International Journal of Global Energy Issues (32 papers in press)

Regular Issues

• Price and volatility of rare earths  
  by Auguste Mpacko Priso, Souleymane Doumbia 
  Abstract: The purpose of this paper is to discuss results of a statistical model for volatility of rare earths prices traded at the London Stock Exchange and compare it to the volatility of other metals prices as well as that of other stock prices. Although known for centuries, rare earths have drawn particular attention interest over recent years due to their potential solution to mitigate climate change effects. These metals with exceptional characteristics are used in high-tech product manufacturing, especially those seen as alternative to the consumption of fossil fuels like car batteries. We show that the volatility of all three indexes is persistent. The volatility model which best fits the rare earths prices is a gjrGARCH(1,1) model. This is to our knowledge the first time the persistent volatility framework is applied to price of rare earths. Our work paves the way to many other applications, including volatility forecasts of rare earths price. This latter can help investors improve their decision-making process.
  Keywords: metal prices; rare earths; climate change; volatility models; ARCH; GARCH models.
  DOI: 10.1504/IJGEI.2023.10056317
   
  

Special Issue on: Strategic Planning and Management in Energy

• Optimal renewable distributed generation planning: an up-to-date state-of-the-art review  
  by Ali Tarraq, Faissal El Mariami, Abdelaziz Belfqih, Touria Haidi 
  Abstract: Due to its multiple benefits, the integration of renewable-based distributed generation (RDG) into the distribution network (DN) is of great importance. Yet, given the complexity of this network, optimal distributed generation planning (ORDGP) is considered a complex combinatorial problem, which remains a real challenge for investigators, decision-makers, and investors. This issue involves finding the optimal locations, sizes, power factors, and number of RDGs to be incorporated into the DN to improve the network's overall efficiency while meeting a set of voltage, current, and power constraints. Moreover, the incorporation of uncertainties related to this type of source, especially wind turbine or PV system, decreases the ability of classical and analytical methods to solve the ORDGP problem. For this reason, meta-heuristic and hybrid methods have become very promising essentially because of their randomness. In this context, a systematic and comprehensive literature review on the different definitions, classifications, and interests of RDG, as well as on the different optimisation techniques, represents the main objective of this study. Concisely, this paper provides in-depth knowledge and serves as a useful guide for future researchers and investors in the ORDGP.
  Keywords: renewable distributed generation; optimal renewable DG planning; distribution network; optimisation methods.
  DOI: 10.1504/IJGEI.2023.10054664
   
  
• The prediction of carbon emissions from construction land in central Yunnan urban agglomeration area based on multiple linear regression model  
  by Lede Niu, Jingzhi Lin, Lifang Zhou, Anlin Li, Yan Zhou 
  Abstract: In order to clarify the quantitative relationship between construction land changes and carbon emissions, a prediction method for carbon emissions from construction land in central Yunnan urban agglomeration area based on multiple linear regression model was proposed. Taking the central Yunnan urban agglomeration area as the study area, based on the data of construction land from 2011 to 2020, the carbon emission of construction land was predicted by using the multiple linear regression model. There is a positive correlation between the carbon emissions of the central Yunnan urban agglomeration area and the level of construction land use. From 2011 to 2020, average annual growth rate of construction land area was 8.56%, and the average annual growth rate of carbon emissions was 5.75%. The annual growth rate of carbon emissions from 2021 to 2030 is 0.97%, indicating that the government's carbon emission control measures have achieved good results.
  Keywords: multiple linear regression model; STIRPAT model; central Yunnan urban agglomeration area; construction land; carbon emission prediction.
  DOI: 10.1504/IJGEI.2023.10055017
   
  
• Study on multi-step prediction method of passive energy-saving building energy consumption based on energy consumption perception  
  by Qiliang Yuan 
  Abstract: Passive energy-saving buildings have problems such as low energy consumption prediction accuracy and complex prediction process. A multi-step prediction method for energy consumption of passive energy-saving buildings based on energy consumption perception is proposed. Firstly, the related thermal parameters and building parameters of passive energy-saving buildings are calculated by steady-state calculation. Secondly, the equivalent thermal parameters of building air-conditioning load, air-conditioning load equivalent thermal parameters and air-conditioning load thermal parameters are determined by means of first-order differential equations, and the envelope structure is calculated. Finally, the ultrasonic sensor is set in the energy-saving building, and the energy consumption data of each part of the building is sensed to realise multi-step prediction. The experimental results show that the fluctuation of the predicted energy consumption value of the proposed algorithm is less than 1 J, the prediction accuracy is always above 90% and the time cost is about 1.54 s.
  Keywords: energy consumption perception; passive energy-saving building; multi-step prediction of energy consumption; dynamic calculation; load equivalent thermal parameters.
  DOI: 10.1504/IJGEI.2023.10055019
   
  
• Intelligent forecasting method of distributed energy load based on least squares support vector machine  
  by Yingwei Chen, Zhikui Chang 
  Abstract: Aiming at the problems of long prediction time and low prediction accuracy of traditional distributed energy load intelligent prediction methods, a distributed energy load intelligent prediction method based on least squares support vector machine is proposed. The method of linear interpolation is used to process the missing load data of distributed energy, and the wrong load data of distributed energy are processed horizontally and vertically. On this basis, the t-test standard in probability and statistics method is used to identify the abnormal load of distributed energy. Using least squares support vector machine, a distributed energy load forecasting model is constructed to realise the intelligent forecasting of distributed energy load. The experimental results show that the average MAPE and RMSE of the proposed method are 1.008% and 1048 respectively, and the time of distributed energy load forecasting is 15.8 s. The proposed method can effectively improve the accuracy and efficiency of distributed energy load forecasting.
  Keywords: least squares support vector machine; linear interpolation; t-test criterion; distributed energy; load forecasting.
  DOI: 10.1504/IJGEI.2023.10055013
   
  
• Investment risk prediction method of renewable energy market under the background of carbon neutralisation  
  by Chunxia Liu 
  Abstract: To improve the accuracy of market investment risk prediction and reduce the time consumption of investment risk prediction, this paper proposes a renewable energy market investment risk prediction method under the background of carbon neutralisation. Firstly, based on the background of carbon neutrality, the earned value management theory is used to quantitatively describe the investment risk of renewable energy market. Secondly, aiming at carbon neutralisation, the system dynamics method is used to design the investment risk prediction function of renewable energy market. Finally, the residual test is used to verify the investment risk prediction results of the energy market, so as to realise the investment risk prediction of the renewable energy market. The results show that the accuracy of market investment risk prediction of this method is 96.5%, and the time of investment risk prediction is only 8.6 s. It can accurately predict the investment risk of renewable energy market.
  Keywords: carbon neutralisation; system dynamics method; renewable energy; market investment; risk prediction.
  DOI: 10.1504/IJGEI.2023.10055016
   
  
• A low carbon treatment technology of green building construction waste based on genetic algorithm  
  by Jinhua Kang, Guanglei Zhao 
  Abstract: In this paper, a low-carbon treatment method of green construction waste based on genetic algorithm is proposed in this paper. Firstly, the group organisation method is used to search in the search solution space to calculate the carbon emission of green building construction waste. According to the calculation method of carbon emissions, the product of activity data and emissions is obtained. Then, through the model objectives and constraints, a single cycle multi-objective mathematical model with waste treatment cost and low-carbon emission as the objective function is established. Finally, the genetic algorithm is used to solve the model to realise the low-carbon treatment of green building construction waste. The experimental results show that the proposed low-carbon treatment time of green building waste is only 16.7 s and the carbon emission is only 0.13 g, which can effectively improve the low-carbon treatment efficiency of green building waste.
  Keywords: genetic algorithm; emission factor method; green building; construction waste; low-carbon treatment.
  DOI: 10.1504/IJGEI.2023.10055012
   
  
• An energy efficiency evaluation method of intelligent building based on fuzzy clustering algorithm  
  by Shuai Wang, Lei Sun, Xinjie Yuan 
  Abstract: Aiming at the problems of low evaluation accuracy and long evaluation time in the traditional energy-saving evaluation methods of smart buildings, an energy-saving evaluation method of smart buildings based on fuzzy clustering algorithm is proposed. Firstly, according to the construction criteria of the evaluation index system, establish the energy-saving evaluation index system of smart buildings, obtain the evaluation indexes, then use the grey correlation theory to determine the weight of the energy-saving evaluation index of smart buildings, and measure the energy consumption of smart buildings separately. Finally, establish the fuzzy similarity matrix, and cluster with the network method in the direct clustering method to obtain the optimal clustering scheme and evaluate the energy-saving of smart buildings. The simulation results show that the accuracy of the proposed method is 100% and the evaluation time is within 7 s. The evaluation effect of the proposed method is good and the evaluation efficiency is high.
  Keywords: fuzzy clustering algorithm; grey correlation theory; smart building; energy-saving evaluation; evaluation index system.
  DOI: 10.1504/IJGEI.2023.10055015
   
  
• New energy industry investment risk assessment method based on fuzzy AHP  
  by Lianguang Mo 
  Abstract: In order to improve the accuracy, efficiency and comprehensiveness of investment risk assessment, a new energy industry investment risk assessment method based on fuzzy AHP is proposed. Firstly, the random forest algorithm is used to predict the investment risk of new energy industry. Secondly, the fuzzy AHP method is used to construct the risk assessment system, the normalisation and consistency test are used to deal with the assessment indicators, and the weight of the assessment indicators is calculated. Finally, based on the evaluation index system, a new energy industry investment risk evaluation model based on multiple regression analysis is established to realise the new energy industry investment risk evaluation. The experimental results show that the highest accuracy of the evaluation results of the proposed method is more than 80%, the evaluation efficiency is high, and the evaluation results are more comprehensive.
  Keywords: fuzzy AHP; risk assessment; random forest algorithm; cart algorithm; multiple regression analysis.
  DOI: 10.1504/IJGEI.2023.10055018
   
  
• Study on evaluation method of energy-saving potential of green buildings based on entropy weight method  
  by Wei Yuan, Zhigang Liu 
  Abstract: For green buildings, the effect of energy-saving potential evaluation using the current method is poor and the evaluation efficiency is low. This paper uses the entropy weight method to study the energy-saving potential evaluation method of green buildings. Firstly, the evaluation index system of energy-saving potential of green buildings is established under the principles of scientificity, systematicness and operability, and the evaluation indexes are obtained. Then the entropy weight method is used to determine the index weight and calculate the energy-saving potential index. Finally, the energy-saving potential is evaluated based on the value on the basis of analysing the economic benefits. The simulation results show that the accuracy of the proposed method for energy-saving potential evaluation is up to 100%, the evaluation time is within 5 s, the evaluation accuracy is the highest and the evaluation time is the shortest.
  Keywords: entropy weight method; economic performance; judgment matrix; G value.
  DOI: 10.1504/IJGEI.2023.10055014
   
  
• Layered energy balance control method for renewable energy grid based on island mode  
  by Yao Li, Hui Liu, Wei Xia, Yanwu Ruan, Xiaochuan Guo 
  Abstract: In order to improve load balance and energy efficiency of renewable energy grid, this study designed a layered energy balance control method based on island mode. Firstly, the operation characteristics of renewable energy grid in island mode are analysed, and then the energy balance control of renewable energy grid is divided into three layers, namely, renewable energy output control, grid reactive power control and grid voltage control. The experimental results show that the load waveform of bus voltage tends to be stable after a short abnormal change in the early stage, and the voltage stabilises around 220 kV after 6 s. The maximum renewable energy utilisation rate can reach 95%, indicating that the proposed method achieves the design expectation.
  Keywords: island mode; renewable energy grid; energy control; layered control; voltage load; energy efficiency.
  DOI: 10.1504/IJGEI.2023.10055441
   
  
• Minimisation of fuel cell electric vehicle cost using Cauchy particles swarm optimisation  
  by Intissar Darwich, Islem Lachhab, Lotfi Krichen 
  Abstract: In this paper, enhanced particle swarm optimisation algorithm is suggested that uses mutated inertia weight which is based on Cauchy distribution in order to optimise fuel cell/ultra-capacitor electrical vehicle cost. This approach is dedicated to identify the optimal number of units of each energy source according to the vehicle performances. The proposed algorithm is based on Cauchy operator which substitutes the random function in classic PSO. Moreover, this method operates within constraints and inhibits to fall in local optimum problem. Cauchy distribution function permits to improve the convergence speed algorithm and to benefit global search ability of particle swarm optimisation. Simulation results show that the enhanced particle swarm optimisation contributes better in speed convergence and accuracy in comparison with classic PSO algorithm for solving traction system cost optimisation.
  Keywords: FCHEV; PSO optimisation; Cauchy operator; hydrogen consumption.
  DOI: 10.1504/IJGEI.2023.10055420
   
  
• An analysis of market power in Iran's electricity market with machine learning  
  by Naser M. Rostamnia 
  Abstract: The Iranian electricity market was reformed over the last three decades primarily to promote competition and improve its production efficiency. This paper provides an analysis of competition in the Iranian electricity market. Although other works have provided similar assessments, none has provided a thorough probe over a long period. This paper analyses the Herfindahl Hirschman Index (HHI) of the market for the last decade which has not been done. Also, the paper forecasts the index in the market for the next year to project its direction. Long Short-Term Memory (LSTM) was implemented to forecast the indices in an efficient computational time. Grid search is used to select the optimal model for forecasting, and interactions analyses provide insights into the parameter options that lead to significantly improved accuracies. The results show that the market was unconcentrated from 2012 to 2021. Also, the forecasts show that the market will remain unconcentrated for the next year. Furthermore, the analysis shows that the entrance of new powerplants into the market could reduce the concentration in the market.
  Keywords: market power analysis; Herfindahl Hirschman index; long short-term memory algorithm; hyperparameter optimisation; grid search.
  DOI: 10.1504/IJGEI.2023.10054134
   
  

Special Issue on: Challenges and Sustainable in Energy

• Study of solidification performance of PCM in a triplex-tube thermal energy storage system with double Y-shaped fins  
  by Jun Du, Menghan Li, Fan Ren 
  Abstract: In this study, the phase change material is RT82, it has the disadvantages of low thermal conductivity, the Triplex-Tube Thermal Energy Storage System (TTESS) with double Y shaped fin is used to enhance heat conduction. In this paper, we used the commercial software FLUENT to study the influence of heat transfer fluid, fin material and fin structure parameters on the solidification process by evaluation indexes solidification time, heat release and PCM average temperature. The results show that when the fin length increases from 4 to 8 mm, the solidification time is reduced by 38.03%, the heat release in 180 s is increased by 4.21%, The fin width increased from 0.5 to 1.5 mm, the heat release in 180 s decreased by 5.03% and the solidification time of PCM decreased by 7.27%. Reasonable fin angle, HTF temperature and high-thermal conductivity fin material can also improve the heat transfer of the solidification process.
  Keywords: double Y-shaped fin; triplex-tube thermal energy storage system; numerical simulation; solidification.
  DOI: 10.1504/IJGEI.2023.10054591
   
  

Special Issue on: Intelligent Expert System in Non Conventional Energy Systems

• Smart plant propagation algorithm for the improvement of self-excited induction generator performance  
  by Swati Paliwal, Sanjay Kumar Sinha, Yogesh Kumar Chauhan 
  Abstract: India has taken effective initiatives to generate a massive amount of electrical power from wind energy. In order to strengthen the development of offshore wind power, self-excited induction generators (SEIG) have proven to be the best choice. But the global acceptance of this machine depends on its improved voltage and frequency regulation. Therefore, this work investigated the performance of SEIG in short and long shunt configurations under different loading conditions and at different power factors. This paper employs one of natures most unique and inspired techniques, Plant Propagation Algorithm (PPA), to improve machine performance in terms of flux or voltage. The PPA is based on the propagation strategy of the strawberry plant, which has the potential to colonise new areas in pursuit of better survival chances. From simulated results, it has been observed that the short shunt configuration requires lower shunt and series capacitance in order to improve SEIG performance.
  Keywords: self-excited induction generator; plant propagation algorithm; Newton Raphson method; loading conditions; simulated annealing; wind energy conversion system; machine flux or voltage.
  DOI: 10.1504/IJGEI.2023.10053981
   
  

Special Issue on: Intelligent Expert System in Non-Conventional Energy Systems

• Optimisation of computer network reliability based upon sensor technology and genetic algorithm  
  by Chengqing Gong 
  Abstract: In todays society, there has been a trend towards high digitisation, which means that the importance of computer networks is also increasing. Computer network reliability is the concept of measuring the security and stability of computer network system based on its importance. Nowadays, network reliability detection systems at home and abroad have their own standards. It is not conducive to the measurement between different system networks. This article is dedicated to optimising the reliability of the computer network, making it more convenient, quick and easy to operate. For this reason, this paper proposes a network reliability analysis algorithm based on sensor technology, and then uses genetic algorithm to optimise it. The proposed optimisation algorithm is to solve the problems of high algorithm complexity and low-computational efficiency. In the experiment, the original algorithm was compared with the optimised algorithm. A number of tests have also been conducted for network reliability analysis. Experimental results show that the optimised algorithm can increase the accuracy rate to more than 90%, and can recall a high percentage of correct matching pairs. The average time overhead is also around 300 ms.
  Keywords: sensor technology; genetic algorithm; computer network; reliability optimisation.
  DOI: 10.1504/IJGEI.2022.10052597
   
  
• Comparison between PID and PSO-PID controllers in analysing the load frequency control in interconnected microgrids in a deregulated environment  
  by Ranjit Singh, L. Ramesh 
  Abstract: This paper focuses on analysing the frequency error in interconnected microgrids and reducing the generation cost, which is considered one of the objective functions. The Simulink model shows the connection between 2microgrids, i.e., microgrid 1 comprises thermal, hydro and gas power plants, whereas microgrid 2 comprises thermal, nuclear and gas power plants. The change in the tie-line power is also considered while simulating the model. The papers main aim is to reduce the variations in frequency in each microgrid to ensure the steady flow of power among the connected microgrids along with the tie-line power. Also, the robustness of PID and PSO-PID Controllers are compared and analysed. The Particle swarm optimisation Algorithm codes tune the controllers gains in MATLAB. The model is simulated using MATLAB 2014b, and necessary graphs are obtained, which show the frequency error reduction time in both the microgrids.
  Keywords: MGs; microgrids; frequency error; LFC; load frequency control; tie line power; PID proportional integral derivative; controller; PSO; particle swarm optimisation.
  DOI: 10.1504/IJGEI.2023.10053492
   
  
• Design of remote data quantum system for geological oil extraction based on ARM and GPRS  
  by Hong Guo, Keping Zhai, Shien Liu, Xiuhua Shan 
  Abstract: With the rapid development of industrial automation, the control accuracy in industrial production has gradually improved and the control process has become more and more complex. Since then, the requirements for the security and real-time performance of the data collected on site have also been continuously improved, and high-quality, strong real-time data has played an important role in the entire control process. This article aims to study the design of a remote data quantum system for geological oil extraction based on ARM and GPRS, and put forward some related methods about quantum information and GPRS technology. In addition, experiments were conducted on a quantum system based on ARM and GPRS for remote data extraction of geological oil. The experimental results of this paper show that the remote data quantum system for geological oil extraction based on ARM and GPRS can detect the flow information of oil and water in the process of oil production in real-time. Moreover, it has played a great protective role in the remote transmission of data, and the security protection of data has been improved by 10%.
  Keywords: GPRS mobile communication; Modbus protocol; geological oil extraction; quantum information; quantum system design; remote data collection.
  DOI: 10.1504/IJGEI.2023.10054747
   
  
• Distributed energy system based on comprehensive utilisation of solar energy and biomass energy  
  by Dexia Kong, Zhiqiang Yao, Yihao Duan, Yulei Zhao 
  Abstract: Traditional energy is mostly non-renewable energy. The main advantage of distributed energy system is to use it in cogeneration of cooling, heating and power. Distributed energy takes into account the two factors of energy saving and environmental protection. Breaking the traditional single power supply, heating or cooling method has become the best way to adjust the energy structure, and has broad market application prospects. In order to understand the utilisation of solar energy and bio-intelligence, this paper collects a large number of scattered energy data information on site in real time, uses high-speed communication network to transmit data, relies on realtime database technology to process the measured data, and uses visual programming configuration tools to quickly, accurately and reliably obtain energy management information. The dimensional division and system configuration of energy management are classified in detail, and the design and development of system functions such as data collection and storage, data statistics and analysis, status monitoring and alarms have been completed. The results of the study found that, based on solar energy and biomass, the cooling time of internal combustion engines and gas turbines is basically the same, but the energy consumed by internal combustion engines is about 10% higher than that of gas turbines.
  Keywords: solar energy; biological intelligence; comprehensive utilisation; distributed energy system.
  DOI: 10.1504/IJGEI.2023.10054750
   
  
• Voiceprint recognition and cloud computing data network security based on scheduling joint optimisation algorithm  
  by Yinhui Ma 
  Abstract: Cloud computing is an upcoming revolution in the information technology industry due to its performance, accessibility, low cost, and many other luxury items. This is a way to maximise capacity without investing in new infrastructure, training new personnel, or licensing new software, for it provides customers with huge data storage and faster calculation speed through the Internet. With the popularity of cloud computing, users store and share confidential data in the cloud, and this approach makes data security an important and difficult issue. In order to ensure data security, cloud service providers must provide efficient and feasible mechanisms to provide reliable encryption methods and appropriate access control systems. This paper takes this as the main research content, focusing on the resource scheduling algorithm and its performance optimisation, voiceprint recognition technology and its optimisation, and the joint optimisation scheduling algorithm for the cloud data network security centre. The research proves that the performance of the voiceprint recognition and cloud computing data network system based on the genetic quantum particle optimisation joint scheduling algorithm proposed in this paper has been improved. It takes the systems network convergence speed as an index, and when the path scheme reuse rate is 30%, the network convergence speed is the fastest, and the convergence time is only 0.72 s.
  Keywords: scheduling joint optimisation algorithm; voiceprint recognition; cloud computing; data network security.
  DOI: 10.1504/IJGEI.2023.10054938
   
  
• Fusion analysis of sports data based on smart sensors and blockchain technology  
  by Yujia Wang 
  Abstract: Smart sensors are sensors with data processing functions. They are equipped with microprocessors capable of collecting, processing and exchanging data, and are the product of the integration of sensors and microprocessors. This article aims to study the impact of smart sensors and blockchain technology on sports data fusion, and introduce a hierarchical model of sports athletes corresponding to the motion capture data. A human motion coordination system and reliable construction algorithm based on reliability and probability are proposed. This paper also proposes a data integration algorithm that enables applications in blockchain technology to run at a high speed, which extends the life cycle of network sensors to a certain extent. Blockchain technology is to use block chain data structure to verify and store data, use distributed node consensus algorithm to generate and update data, use cryptography to ensure the security of data transmission and access, and use smart contracts composed of automated script codes. The experimental results of this article show that through the dynamic analysis algorithm of the smart sensor, it can be obtained that the visual range of the motion data is 22.5, and the network space value is 200. At the same time, it also shows that the use of distributed compressed sensing technology in blockchain technology can reduce the amount of information transmission in some aspects, and improve the speed and accuracy of data fusion.
  Keywords: smart sensors; blockchain technology; data fusion; sports data.
  DOI: 10.1504/IJGEI.2023.10055439
   
  
• Application of blockchain-based data pre-processing algorithm in motion analysis system  
  by Ting Wang 
  Abstract: Databases and data warehouses are very susceptible to the intrusion of noisy data. The existence of noisy data has a great impact on the speed and quality of data information mining. This article aims to study a motion analysis system based on blockchain-based data pre-processing algorithms, and proposes the application method of data pre-processing algorithms in motion analysis training. This article analyses related content such as data pre-processing algorithms, sports training, and motion analysis systems, and conducts experiments on a motion analysis system based on blockchain-based data pre-processing algorithms. The experimental results show that the sports analysis system based on the data pre-processing algorithm can analyse the various states of the athletes during the exercise, and can effectively point out some deficiencies in the training process, which is beneficial to improve the training effect of the athletes, especially it is a 7% increase in improving the athletes maximum physical state value.
  Keywords: blockchain technology; data pre-processing; sports analysis system; sports training; data noise reduction.
  DOI: 10.1504/IJGEI.2023.10055440
   
  
• Analysis and research of communication network system based on low power loss routing protocol  
  by Wenming Cai, Xin Niu 
  Abstract: In view of LLN communication network system has broad prospects for development. In this paper, the RPL routing protocol in the LLN communication network system is the main research content. Aiming at the deficiency of RPL routing protocol in high load communication network, a low power loss routing protocol based on load balancing LLN is proposed in this paper. This protocol can increase the connectivity and coverage of the communication network system, and can balance the data load of the network system effectively, improve the overall throughput of the communication network, further prolong the life cycle of the communication network system and ensure the normal communication work of the communication network system through the low power dissipation network routing protocol based on load balance LLN.
  Keywords: LLNs; load balancing; RPL; low power consumption; lossy network; routing protocol.
  DOI: 10.1504/IJGEI.2023.10055833
   
  
• A Monte Carlo simulation for electron scattering and collision for electron transport in low-temperature plasmas  
  by Rawaa A. Abdul-Nabi 
  Abstract: Owing to the lack of equilibrium between electrons and neutral species and ions and the electrons themselves, the electron velocity distribution function in partially ionised plasmas deviates substantially from a Maxwellian value. Electrons are also out of thermal equilibrium with one another and with neutral species and ions. Low-temperature non-equilibrium plasma technology is now widely used. The employment of plasma modelling considerably improves their understanding, progress, or optimisation. To successfully portray plasma characteristics as a function of external influences, electron and ion collisions and transmissions with neutral substances must be well described. We provide free MATLAB code for modelling electron transport in a uniform electric field in any gas combination. The program gives a coefficient, reaction rate, or electron energy division function for steady-state electron transmission. The program is compatible with electron cross-sectional files generated using the Monte Carlo method. The program uses well-known Monte Carlo methods and works with open-access electron scattering cross-sections. The LXCat Plasma Data Share Project aims to exchange plasma data.
  Keywords: electrons; electron scattering; Monte Carlo simulation; gas; collision; LXCat and Plasma Data Share Project.
  DOI: 10.1504/IJGEI.2023.10055834
   
  
• Computer image processing and recognition technology under the background of new energy digitisation  
  by Qinghua Feng 
  Abstract: Threshold-based segmentation methods, region-based segmentation methods, edge-based segmentation methods, and specific theory-based segmentation methods, etc. From a mathematical point of view, image segmentation is the process of dividing a digital image into mutually disjoint regions. Nowadays, the development of new energy has become an indispensable part. Therefore, it is of great significance to study computer image processing and recognition technology under the background of new energy digitisation. This paper introduces the theoretical knowledge of computer graphics, computer science and other related disciplines commonly used in computer vision algorithm, analyses some problems and defects in its practical application, and how to better solve these defects, and puts forward corresponding solutions to improve the reference value of new energy digital informatisation, provide some help for environment-friendly development. Then this paper introduces the processing methods of computer image processing and recognition technology. According to the application of the algorithm, this paper uses denoising and recognition technology to test the corresponding performance for the image blur caused by different noise. Finally, the test results show that the gray transformation can filter out the image noise and better maintain the edge definition and contour information of the image. The restoration results obtained by wavelet transform method are excellent. Denoising the observation data first and then constructing the weight matrix can get better denoising effect.
  Keywords: new energy digitisation; computer image; image processing; recognition technology.
  DOI: 10.1504/IJGEI.2023.10056062
   
  
• Field information monitoring system for micro-small quadrotor UAV based upon wireless sensor network  
  by Lei Gu, Juan Meng 
  Abstract: At present, information wireless sensor is a research hotspot. The wireless sensor network for drones provides a management method that can monitor farmland data. Farmland information collection is the research base of modern agriculture and digital agriculture. Its main content is to realise the dynamic, accurate and real-time monitoring of farmland geographical environment, soil structure, climate parameters, crop growth status and other information. Quadrotor drones are widely used in military, civilian, scientific research and education fields, and have the advantages of light weight, simple structure, low cost, and strong mobility. The influence of natural wind on quadrotor drones cannot be ignored, and it is one of the main reasons restricting the use of quadrotor drones. This paper takes quadrotor UAV as the research object, and studies a quadrotor UAV control system suitable for farmland data collection.
  Keywords: wireless sensor network; miniature quadrotor unmanned aerial vehicle; farmland information monitoring system; anti-wind disturbance.
  DOI: 10.1504/IJGEI.2023.10056389
   
  
• Smart city traffic evaluation system based on neural network model  
  by Mingyue Wang 
  Abstract: Based on the basic connotation of green transportation and reorganisation and the five-in-one theory of green transportation, the article constructs an evaluation index system for urban green transportation, proposes an evaluation model based on BP neural network, and tests it. Verify the efficiency and rationality of this method. Determine the number of network layers, transfer function, training function, hidden layer neurons, and provide a feasible evaluation program, use MATLAB Neural Network Toolbox (NNT) to design the calculation network, and use sample training for simulation testing. From the results, it can be seen that the accuracy of the urban ecological transportation BP neural network evaluation model is relatively high. The training accuracy can reach 3.4*10-3 magnitude, the output accuracy can reach 10-4 magnitude, and the error of the model is within a predetermined range. And strategic measures for the development of urban ecological transportation are proposed.
  Keywords: urban ecological transport; ecological transport evaluation; index system; neural network model.
  DOI: 10.1504/IJGEI.2023.10056390
   
  
• Automatic generation of civil engineering structure model based on network virtual reality  
  by HaiYang Yu, Changping Chen 
  Abstract: The rapid development of the data age makes it closely related to research work in various fields of life. This paper uses virtual reality technology to study the automation design of civil engineering process design. Based on the traditional AR technology framework, this article combines cloud platform technology to specialise in civil engineering firmware systems. By analysing the authenticity, flexibility, regionality and high-security requirements of the fusion of virtual reality and real-time network simulation, virtual reality technology analyses the simulation network based on the cloud platform and this paper further studies the support of multiple video stream node test sites. By providing virtual reality imaging technology and actual internet connection methods, it is found that when the transmission bandwidth is between 400 Mb/s and 800 Mb/s, the packet loss rate does not exceed 0.01%. This small packet loss has almost no effect on normal network communication.
  Keywords: network simulation; steel structure; three-dimensional network; cloud platform.
  DOI: 10.1504/IJGEI.2023.10056391
   
  

Special Issue on: Smart Energy Infrastructures for Smart Cities

• Cloud computing load balancing based on improved genetic algorithm  
  by Fengxia Zhu 
  Abstract: In the cloud computing environment, when most users request services, how to quickly and reasonably allocate a large number of tasks to a single virtual resource node and achieve parallelism is one of the research topics of current researchers. The key to this method in load balancing technology is load programming, whose quality directly affects the performance of the equalisation system. Therefore, this paper starts with distributed cloud computing technology and virtualisation technology, reveals the concept and method of load balancing implementation, and proposes an improved genetic load balancing algorithm. Traditional genetic algorithms can be used as meta-heuristic algorithms with slow convergence problems. We used the Cloudsim open source cloud simulation platform for simulation. The results show that compared with the traditional genetic algorithm, the improved genetic algorithm can better adapt to the load balancing requirements in the cloud computing environment and improve the balance and efficiency of resource utilisation.
  Keywords: improved genetic algorithm; cloud computing; load balancing; virtualisation technology.
  DOI: 10.1504/IJGEI.2023.10054590
   
  
• Secure application-centric service authentication with regression learning for security systems in smart city applications  
  by Pei Yang, Guoqiang You 
  Abstract: Smart city applications rely on different security paradigms for meeting the user demands and authenticated service disseminations. Diverse applications require different security modifications for improving the smart city contract-level application support. The challenging task is security adaptability and its improvements for smart city scenarios. In this article, a Secure Application-Centric Service Authentication (SACSA) is introduced for leveraging end-to-end authentication. This scheme introduces group key-based authentication for securing services in an end-to-end manner. The proposed scheme administers security using batch keys to improve the sharing efficiency of different services. The security and service time rely on the application type and distinct intervals, providing less complex and time-consuming security. In this process, blockchain is applied to perform the grouping, key generation and authentication recommendation in collaboration with the regression learning. Through this learning, batch consecutiveness is identified for improving application security. In the proposed scheme, authentication and key generation are performed using the Merkle Hash tree to prevent replication and decrease distribution. The proposed schemes performance is analysed using the metrics authentication time, complexity, service failure, and service latency. Thus, the SACSA system maintains system security with minimum authentication time, complexity, service failure, and latency of 9.45%, 7.75%, 9.2%, and 9.39%, respectively.
  Keywords: blockchain; group key; IoT; Merkle hash.
  DOI: 10.1504/IJGEI.2023.10055115
   
  
• Pricing mechanism and estimation model of integrated energy service products  
  by Lingyuan Ge, Yanni Tan, Jianping Ren, Rong Wei, Zhaoli Wang 
  Abstract: In the context of energy shortage, the concepts of energy revolution and supply-side reform and development require the energy sector, to further promote the energy revolution, strive to promote the transformation of energy production and development, optimise the energy supply structure, improve energy efficiency, build a clean, low-carbon, safe and efficient modern energy system, and maintain national energy security. The current research on the pricing mechanism and estimation model of integrated energy services is not thorough enough. In this paper, we introduce a consistent pricing strategy and analyse the optimal decision and profit situation of manufacturers and retailers within the framework of this pricing strategy. It is found that the differentiated pricing strategy outperforms the unified pricing strategy in most cases for manufacturers and the entire supply chain. When consumers have moderate preferences for the online channel, a manufacturers choice of a uniform pricing strategy can make it more affordable for manufacturers and suppliers, with less lost profit for the entire chain. In addition, when manufacturers have low pricing power (p < 55), they are more inclined to go for a consistent pricing strategy.
  Keywords: integrated energy services; factor analysis; approximate ideal knot analysis; pricing mechanism.
  DOI: 10.1504/IJGEI.2023.10056259
   
  
• Comprehensive energy service operation mode and benefit evaluation model  
  by Yanni Tan, Lingyuan Ge, Jianping Ren, Rong Wei, Zhaoli Wang 
  Abstract: This paper mainly studies the benefits of integrated energy services under the modes of independent investment and operation, independent investment and entrusted operation, cooperative investment and operation, and cooperative investment and entrusted operation. It also constructs a corresponding model for benefit evaluation and analysis by means of the subject feature method. This paper selects the integrated energy system of an industrial park in Southwest China as the research object. By analysing the operation modes of three different participating entities, this paper analyses their benefits by combining the cost-benefit model, and concludes that the State Grid Corporation has the greatest benefit under the independent investment and operation mode. The research results of this paper have certain practical guiding significance for the operation mode and benefit maximisation of integrated energy services.
  Keywords: integrated energy services; operation mode; cost-benefit model; subject characteristics.
  DOI: 10.1504/IJGEI.2023.10056260