Energy Consumption Optimization Model Research Articles

Energy consumption optimization in green cognitive radio networks based on collaborative spectrum sensing

In the realm of green communications, the focus is on achieving high spectrum efficiency and low energy consumption. This paper addresses the crucial goal of reducing energy usage in green cognitive radio networks (CRNs) during communication between secondary users (SUs) and primary users (PUs). This paper proposed an energy consumption optimization model (ECOM) for green CRN utilizing collaborative spectrum sensing thereby minimizing the environmental impact and prolonging the operational lifetime of devices. The collaborative spectrum sensing proved its role in optimizing the energy consumption in the green CRN. An energy-efficient scheduling algorithm is implemented in ECOM, in which the SUs can be scheduled to perform their sensing process in a time-division manner to reduce energy consumption. Applied collaborative spectrum sensing serves as a valuable resource for researchers, network operators, and policymakers seeking to balance the increasing demand for wireless communication services with the imperative of sustainability. The simulation results and mathematical proof emphasize that ECOM demonstrates reduced energy usage and increased average effective throughput when compared to other recent models.

Optimizing low-power task scheduling for multiple users and servers in mobile edge computing by the MUMS framework

In today's increasingly popular Internet of Things (IoT) technology, its energy consumption issue is also becoming increasingly prominent. Currently, the application of Mobile Edge Computing (MEC) in IoT is becoming increasingly important, and scheduling its tasks to save energy is imperative. To address the aforementioned issues, we propose a Multi-User Multi-Server (MUMS) scheduling framework aimed at reducing the energy consumption in MEC. The framework starts with a model definition phase, detailing multi-user multi-server systems through four fundamental models: communication, offloading, energy, and delay. Then, these models are integrated to construct an energy consumption optimization model for MUMS. The final step involves utilizing the proposed L1_PSO (an enhanced version of the standard particle swarm optimization algorithm) to solve the optimization problem. Experimental results demonstrate that, compared to typical scheduling algorithms, the MUMS framework is both reasonable and feasible. Notably, the L1_PSO algorithm reduces energy consumption by 4.6 % compared to Random Assignment and by 2.3 % compared to the conventional Particle Swarm Optimization algorithm.

Energy consumption optimization model and system for the pellet drying process of straight grate

The drying process is a crucial stage in the thermal treatment of iron ore pellets in a straight grate, as it has a direct impact on pellet quality and energy consumption. Consequently, this paper develops a simulation model for the pellet bed drying process, which is validated through industrial data and enables the visualization of temperature and moisture distribution within the pellet bed. Based on the simulation model, an optimization model for the drying process is developed to minimize the specific energy consumption per tonne of green pellets. The constraints of the drying process are the moisture content requirements and burst temperature. A genetic algorithm is employed to optimize the parameters such as trolley speed, pellet bed thickness, air temperature, and air pressure. The results show that, with an absolute error criterion of 10 °C, the temperature calculation accuracy of the simulation model exceeds 86%, effectively describing the drying process in a straight grate sintering machine. Under yield limitations, the optimal cumulative energy consumption per tonne of green pellet is reduced by 1.50% during the drying process. Finally, a simulation and optimization system for the drying process in the straight grate is implemented in C++. This system not only generates essential data for designing the drying section in a straight grate but also lays a theoretical foundation for system optimization and optimal control.

Construction of new energy consumption optimization model based on improved pathfinder algorithm

As traditional energy reserves continue to decline, the importance of new energy sources increases. However, the current traditional power system often fails to consider new energy sources, particularly in power supply systems that integrate multiple new energy sources. The cost, efficiency, and environmental factors seriously affect the energy system’s efficiency. Therefore, this proposal presents a multi-objective optimization discrete assignment pathfinder algorithm. The algorithm can handle multi-objective optimization problems and adapt to various constraints, providing a more precise optimization scheme for new energy systems. The experimental results indicated that the proposed research method exhibits better performance compared to other algorithms of the same type. Compared with the multi-objective multivariate universe optimization algorithm and the multi-objective sparrow search algorithm, the research method was ahead in terms of fitness value by 9.54% and 14.67%, respectively. Meanwhile, in the grid simulation, the research method achieved an average efficiency of 96.16%, which is better than the comparative algorithms by 6.57–14.02%. The study not only improves the optimization efficiency of new energy consumption, but also provides a powerful decision support tool for the planning and operation of wind farms. It is of great significance for the improvement of power system efficiency and decarbonization, and helps to promote the large-scale integration and sustainable development of new energy.

Modeling and optimization of energy consumption in wire cut electrical discharge machining

WEDM (Wire Electrical Discharge Machining) is widely implemented in the manufacture of dies and challenging materials due to its use of spark discharge to shape a workpiece. However, existing research focuses primarily on enhancing processing efficiency, accuracy, and quality, with limited investigation into energy conservation and emissions reduction during processing. This paper constructs an energy consumption model to forecast energy usage during electric machining and proposes a low energy consumption optimization model founded on processing parameters. A case study revealed that the energy consumption model is 98.06 % accurate throughout the processing process and at least 92 % precise in predicting energy consumption for a single process. Upon utilizing the low energy consumption optimization model, the results show a reduction in energy consumption and carbon emissions by 11.5 % and 5.74 %, respectively, compared to traditional methods. The models presented in this paper can enhance EDM machining efficiency while concurrently minimizing energy consumption and carbon emissions, thereby supporting the adoption of energy-saving and efficient practices in electric machining.

Optimization model of new energy consumption based on load demand-side response

As a technically and economically feasible and potentially resource-rich regulation tool, demand-side response can effectively strengthen the guarantee of new energy consumption, and is expected to play an enhanced key role in the construction of new power systems. This paper analyzes the factors affecting the linkage between demand-side and new energy generation side, the promotion of new energy consumption by demand-side response, and proposes a method to respond to the change of new energy output from the customer side, using interruptible load, transferable load, shiftable load, and curtailable load strategies to respond to the peak regulation problem brought by new energy access to the grid. The optimal demand-side response strategy is obtained by constructing a new energy consumption optimization model and solving it with the direct non-inferiority solution method, taking into account the economic and new energy consumption objectives. Finally, the effectiveness of the method is verified by simulating the IEEE 37-node system with multiple new energy configurations.

Energy Consumption Modeling and Optimization of a Hobbing Machine Tool Considering Multi-Axis Coupling

Energy consumption of machine tools has always been a hot issue in the manufacturing sector due to the concern about climate change. To decrease the energy consumption of machine tools, this paper investigates the multi-axis coupling mechanism of a hobbing machine tool during machining from the design stage, and develops an energy consumption model under multi-axis coupling of the whole machine. Firstly, the multi-axis coupling mechanism in the actual machining process is studied. The hob speed and the feed speed of Z axis moving component will affect the hobbing force, and the component of hobbing force in each direction will affect the load and energy consumption of each axis. Meanwhile, the tracking error of each axis is reduced by a sliding mode controller. Next, a comprehensive energy consumption optimization model of a hobbing machine tool under multi-axis coupling is constructed. Furthermore, three optimization algorithms are implemented to the energy consumption model. Compared with the initial scheme, the simulation experiment results of the case-study indicate that the hobbing machine tool energy consumption is reduced by 1.19%, the steady-state error of each axis is reduced by 35.27%, 21.70%, and 36.34%, respectively, and the maximum deformation of the tool holder is reduced by 10.91%. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is dedicated to dealing with the issue of a hobbing machine tool energy consumption. Based on our previous work, the multi-axis coupling mechanism of the gear hobbing machine tool is investigated, and a comprehensive energy consumption model of the gear hobbing machine tool is proposed. The design variables of the energy consumption optimization model include the structural parameters of the Z axis moving component and the sliding mode controller parameters of the three axes (B, C, and Z axis). Three optimization algorithms are used to drive the optimization model. The simulation results verify the feasibility of the method. The control performance of the system is improved, and the mass of moving component is reduced.

Joint optimization of energy conservation and transfer passenger service quality in rail transit system

ABSTRACT The complex electricity transmission process and transfer passenger demand in the rail transit system make it challenging to formulate optimal timetables and train speed profiles to meet the needs of the system. This study optimizes train timetable to meet energy conservation and passenger service quality of cross-mode rail transit system during different operation periods. Firstly, based on the spatiotemporal travel data of transfer passengers, the train-time matrix is proposed to construct a bi-objective optimization model (BOOM) that incorporates the total energy consumption optimization model (TECOM) and total waiting time optimization model of transfer passenger (TWTOM). It can better combine the passenger transport scheme and the train energy-saving operation strategy, thus improving energy-saving performance and passenger transport efficiency and simplifying the difficulty of mathematical modelling of such BOOM. Then, a method for determining the appropriate weight coefficient ratio and the improved non-dominated sorting genetic algorithm II (NSGA-II) are applied to obtain the efficient Pareto front solutions. Taking the numerical experiments as test cases, the improved NSGA-II can get a efficient solution faster than the traditional NSGA-II. Finally, two numerical experiments using real-world data are conducted to verify the practicability of the optimization model and the effectiveness of improved NSGA-II. The optimization results can reduce energy consumption by up to 11.02% and passenger waiting time by 137,320 s, respectively. This study helps to improve the energy efficiency and passenger service quality of cross-mode rail transit systems, and also provides a reference for dispatchers to optimize train timetable.

Building energy consumption optimization method based on convolutional neural network and BIM

The increasing tension of energy supply and demand makes the optimization of building energy consumption more and more concerned by researchers. Based on the theory of convolutional neural network and BIM (Building Information Modeling), a building energy consumption optimization model is constructed. The optimization parameter solving problem of convolutional neural network is solved. In the simulation process, a calculation model of the same size as Revit's three-dimensional model is established in eQUEST software, and the basic analysis parameters of the model, such as geographical location, meteorological data and other information, component materials, and running time table are set as unified standards. The energy consumption simulation analysis was carried out for the self-built model and the model automatically generated by the improved DOE-2 file in eQUEST software. The body coefficient of the building is 0.370, and the window-wall ratios in the east, west, south and north directions are 0.07, 0.21, 0.30 and 0.16, respectively, which all meet the requirements of relevant specifications. Compared with the scheme before optimization, it is found that building energy consumption is reduced by 24.53%, natural lighting is increased by 18.98%, and natural pressure par hours are increased by 10.57%.

A Minimizing Energy Consumption Scheme for Real-Time Embedded System Based on Metaheuristic Optimization

With the widespread application of real-time embedded systems, the contradiction between the energy consumption requirements of modern processors and the limited battery capacity becomes more obvious. Dynamic voltage scaling (DVS) has been proven to be one of the most effective technologies for energy management. However, recent studies have shown that the use of DVS leads to a significant increase in the transient fault rate of processors as the characteristic size of logic gates (or transistors) gets smaller and smaller. In this paper, we consider the problem of assigning processing frequencies to a group of periodic real-time tasks so as to minimize the overall energy consumption under the constraints of time and reliability. Firstly, under the DVS, we take the reliability of the embedded systems into consideration through the regularization terms and present the energy consumption optimization model based on the meta-heuristic algorithms. Secondly, a novel algorithm for adaptive differential whale swarm optimization (ADWOA) is proposed according to the optimization requirements. Finally, the optimized data are saved on the chain through the storable feature of the blockchain for the necessary queries. It is worth noting that the on-chain data contains the intrinsic characteristics of the embedded system, which may give rise to the disclosure of processor privacy. Therefore, we come up with the differential privacy on-chain creating algorithm (DPCA) to protect the privacy of data on the chain. Experimental results show that ADWOA can minimize the energy consumption in real-time embedded system on the premise of ensuring system reliability and privacy.

Genelleştirilmiş regresyon modelleri kullanılarak enerji talebi ve tüketimi için bir modelleme yaklaşımı

Energy management is an important process for maintaining available energy resources and meeting basic household energy needs. Many studies seek to optimize the household energy consumption patterns to manage the load demand and minimize energy costs. Adopting such optimizations in conflict-affected countries is more beneficial due to limited energy sources. This study identifies the optimal energy consumption model for households in northern Syria. The objective is to identify the most cost-efficient energy sources while considering the prices, average monthly household income, the main source of electricity, battery storage capacity, and energy demands for space heating, water heating and cooking. One hundred and thirty-six (136) standardized surveys of residential households are collected and used as a test case. Statistical analysis of the data was carried out using the R-Studio software, where Poisson regression and negative binomial regression were employed. Findings revealed that the Negative Binomial (NB) model used has high explanatory power. In addition, the energy sources used for space heating and water heating have a direct impact on monthly expenditures. The produced model showed that the most cost-effective energy sources are coal for space heating and natural gas and kerosene for water heating.

A Two-Stage Optimization Algorithm of the Train Traction Energy Consumption in Urban Rail Transit

The optimization of the train traction energy consumption in urban rail transit is a nonlinear optimization problem, and its solution is a very difficult task. This article focuses on the optimization of train traction energy consumption on the line with multiple stations. First, a single-objective nonlinear energy-saving optimization model is established for a given line and total travel time. Second, a two-stage optimization algorithm is proposed to solve this model. In the first stage, for a given interval and travel time, an optimization model of the traction energy consumption is established by using the distance discretization method. A dichotomous iterative algorithm based on energy consumption is proposed to search the optimal traction energy consumption. By calling this algorithm repeatedly, the energy–time curve of each interval can be obtained. In the second stage, according to these energy–time curves, the total travel time of a train on the line is allocated to each interval one by one in the form of time slices. And finally the optimal travel time and optimal traction energy consumption of the train on each interval are obtained. This method does not need to set the train operation mode sequence in advance but adaptively selects the energy-saving operation mode according to the line constraints and the train parameters. The up-direction from Yizhuang to Tongjinan of the Beijing Metro Yizhuang line in China is selected as the test section. The experimental results demonstrate the effectiveness and computational efficiency of our proposed methods and algorithms. Moreover, the dichotomous iterative algorithm runs so fast that it can be used in real-time driver advisory systems or automatic train operation systems.

Energy Consumption Optimization Model of Large Parallel Natural Gas Pipeline Network: Using Compressors with Multiple Operating Modes

In long-distance parallel pipelines, compressor stations are built together, and connecting pipes and cut-off valves are arranged between pipelines, which makes it possible for different stations to operate jointly or independently. In order to reduce pipeline energy consumption, this paper considers the valve switch state, deeply analyzes the station operation mode, and establishes the optimal model of minimum energy consumption of a parallel pipeline. Taking the second and third parallel pipelines of China’s west-east gas transmission as an example, the phasor particle swarm optimization algorithm is used to solve the model. The results show that after optimization, the internal flow was redistributed and energy consumption was reduced by 15.54%.

Energy consumption optimization of tramway operation based on improved PSO algorithm

Tramway has the advantages of reliable operation, energy-saving, and environmental protection, which is an important way to alleviate traffic pressure. Energy consumption optimization of tramway operation is significant in reducing the operation cost. Therefore, the energy consumption optimization model of tramway operation based on the improved PSO algorithm is developed. Firstly, the energy consumption model of the tramway is established based on the energy-saving operation strategy. Then, the competition mechanism-based particle swarm optimization (CM-PSO) algorithm is developed to improve energy-saving performance. Finally, the Guangzhou Haizhu tramway is taken as an example to illustrate the effectiveness of the developed method. The results show that the developed model effectively reduces the tramway operation energy consumption and performs better than the previous methods.

Optimization of energy efficiency, energy consumption and CO2 emission in typical iron and steel manufacturing process

Improving energy efficiency of the iron and steel manufacturing process (ISMP) plays a vital role in saving energy and reducing CO2 emissions. However, there is currently a lack of research on assessment method and improvement rules of energy efficiency of the ISMP. In addition, the impact of energy efficiency on CO2 emissions and energy consumption is rarely mentioned in previous studies. To fill this gap, this study establishes an assessment and optimization model of energy efficiency, energy consumption and CO2 emissions in the ISMP. Taking material parameters, process parameters and reaction conditions as constraints, this study optimizes a practical ISMP with maximizing energy efficiency as the optimization goal, and further analyze energy consumption and CO2 emissions of typical ISMP. After optimizing, energy efficiency of the ISMP is increased from 64.00% to 68.20%, energy consumption of the ISMP is reduced from 15.76 GJ/t-steel to 12.31 GJ/t-s, and the CO2 emission of the ISMP is reduced from 1215.2 kg/t-steel to 815.3 kg/t-s. It is found that injected coal ratio of blast furnace, charge structure of blast furnace, iron-steel interface and casting-rolling interface have a greater impact on energy efficiency, energy consumption and CO2 emission of the ISMP.

Data mining with 12 machine learning algorithms for predict costs and carbon dioxide emission in integrated energy-water optimization model in buildings

In recent years, various models, which employ green and sustainable energy-supplying systems, have been presented in different ways to optimize water and energy consumption in several countries, pursuing the purpose of reducing water and energy consumption and costs. The present study considered an integrated and unconcentrated water and energy consumption optimization model in the building and investigated it economically and environmentally using mixed-integer linear programming. In this research, the data of different sections, including the data related to the climatic conditions, environment, costs, technologies, etc., were collected. Next, the researchers sought to predict the model results using 12 highly accurate machine learning algorithms and considering four indices to examine the prediction accuracy of the algorithms. The utilization of the machine learning prediction algorithms helped us economically and environmentally investigate and predict the model conditions in every geographical location with respect to the data that varied according to the climatic conditions. The results obtained from investigating the indices of the algorithms for the data examined for the objective functions, including cost optimization and carbon emission reduction, revealed that the prediction accuracy ranged from 0.8 to 0.96, and 0.79 to 0.91 for the first and second objective functions, respectively, in the 12 examined algorithms. Meanwhile, the Light Gradient Boosting Machine and Extra Tree algorithms enjoyed higher prediction accuracy in this research than other algorithms. Next, to analyze the results, the researchers implemented the Principal Component Analysis method to reduce the input data dimension to the algorithms. The results of the algorithms reflected a decline in the prediction accuracy after the dimension reduction. Finally, the effective variables in the prediction accuracy of the algorithms were presented by using the Stepwise Regression method for both objective functions. Overall, the output results of the algorithms showed high prediction accuracy for investigating the model conditions in various geographical regions.

Dynamic Time- and Load-Based Preference toward Optimal Appliance Scheduling in a Smart Home

In this paper, the household appliance scheduling based on the user predefined preferences is addressed. Previous works generally deal with this problem without integration of renewable energy sources (RESs) in smart home. The present paper proposes a new demand side management (DSM) technique considering time-varying appliance preferences and solar panel generation. The branch and bound (B&amp;B) algorithm is developed based on three postulations that allow the time-varying preferences to be quantified in terms of time- and load-based features. Based on the input data including the load’s power rating, the absolute comfort derived from time- and load-preferences, the total energy available from the solar panels as well as the energy purchased from the utility grid, the (B&amp;B) algorithm is run to generate the optimal energy consumption model that would give maximum comfort to the householder based on the mixed-integer linear programming (MILP) technique. To test the performance of the proposed mechanism, three scenarios are considered with local energy production and limited budget for purchasing the energy from the utility grid to cover the user needs. The simulation results reveal that the proposed DSM mechanism based on the MILP method offers maximum level of comfort for all the scenarios within the available energy limitation.

A cooperative inter-provincial model for energy conservation based on futures trading

With rapid development of global society, economic growth is increasingly depending on energy consumption. It will be urgent to explore an effective model to conserve energy while maximizing the socioeconomic benefits. To improve on China’s current territorial management model, we applied market-based means to cooperative energy conservation by constructing an energy conservation model based on futures trading. The model has three components: a function that defines the annual socioeconomic benefits for energy consumption; a method for dividing provinces into buyers and sellers of energy-saving index futures; and a cooperative optimal energy consumption model for the buyer or seller provinces. To demonstrate this approach, we performed an empirical analysis of electricity conservation for four Chinese provinces (Shanxi, Jiangsu, Sichuan, and Gansu). Compared with the current territorial management model, the cooperative model increased the overall socioeconomic benefits for electricity consumption by 18.7% and decreased the overall electricity consumption per unit of socioeconomic benefits by 15.7%. In addition, each province’s socioeconomic benefits from energy consumption improved and the electricity consumption per unit of socioeconomic benefits decreased. Our sensitivity analysis revealed that fluctuation of the futures price (from −50% to +50%) created only small differences in the socioeconomic benefits for all provinces.

An integrated optimization model of metro energy consumption based on regenerative energy and passenger transfer

Energy consumption by metro trains has attracted considerable attention due to economic and environmental concerns. Passengers want convenient travel that takes less time. Based on the requirements of reduced train costs and more passenger comfort, the train control strategy and time parameters need to be adjusted properly. This paper proposes an integrated method for the minimum energy consumption of metro trains and minimum transfer waiting time cost for transfer passengers. First, we used a four-stage control strategy to optimize the train trajectory and generated a power matrix. Then, a tab encoding method was established for the regenerative energy calculation on the basis of the power matrix. To obtain the transfer waiting time of passengers, an arrival time matrix was built that was inspired by the matching operation of the regenerative energy. The genetic algorithm was employed for optimization because of its good searching ability, and the matrix form in this model was suitable for computer programming. Finally, two metro lines from the Nanning rail transit system were selected for the case study. The results show that the proposed method has good efficiency for energy conservation and decreased the transfer waiting time between two metro lines. Further research can be conducted by considering more kinds of passengers from the travel flow in the future.

A novel bi-level distributed dynamic optimization method of ship fleets energy consumption

The optimization of ship energy consumption is attracting a great deal of attention, as societies seek to save energy and reduce emissions. Shipping companies are more concerned with the energy consumption of a ship fleet, as opposed to that of a single ship. Because the energy consumption of a fleet is influenced by multiple factors including environmental factors, port operations and transport demands, an improvement in a single ship's energy consumption does not necessarily mean that the overall energy consumption of a fleet is good. In addition, those factors are usually varying over time, making it hard to optimize the fleet's energy consumption by methods that do not consider these time-varying factors. Therefore, a bi-level distributed dynamic optimization method based on distributed model predictive control is proposed. Moreover, an upper-level optimization model for fleet operational decision-making and a lower-level dynamic optimization model of fleet energy consumption are established. Based on these, a control algorithm for the dynamic optimization of fleet energy consumption is developed. Finally, a case study is carried out to demonstrate the effectiveness of the method. It can further reduce the energy consumption of each ship by at least 1.1% and about 6.8% for the whole fleet.