analogous to voluntary exercise. Purpose: To determine the acute effect of 30 mins of NMES on energy metabolism and fasting blood glucose level. Methods: Fifteen participants without diabetes participated in the study (Males: 6; Females: 9; Age: 33.80 ± 12.99 years; BMI: 32.59 ± 9.17 kg/m2). Each participant underwent 30 mins of cyclical NMES on the quadricep muscles of both legs while lying supine on a bed. NMES was applied at a frequency of 50Hz with a pulse width of 300μs up to maximum tolerable intensity. Blood glucose and lactate levels were determined at baseline and at 5 mins intervals during the stimulation. Energy expenditure and substrate utilization were measured at baseline and monitored continuously during the NMES via indirect calorimetry. The experimental procedure was performed in a fasting condition and was directly preceded by a day of dietary control. Results: A decreasing blood glucose effect of NMES was observed between baseline glucose level (95.94 ± 7.81 mg/dL) and after 25 mins (92.85 ± 8.82 mg/dL) and 30 mins (91.79 ± 9.00 mg/dL) of NMES (p<0.05). Average energy expenditure rate during the 30min of NMES (19.37 ± 4.49 Kcal/kg/day) was relatively higher than that of the resting baseline rate (19.24 ± 3.57 Kcal/kg/day) (p<0.05). Blood lactate level and respiratory exchange ratio (substrate utilization) however, remained unaffected throughout the experimental procedure. Conclusion: Neuromuscular electrical stimulation is effective in decreasing blood glucose level and hence could serve as a beneficial intervention for blood glucose management. Supported by NIH Grant R01 DK132430 This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Age and Energy Analysis for $L$th Best Relay Enabled Cooperative Status Update Systems With Short Packet Communications

In the Internet of Things monitoring systems, the fresh information delivery and efficient energy usage are two significant design concerns. However, they generally cannot be simultaneously achieved. In this paper, we investigate the fundamental tradeoff between the age of information (AoI) and energy consumption (EC) for a generalized <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell$</tex-math></inline-formula> th best relay selection policy in two-hop cooperative status update systems with automatic repeat request (ARQ), where the source delivers the sensed status update to the destination through multiple decode-and-forward relays. Among these relays, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell$</tex-math></inline-formula> th best relay (i.e., the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell$</tex-math></inline-formula> -th earliest one that correctly decodes the update packet) will be selected as a forwarder to relay the data to the sink. When a decoding failure occurs, we can either retransmit the old packet to save sensing EC or sense and transmit a new one to reduce AoI. Accordingly, the classical ARQ and truncated ARQ protocols are employed in the second hop. By considering short packet-based transmissions, the average AoI and average EC expressions are derived, and the weighted combination of them is minimized to attain the tradeoff. Numerical results demonstrate that an optimal packet size that can trade off age and energy always exists. Moreover, the best relay does not always minimize the weighted combination, and the truncated ARQ is superior to the classical one. As a promising candidate, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell$</tex-math></inline-formula> th best relay selection can be implemented when either the system energy resource is limited or the best relay is unavailable.

Tingkat Konsumsi Energi, Durasi Penggunaan Gawai, dan Status Gizi Anak Sekolah Dasar

Nutritional status is influenced by the level of food consumption, especially energy. Nutritional status is indirectly affected by the use of gadgets. This study aims to determine the relationship between the level of energy consumption and duration of use of gadgets with the nutritional status of elementary school children in Mengwi. This type of research is an observational study with a cross-sectional design and uses 83 samples with ages of 9-11 years. The data was collected using the 24-hour recall method and questionnaire. The data was presented with a frequency table and then analyzed using Pearson. The results showed that the average energy consumption level of the sample was 103%, and the average duration of the sample's use of gadgets was 4 hours 25 minutes; 16.9% of the samples with overweight nutritional status and 9.6 with obesity nutritional status had consumption levels above the requirement, 7.2% had more nutritional status, and obesity had an excessive gadget use duration. The results of the Pearson show a relationship between the level of energy consumption and nutritional status with value (p-value=0.00), and there is no relationship between the duration of use of gadgets with nutritional status with value (p-value=0.53). There is a significant relationship between the level of energy consumption and the nutritional status of elementary school students in the Mengwi District. It is expected that elementary school students in Mengwi District will be able to pay attention to nutritional intake, especially energy consumption, and increase their physical activity.

Multi-Objective Immune Optimization of Path Planning for Ship Welding Robot

In order to improve the welding efficiency of the ship welding robot, the path planning of the welding robot based on immune optimization is proposed by taking the welding path length and energy loss as the optimization goals. First, on the basis of the definition of the path planning of the welding robot, the grid modeling of the robot’s working environment and the triangular modeling of the welding weldments are carried out. Then, according to the working process of the welding robot, the length objective function, including the welded seam path and the welding torch path without welding, is constructed, and the energy loss function is constructed based on the kinematics and Lagrange function. Finally, the immune optimization algorithm based on cluster analysis and self-circulation is introduced to realize the multi-objective optimization of the path planning for the ship welding robot. The test results of four kinds of ship welding weldments show that compared with the simple genetic algorithm, immune genetic algorithm, ant colony algorithm, artificial bee colony, particle swarm optimization, and immune cloning optimization, the proposed multi-objective immune planning algorithm is the best in terms of planning path length, energy consumption, and stability. Furthermore, the average shortest path and its standard deviation, the average minimum energy consumption and its standard deviation, and the average lowest convergence generation and its standard deviation are reduced by an average of 9.03%, 54.04%, 8.23%, 19.10%, 27.84%, and 52.25%, respectively, which fully verifies the effectiveness and superiority of the proposed welding robot path planning algorithm.

Local bit line 8T SRAM based in-memory computing architecture for energy-efficient linear error correction codec implementation

Memory reliability is a critical issue in SRAM-based In-Memory Computing (IMC) architecture. The rapid advance in transistor technology makes SRAM more sensitive to soft errors. Various developments have recommended different methods to prevent store data corruption using Hamming Code. In order to encode or decode the information, a piece of Error Correction Code (ECC) hardware is necessary, which results in an area and energy overhead. In this paper, an IMC-based linear Error Correction Codec (IMC-ECC) hardware for parity check bit generation (encoding) and syndrome vector generation (decoding) is implemented by using the proposed Transmission Pass Gate (TPG) Local Bit-line (LB-8T) SRAM structure based IMC architecture. The local bit line structure resolves the half-select issues that occur for bit-interleave architecture. The TPG improves the write stability by delivering both strong VDD and GND. Furthermore, the proposed IMC-ECC scheme, along with bit-interleave architecture, makes the IMC architecture more resilient to multi-bit soft error. The proposed IMC-ECC scheme is validated by implementing a 4 Kb LB-8T SRAM array in 65 nm CMOS technology and Hamming code (7, 4) as an example. The proposed IMC-ECC scheme achieves the average energy consumption of 0.294 pJ/bit and 0.075 pJ/bit for the encoding and decoding process, respectively, at a supply voltage of 1 V.

Marine food consumption by adult Atlantic salmon and energetic impacts of increased ocean temperatures caused by climate change

Atlantic salmon Salmo salar are experiencing widespread population declines, and reductions in growth and survival in the marine environment are contributing factors. Our aims were to estimate marine food consumption of adult salmon and to determine how energetics would be directly affected by the increased ocean temperatures associated with climate change. We tagged previous spawners on outward migration (body size 76–119 cm) with archival tags and used a bioenergetic model to combine in situ temperature recordings with individual data on body growth. Average energy consumption was estimated to be 331–813 kJ per day, which is equivalent to 5–11 prey fish with an average body mass of ca. 15 g. Energy content of prey was the most important factor determining food consumption required to maintain growth. Conversely, the increases in ocean temperatures expected with climate change were predicted to have limited physiological effects on energy budgets and limited impact on the food consumption needed to maintain growth. We conclude that climatic warming will impact Atlantic salmon primarily through changes in prey availability and ecosystem structure rather than the direct effects of temperature on physiological performance.

Investigation and Optimization of Effects of 3D Printer Process Parameters on Performance Parameters.

Professionals in industries are making progress in creating predictive techniques for evaluating critical characteristics and reactions of engineered materials. The objective of this investigation is to determine the optimal settings for a 3D printer made of acrylonitrile butadiene styrene (ABS) in terms of its conflicting responses (flexural strength (FS), tensile strength (TS), average surface roughness (Ra), print time (T), and energy consumption (E)). Layer thickness (LT), printing speed (PS), and infill density (ID) are all quantifiable characteristics that were chosen. For the experimental methods of the prediction models, twenty samples were created using a full central composite design (CCD). The models were verified by proving that the experimental results were consistent with the predictions using validation trial tests, and the significance of the performance parameters was confirmed using analysis of variance (ANOVA). The most crucial element in obtaining the desired Ra and T was LT, whereas ID was the most crucial in attaining the desired mechanical characteristics. Numerical multi-objective optimization was used to achieve the following parameters: LT = 0.27 mm, ID = 84 percent, and PS = 51.1 mm/s; FS = 58.01 MPa; TS = 35.8 MPa; lowest Ra = 8.01 m; lowest T = 58 min; and E = 0.21 kwh. Manufacturers and practitioners may profit from using the produced numerically optimized model to forecast the necessary surface quality for different aspects before undertaking trials.

Electrochemical Lithium Extraction with Gas Flushing of Porous Electrodes.

Electrochemical extraction of lithium from seawater/brine is receiving more and more attention because of its environment-friendly and energy-saving features. In this work, an electrochemical lithium extraction system with gas flushing of porous electrodes is proposed. We verified that the operation of multiple gas washes can significantly reduce the consumption of ultrapure water during the solution exchange and save the time required for the continuous running of the system. The water consumption of multiple gas flush operations is only 1/60 of that of a normal single flush to obtain a purity close to 100% in the recovery solution. By comparing the ion concentration distribution on the electrode surface in flow-through and flow-by-flow modes, we demonstrate that the flow-through mode performs better. We also verified the lithium extraction performance of the whole system, achieving a purity close to 100% and average energy consumption of 0.732 kWh∙kg-1 in each cycle from the source solution of the simulated Atacama salt lake water. These results provide a feasible approach for the large-scale operation of electrochemical lithium extraction from seawater/brine.

Semantic-Based Multi-Objective Optimization for QoS and Energy Efficiency in IoT, Fog, and Cloud ERP Using Dynamic Cooperative NSGA-II

Regarding enterprise service management, optimizing business processes must achieve a balance between several service quality factors such as speed, flexibility, and cost. Recent advances in industrial wireless technology and the Internet of Things (IoT) have brought about a paradigm shift in smart applications, such as manufacturing, predictive maintenance, smart logistics, and energy networks. This has been assisted by smart devices and intelligent machines that aim to leverage flexible smart Enterprise Resource Planning (ERP) regarding all the needs of the company. Many emerging research approaches are still in progress with the view to composing IoT and Cloud services for meeting the expectation of companies. Many of these approaches use ontologies and metaheuristics to optimize service quality of composite IoT and Cloud services. These approaches lack responsiveness to changing customer needs as well as changes in the power capacity of IoT devices. This means that optimization approaches need an effective adaptive strategy that replaces one or more services with another at runtime, which improves system performance and reduces energy consumption. The idea is to have a system that optimizes the selection and composition of services to meet both service quality and energy saving by constantly reacting to context changes. In this paper, we present a semantic dynamic cooperative service selection and composition approach while maximizing customer non-functional needs and quickly selecting the relevant service drive with energy saving. Particularly, we introduce a new QoS energy violation degree with a cooperative energy-saving mechanism to ensure application durability while different IoT devices are run-out of energy. We conduct experiments on a real business process of the company SETIF IRIS using different cooperative strategies. Experimental results showed that the smart ERP system with the proposed approach achieved optimized ERP performance in terms of average service quality and average energy consumption ratio equal to 0.985 and 0.057, respectively, in all simulated configurations compared to ring and maser/slave methods.

Solar-powered Parking Analytics System Using Deep Reinforcement Learning

Advances in deep vision techniques and the ubiquity of smart cameras will drive the next generation of video analytics. However, video analytics applications consume vast amounts of energy as deep learning techniques are power-hungry. In this article, we focus on a parking video analytics platform and propose RL-CamSleep, a deep reinforcement learning-based technique, to actuate the cameras to reduce the energy footprint while retaining the system’s utility. Our key insight is that many video-analytics applications do not need to be always operational, and we can design policies to activate video analytics only when necessary. We design two modes of operation for the reinforcement learning (RL) controller: (i) cloud-based mode and (ii) grid-isolated solar-powered mode. In the cloud-based mode, the controller runs on the cloud to control the cameras, whereas, in the solar-powered mode, the RL controller is constrained by the energy produced by solar. We evaluate our approach on a city-scale parking dataset having 76 streets spread across a city. Our analysis shows RL-CamSleep can learn an adaptive policy that reduces the average energy consumption by 76% and achieves an average accuracy of 98%. For the grid-isolated mode, RL-CamSleep outperforms other baseline techniques demonstrating the need for adaptive policy in energy-constrained environments.

A Novel Topology Optimization Protocol Based on an Improved Crow Search Algorithm for the Perception Layer of the Internet of Things

In wireless sensor networks, each sensor node has a finite amount of energy to expend. The clustering method is an efficient way to deal with the imbalance in node energy consumption. A topology optimization technique for wireless sensor networks based on the Cauchy variation optimization crow search algorithm (CM-CSA) is suggested to address the issues of rapid energy consumption, short life cycles, and unstable topology in wireless sensor networks. At the same time, a clustering approach for wireless sensor networks based on the enhanced Cauchy mutation crow search algorithm is developed to address the issue of the crow algorithm's sluggish convergence speed and ease of falling into the local optimum. It utilizes the Cauchy mutation to improve the population's variety and prevent settling for the local optimum, as well as to broaden the range of variation and the capacity to carry out global searches. When the leader realizes he is being followed, the discriminative probability is introduced to improve the current person's location update approach. According to the simulation findings, the suggested CM-CSA algorithm decreases the network's average energy consumption by 66.7%, 50%, and 33.3% and enhances its connectivity performance by 52.9%, 37.6%, and 23.5% when compared to the PSO algorithm, AFSA method, and basic CSA algorithm.

Experimental investigation of an innovative solar dryer integrated with the thermal energy storage system

An experimental investigation was performed in the solar dryer integrated with the thermal energy storage system for drying tomato slices and Calotropis gigantea leaves. The experiment was conducted for the drying of tomato slices and C. gigantea leaves based on field performance data under the climatic conditions of Gurugram regions of India. It was observed that the average specific energy consumption, collector efficiency and drying efficiency of the evacuated tube collector-based solar dryer with thermal energy storage during drying of tomato slices and C. gigantea leaves are 2.321 kWh/kg, 61.70%, 36.33%, and 2.882 kWh/kg, 57.70%, 33.08% respectively. The efficiency of the system is enhanced by 30% with the use of thermal energy storage. The benefit-cost ratio of the system is 3.4 with an attractive payback period of 2.8 years. Highlights: Development of solar dryer with thermal energy storage system is carried out. Drying of tomato slices and Calotropis gigantea leaves is investigated. Performance evaluation of thermal energy storage is carried out. Performance evaluation of solar dryer with thermal energy storage is analysed.

Impacts of Connected and Autonomous Vehicles with Level 2 Automation on Traffic Efficiency and Energy Consumption

Evaluating the economic benefits of traffic optimization from connected and autonomous vehicles (CAVs) and relevant traffic organization methods is significant, which will help to put forward suggestions for policymakers to promote the application of CAVs. The impacts and related benefits from CAVs with level 2 automation (L2 CAVs) on traffic efficiency and energy consumption of expressways are analyzed in this paper. Average travel time and actual road capacity are on behalf of traffic efficiency while average electric energy consumption is used to compute traffic energy consumption. The corresponding traffic economic benefits consist of travel-time-saving benefits, road construction benefits, and energy-saving benefits. A benefit evaluation framework is newly proposed and microscopic traffic simulation software is applied as the experiment platform. Different market penetration rates of L2 CAVs and various traffic flow statuses are considered. Besides, dedicated lanes for CAVs are also involved in this research, which are regarded as a traffic organization method expected to promote the realization of CAV’s traffic benefits. It is found that L2 CAVs can save the average travel time and reduce average energy consumption for a single vehicle in most scenes. However, negative impacts on energy consumption are observed in several scenes due to the increase of actual road capacity. Positive economic benefits are obtained as soon as the traffic flow rate is out of saturation, which become increasingly higher as CAV’s market penetration rate turns larger. Additionally, amplification in traffic economic benefits appears only if CAV lanes are provided under proper conditions.

Multirobot Task Planning Method Based on the Energy Penalty Strategy

In multirobot task planning, the goal is to meet the multi-objective requirements of the optimal and balanced energy consumption of robots. Thus, this paper introduces the energy penalty strategy into the GA (genetic algorithm) to achieve the optimization of the task planning of multiple robots in different operation scenarios. First, the algorithm model is established, after which the objective function is constructed by taking the energy excess of the relative average energy consumption of each robot as the penalty energy, along with the total energy consumption of multiple robots. In the genetic operation, two-segment chromosome coding is used to realize the iterative optimization of the number and task sequences of robots through diversified cross and mutation operators. Then, in the task scenario with obstacles, the A* (A-Star) algorithm and GA are used to plan the optimal obstacle avoidance path and to realize the secondary optimization of the robot task sequence without changing the number of tasks. During optimization, the energy penalty strategy imposes punishment on the objective function through the size of the penalty energy, enabling the robot energy consumption to reach an equilibrium state by maintaining the total energy consumption at the minimum. Finally, the MATLAB platform is used to conduct the simulation experiments to compare with basic genetic algorithms and penalty function algorithms, after which the optimal allocation scheme and energy consumption iteration of the algorithm are analyzed under different robot numbers, task numbers, and task scenarios, and the simulation results include robot task sequences, total energy consumption, average energy consumption, and standard deviation of energy consumption.

Based on human-like variable admittance control for human–robot collaborative motion

AbstractAdmittance control of the robot is an important method to improve human–robot collaborative performance. However, it displays poor matching between admittance parameters and human–robot collaborative motion. This results in poor motion performance when the robot interacts with the changeable environment (human). Therefore, to improve the performance of human–robot collaboration, the human-like variable admittance parameter regulator (HVAPR) based on the change rate of interaction force is proposed by studying the human arm’s static and dynamic admittance parameters in human–human collaborative motion. HVAPR can generate admittance parameters matching with human collaborative motion. To test the performance of the proposed HVAPR, the human–robot collaborative motion experiment based on HVAPR is designed and compared with the variable admittance parameter regulator (VAPR). The satisfaction, recognition ratio, and recognition confidence of the two admittance parameter regulators are statistically analyzed via questionnaire. Simultaneously, the trajectory and interaction force of the robot are analyzed, and the performance of the human–robot collaborative motion is assessed and compared using the trajectory smoothness index and average energy index. The results show that HVAPR is superior to VAPR in human–robot collaborative satisfaction, robot trajectory smoothness, and average energy consumption.

Comparing energy profiles of different building types by determining their sensitivities

ABSTRACT The main purpose of this study is to understand the energy consumption profiles of different building types by identifying and comparing their energy consumption parameters with their sensitivities to each other while indicating the long-term energy consumption implications on energy cost and CO2 emissions. A duplex house, apartment building, convention center, and school building were selected to develop dynamic simulation models for the investigation. The annual electric and gas demand of buildings were calculated and compared based on their energy usage intensity (kWh/m2), emission intensity (kg CO2/m2), and cost intensity (USD/m2) with their sensitivity to selected parameters. Further, the 50-year longevity of the buildings that include the selected materials’ production and demolition stages were included to estimate the lifetime. Also, retrofitting strategies including renewable technology integration were developed for each building type separately based on their energy concerns. The average energy consumption of parameters from highest to lowest are space heating, area lights, equipment, space cooling, hot water, fans, and pumps, respectively. The critical primary energy consumption of the educational building and convention center is 43% and 38% from equipment and 30% from space heating for residential buildings. A large portion of the emission is allocated to energy consumption during the lifetime of buildings, mainly due to electricity consumption. The energy costs mainly are also electricity related. Although the effects of retrofit strategies are different depending on the energy behavior of buildings, the most effective scenario is envelope retrofitting, followed by lighting improvement.

Vibration quality evaluation of reinforced concrete using energy transfer model

Vibration is an important step to ensure the compactness of reinforced concrete. In this study, the energy transfer model is firstly proposed based on the working mechanism of the internal vibrator and vibration energy theory to evaluate the vibration quality, and the reliability of the model is verified by the experiment of sinking ball. In addition, the optimal combinations of process parameters are obtained. The worse the fluidity of concrete and the larger the aggregate proportion, the faster the energy attenuation in concrete, but the total energy absorbed by concrete and the energy transfer efficiency of the vibrator increase. The overall efficiency is greater than 57% and the highest average efficiency is 66.1%. The rebar not only accelerates the energy attenuation rate but also reduces the effective energy in the concrete. In the experiments, the average energy consumption rates of rebar are 28.5%, 57.3% and 70.9% with the reinforcement spacing of 20, 10 and 5 cm respectively. The proposed model provides a new method to evaluate the vibration quality, which contributes to dealing with unqualified areas in time and providing reliable process parameters for vibrating construction.