Energy Efficiency Optimization Method Research Articles

An Energy Efficiency Optimization Method for Electric Propulsion Units during Electric Seaplanes’ Take-Off Phase

The electric seaplane, designed for take-off and landing directly on water, incorporates additional structures such as floats to meet operational requirements. Consequently, during the take-off taxiing phase, it encounters significantly higher aerodynamic and hydrodynamic resistance than other aircraft. This increases energy demand for the electric seaplane during the take-off phase. A mathematical model for energy consumption during this stage was developed by analyzing resistance, using the propeller pitch angle as an optimization variable. This study proposes a coupled energy efficiency optimization method for the take-off phase of an electric seaplane’s electric propulsion unit (EPU). The method aims to determine an optimal propeller pitch angle configuration aligned with the seaplane’s design criteria. This ensures that the propeller output thrust meets minimal requirements during take-off while enhancing energy efficiency. Experimental validation with the two-seater electric seaplane prototype RX1E-S has demonstrated that selecting the optimal propeller pitch angle can effectively reduce energy consumption by approximately 10.4%, thereby significantly enhancing flight efficiency.

Energy Efficiency Optimization Method of WDM Visible Light Communication System for Indoor Broadcasting Networks

Energy Efficiency Optimization Method of WDM Visible Light Communication System for Indoor Broadcasting Networks

A multi-objective energy efficiency optimization method of ship under different sea conditions

With the increasing need for environmental protection and the related advancement of relevant laws and regulations, ship energy efficiency optimization has become an important research topic. In this study, a multi-objective optimization model of ship energy efficiency based on the multi-objective particle swarm optimization algorithm is designed. The model uses the main engine speed as the optimization variable, and the ship's fuel consumption and sailing time as optimization objectives. Additionally, considering that the ship's sailing state can be affected by different sea conditions, the sea conditions are clustered by using the K-means clustering algorithm. Then, the route is divided, and the main engine speed is optimized for each segment. The results show that the solutions can reduce the fuel consumption by up to 25.28 % or the sailing time by up to 17.08 %. In the Ideal Solutions can reduce the fuel consumption by up to 3.98% or reduce the sailing time by up to 2.37%. Thus, the proposed model could effectively optimize the ship's energy efficiency. And it is necessary to consider the relationship between fuel consumption and sailing time when identifying the most suitable solution to meet real-world needs.

Research on Multi-Objective Energy Efficiency Optimization Method of Ships Considering Carbon Tax

Various measures have been taken to improve ship energy efficiency while decreasing CO2 emissions. In this work, the navigation environment between Wuhan and Shanghai in China has been classified based on an improved K-means algorithm in order to realize route division. A fuel consumption prediction model considering the navigation environment factors has been established. Consequently, speed optimization models with multiple different optimization objectives have been constructed and tested based on an actual case using an artificial fish swarm algorithm. Finally, sensitivity analysis has been carried out focusing on the navigation time, fuel price, charter rate, free carbon credits, and carbon tax rate. The results show that the total shipping cost and CO2 emissions could be reduced by 0.94% and 0.38%, respectively, after the optimization. Considering a carbon tax policy with a tax rate of roughly 1300 RMB/t, the optimization result (including the carbon tax cost) is close to the compromised solution of multi-objective optimization, and the corresponding carbon tax rate can provide a useful reference for policymakers.

On the Energy Efficiency of Multicell Massive MIMO with Antenna Selection and Power Allocation

The energy consumption of massive multiple-input multiple-output (MIMO) systems increases with the number of antennas. Optimizing the energy efficiency (EE) of massive MIMO systems is one of the ways to achieve green communication. This paper proposes an EE optimization method that genetic algorithm-based antenna selection and power allocation (GA-AS+PA) for the downlink of a multicell massive MIMO system under the restriction of the users’ sum-rate. First, we use the genetic algorithm to determine the active transmitting antenna of each base station (BS). Then, the transmission power for each user is allocated using the convex optimization method. Finally, the EE of system is optimized under the achieved optimum BS’s transmit power and the number of active antennas. From the simulation results, the GA-AS+PA method can improve the EE of the system while meeting user sum-rate requirements, which achieves better performance compared with random antenna selection+equal power allocation method (RAS+EPA), random antenna selection+power allocation method (RAS+PA), the antenna selection method based on genetic algorithm+equal power allocation method (GA-AS+EPA), and equal power allocation (EPA) these four methods. The EE of the proposed GA-AS+PA method is improved by 33.3% compared to the EPA method.

Design and Optimization of Children’s Education Online Monitoring System Based on 5G and Internet of Things

At present, the problem of children’s education informationization is becoming more and more prominent. With the strengthening of national education, children’s online education has become one of the main forms. In order to educate children online, this paper proposes an online education system based on 5G technology and Internet of Things technology, which mainly solves the problems existing in current education. The results show the following: (1) UI test results basically meet the requirements of users; for the pursuit of personalized users, further development and improvement are needed. (2) Compared with children’s module, the response time of manager module increased by 12%, and all the response times did not exceed 10s, so the user experience was very good. The access success rate of children’s module is 99%, and that of managers’ module is 98%. It is necessary to further study what factors interfere with the success rate, which leads to the failure of 100% success. (3) After optimization, the total energy consumption is the lowest compared with NOMA and TDMA, which is the best energy efficiency optimization method to solve the communication of Internet of Things. The simulation of the system is in good condition, and the follow-up research work of developers will take this system as the core to improve more details, hoping to put it into market operation formally.

Energy efficiency optimization for ecological 3D printing based on adaptive multi-layer customization

Abstract This paper presents an energy efficient optimization method for ecological 3D printing based on Adaptive Multi-Layer Customization (AMC). With the widespread application in end-use fields, the 3D printing (3DP) has already attracted great ecological attentions in energy efficiency, material saving and emissions reduction for better sustainability. Firstly, the energy and material consumption model of 3DP is built by decomposing 3DP into three sub-systems: thermal, mechanical, and auxiliary. The potential impacts on the ecological environment can be further evaluated by correlated degree using covariance analysis. The customized parameters of 3DP such as adaptive layer thickness, infill patterns, infill trajectories are sequentially and deeply investigated to form customization schemes for higher success rate by parameter combination. Considering the implicit non-linear relationships between energy efficiency and the customized parameters, adaptive Generative Adversarial Network (GAN) is built to improve calculation accuracy and prevent premature convergence inspired by Game Theory. To determine the best customization scheme according to the requirements, the mathematical model of AMC is built using multi-objective optimization (MOO). The physical experiment of energy efficiency is implemented by testing energy consumption, temperature, emissions and scanning electron micrograph (SEM). The energy efficiency is improved by maximum ratio of 11.51% and the maximum total carbon emission is reduced by 49.91% for ecological 3DP. The experiment proves that the AMC method can improve energy efficiency of complex functional specimens in highly customized fields such as medical healthcare and astronautics manufacturing industry.

An Energy Efficiency Optimization Method Based on Decoupling in Cloud Computing

The cloud resource is becoming a key infrastructure carrying the various types of applications, and providing basic support for emerging areas such as artificial intelligence, big data, and internet of things. It is an important driving force for the development of science and technology. Cloud computing has laid the development direction and foundation for future computing. Because cloud is composed of large-scale distributed resources, resource allocation and optimization of computing efficiency in cloud computing is a very important task. In order to adapt to the complex use environment of multi-load parallel in cloud computing, this paper proposes a decoupling method in virtualization environment, which can optimize the energy allocation strategy of distributed systems and effectively improve the efficiency of cloud resources.

An Energy Efficiency Optimization Method for Fixed Pitch Propeller Electric Aircraft Propulsion Systems

As a key component of electric aircrafts, electric propulsion systems provide sufficient thrust for electric aircrafts. By improving its efficiency, the power consumed by the electric propulsion system can be reduced. Therefore, the maximum range of electric aircrafts, which is limited by the energy density of batteries, can be further increased. Upon investigating the power demand and the energy consumption of the electric propulsion system in each flight profile phase of fixed pitch propellers electric aircrafts, this paper proposes a novel energy efficiency optimization method tailored for fixed pitch propeller electric aircrafts by minimizing the total energy consumption of one flight over the maximum efficiency of the propeller. The proposed method is adopted to the design of a fixed pitch propeller electric aircraft. Experimental results suggest that the tested fixed propeller electric aircraft designed can achieve better energy efficiency while reducing energy consumption by more than 10%.

Energy efficiency optimization based on household electric water heater aggregation considering user comfort

With the development of new energy power generation, consumption of new energy has become a tricky problem. The aim of this letter is to develop an energy efficiency optimization method considering user comfort, specifically for household electric water heaters (HEWHs), which is effective to improve utilization associated with the new energy. The control cycle is calculated based on aggregate cycling load models, which is not affected by the comfortable conditions of using HEWHs. And, we develop a model to approach the target profile representing power needed to consume, then select the optimal combination of the model using the artificial immune algorithm, calculating the weight coefficient of components in combination using the least squares method. The validity of the method is verified using experiment. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

A Building Energy Efficiency Optimization Method by Evaluating the Effective Thermal Zones Occupancy

Building energy efficiency is strongly linked to the operations and control systems, together with the integrated performance of passive and active systems. In new high quality buildings in particular, where these two latter aspects have been already implemented at the design stage, users’ perspective, obtained through post-occupancy assessment, has to be considered to reduce whole energy requirement during service life. This research presents an innovative and low-cost methodology to reduce buildings’ energy requirements through post-occupancy assessment and optimization of energy operations using effective users’ attitudes and requirements as feedback. As a meaningful example, the proposed method is applied to a multipurpose building located in New York City, NY, USA, where real occupancy conditions are assessed. The effectiveness of the method is tested through dynamic simulations using a numerical model of the case study, calibrated through real monitoring data collected on the building. Results show that, for the chosen case study, the method provides optimized building energy operations which allow a reduction of primary energy requirements for HVAC, lighting, room-electricity, and auxiliary supply by about 21%. This paper shows that the proposed strategy represents an effective way to reduce buildings’ energy waste, in particular in those complex and high-efficiency buildings that are not performing as well as expected during the concept-design-commissioning stage, in particular due to the lack of feedback after the building handover.