Template-Type: ReDIF-Article 1.0 Author-Name: Ammar Ali Author-X-Name-First: Ammar Author-X-Name-Last: Ali Author-Name: Abinash Mahapatro Author-X-Name-First: Abinash Author-X-Name-Last: Mahapatro Author-Name: Binayak Pattanayak Author-X-Name-First: Binayak Author-X-Name-Last: Pattanayak Author-Name: Adel Abdalrahman Author-X-Name-First: Adel Author-X-Name-Last: Abdalrahman Author-Name: Fadi Ali Author-X-Name-First: Fadi Author-X-Name-Last: Ali Title: Numerical study on the effect of collector height on the performance of solar water heater collector Abstract: The current research aims to perform a numerical study on the effect of the collector height on the feasibility and effectiveness of a flat plate solar water heater (SWH) integrated with baffles. Numerical simulation is carried out using Ansys Workbench (CFD fluent), where the domain of study consists of water as fluid and metal collector (aluminium) as solid. Transverse baffles channel the water flow in the collector to ensure a proper flow distribution across the collector. Two height variations (H) viz. 3 cm and 5 cm are considered for the current investigation. The results show that reducing the height of the solar water heater leads to an increase in the speed of water flow in the collector. Nevertheless, the temperature of the water is increased by reducing the collector height due to the reduction of total volume and mass of water available in the collector. Moreover, the higher outlet temperature of the water corresponds to the lower collector height. Thus, the height of the solar water heater has a significant economic effect correlated to the performance and cost of the collector. The authors thereby emphasise that the height and the associated outlet temperature are reference parameters to evaluate the feasibility and effectiveness of the solar water collector. Journal: Int. J. of Global Energy Issues Pages: 288-300 Issue: 3 Volume: 45 Year: 2023 Keywords: CFD; solar water heater; flat plate collector; collector height; baffles. File-URL: http://www.inderscience.com/link.php?id=130658 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:3:p:288-300 Template-Type: ReDIF-Article 1.0 Author-Name: Yanpeng Li Author-X-Name-First: Yanpeng Author-X-Name-Last: Li Author-Name: Guofeng Zhang Author-X-Name-First: Guofeng Author-X-Name-Last: Zhang Title: Short-term forecasting method for lighting energy consumption of large buildings based on time series analysis Abstract: In order to overcome the problems of high data noise, low prediction accuracy and long prediction time in the traditional short-term prediction method of lighting energy consumption of large buildings, a short-term prediction method of lighting energy consumption of large buildings based on time series analysis is proposed in this paper. The improved threshold function is used to denoise the data, and the fuzzy c-means clustering algorithm is used to cluster the denoised data. The time series analysis method is used to construct the self-excitation threshold autoregressive model. When the model parameters are optimal, the clustered data are input into the model to output the short-term prediction results of lighting energy consumption of large buildings. The experimental results show that compared with the traditional method, the average data noise of this method is 12.3 dB, the prediction accuracy remains above 94% and the average prediction time is only 57 ms. Journal: Int. J. of Global Energy Issues Pages: 220-232 Issue: 3 Volume: 45 Year: 2023 Keywords: time series analysis; large buildings; lighting energy consumption; short-term forecast; fuzzy c-means clustering algorithm; self-excitation threshold autoregressive model; particle swarm optimisation algorithm. File-URL: http://www.inderscience.com/link.php?id=130673 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:3:p:220-232 Template-Type: ReDIF-Article 1.0 Author-Name: Hatem Rjiba Author-X-Name-First: Hatem Author-X-Name-Last: Rjiba Author-Name: Federica Salvadé Author-X-Name-First: Federica Author-X-Name-Last: Salvadé Author-Name: Ryan Tolliver Author-X-Name-First: Ryan Author-X-Name-Last: Tolliver Author-Name: Juan Ignacio Torriconi Author-X-Name-First: Juan Ignacio Author-X-Name-Last: Torriconi Title: The Paris Agreement's impact on the green bonds market Abstract: Green bonds are a relatively new instrument in financial markets, and are considered one of the best tools available to finance green projects and help reduce greenhouse gas emissions. This paper investigates the effects of the Paris Climate Agreement on the green bond market. We focus on US municipal, corporate and government green bonds. We document that the number of such bonds issued have significantly increased immediately after the agreement was adopted, suggesting that the adoption of the Paris Agreement has played a significant role in the development of the green bond market. Journal: Int. J. of Global Energy Issues Pages: 301-314 Issue: 3 Volume: 45 Year: 2023 Keywords: Paris Agreement; green bonds; green house gas emissions. File-URL: http://www.inderscience.com/link.php?id=130675 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:3:p:301-314 Template-Type: ReDIF-Article 1.0 Author-Name: Lei Zhang Author-X-Name-First: Lei Author-X-Name-Last: Zhang Title: An energy market demand prediction based on grey BP-NN optimal combination Abstract: Aiming at the problems of low-prediction accuracy and long prediction time in traditional energy market demand prediction methods, an energy market demand prediction method based on grey BP-NN optimisation combination is proposed. The influencing factors of energy market demand are analysed through economic growth factors, energy price factors, industrial structure factors, population and urbanisation factors and environmental policy factors, the analysis sequence is determined, the analysis matrix is constructed, the grey correlation degree of each influencing factor is calculated and the mean value is standardised. According to the processing results, the energy market demand prediction model of grey theory is constructed. Taking the prediction results of grey model and factors affecting energy demand as the input of BP neural network, an improved BP neural network structure is constructed and the prediction results are output. The simulation results show that the proposed method has high accuracy and short prediction time. Journal: Int. J. of Global Energy Issues Pages: 233-246 Issue: 3 Volume: 45 Year: 2023 Keywords: BP neural network; grey theory; energy market; demand prediction; grey correlation degree; mean normalisation. File-URL: http://www.inderscience.com/link.php?id=130678 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:3:p:233-246 Template-Type: ReDIF-Article 1.0 Author-Name: Hui Cao Author-X-Name-First: Hui Author-X-Name-Last: Cao Title: Load random access method of intelligent charging pile based on distributed energy Abstract: In order to improve the stability and convergence of random-access process of charging pile load, a load random access method of intelligent charging pile based on distributed energy is designed in this paper. Firstly, the distributed energy dispatching control process is designed, then the load balancing scheduling algorithm is designed according to the charging pile demand, and the random load access is realised by planning the charging pile load boundary. Experimental results show that the load rate of each line in the power grid with charging piles is relatively balanced, and the power load output value can be stable at 36.5 kW after the application of the proposed method. Moreover, the loss function value of the proposed method gradually tends to be stable after the 100th iteration, and its value is 0.03, indicating that the proposed method has a faster convergence rate and better convergence. Journal: Int. J. of Global Energy Issues Pages: 247-260 Issue: 3 Volume: 45 Year: 2023 Keywords: charging pile; distributed energy; load balancing dispatching; load random access. File-URL: http://www.inderscience.com/link.php?id=130679 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:3:p:247-260 Template-Type: ReDIF-Article 1.0 Author-Name: Hui-Hua Xiong Author-X-Name-First: Hui-Hua Author-X-Name-Last: Xiong Author-Name: Ming Luo Author-X-Name-First: Ming Author-X-Name-Last: Luo Title: An emission reduction prediction method of green building engineering based on time weighting Abstract: Aiming at the problems of low accuracy of emission reduction prediction and long time-consuming emission reduction prediction methods of existing green building engineering emission reduction prediction methods, the paper proposes a new time-weighted emission reduction prediction method for green building engineering. First, construct the objective function of the time change of green building emission reduction, and use time weighting to calculate the weight of green building engineering emission reduction forecast. Secondly, the grey model is used to obtain the fitted sequence of emission reductions of green building projects. Finally, the Markov Chain is used to construct the emission reduction prediction function, and the output result of the function is the prediction result. The results of the simulation study show that the prediction accuracy of emission reductions of the method in this paper is maintained above 95%, and the time cost is effectively reduced. Journal: Int. J. of Global Energy Issues Pages: 261-272 Issue: 3 Volume: 45 Year: 2023 Keywords: time weighting; green building engineering; emission reduction prediction. File-URL: http://www.inderscience.com/link.php?id=130680 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:3:p:261-272 Template-Type: ReDIF-Article 1.0 Author-Name: Huanan Liang Author-X-Name-First: Huanan Author-X-Name-Last: Liang Author-Name: Zhibin Xu Author-X-Name-First: Zhibin Author-X-Name-Last: Xu Title: Research on emission reduction potential prediction method under green building planning based on multi-factor analysis Abstract: In order to overcome the problem of large prediction error of traditional methods, a prediction method of green building emission reduction potential based on multi-factor analysis is proposed. The capacity forecast is divided into four stages: building materials transportation, construction, operation and maintenance, and demolition and recycling. The energy consumption of construction equipment is calculated by the rated estimation method, the carbon emission in the operation, maintenance and demolition and recycling stage is calculated and the weight of emission reduction potential index is calculated by the deviation maximisation method. Build a carbon emission prediction model and substitute the above results into the model to realise the multi-factor analysis of emission reduction potential prediction. The experimental results show that the prediction error of emission reduction potential of this method is only 1.032%, which shows that the prediction effect of emission reduction potential of this method is good. Journal: Int. J. of Global Energy Issues Pages: 273-287 Issue: 3 Volume: 45 Year: 2023 Keywords: green building planning; emission reduction potential; prediction; multiple factors; weight; correlation. File-URL: http://www.inderscience.com/link.php?id=130681 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:3:p:273-287 Template-Type: ReDIF-Article 1.0 Author-Name: Yongbing Guan Author-X-Name-First: Yongbing Author-X-Name-Last: Guan Author-Name: Yebo Fang Author-X-Name-First: Yebo Author-X-Name-Last: Fang Title: An energy consumption prediction of large public buildings based on data-driven model Abstract: Because the traditional energy consumption prediction method of large public buildings has the problems of large prediction error and long prediction time, an energy consumption prediction of large public buildings based on data-driven model is proposed. The process includes to build the energy consumption model of large public buildings through data driving, collect energy consumption data such as equipment capacity, load grade and equipment failure rate, pre-process the energy consumption data, take the pre-processed energy consumption data as training samples, input it into BP neural network for training, optimise BP neural network by genetic algorithm and build the energy consumption prediction model of large public buildings, and get the prediction results. The simulation results show that the energy consumption prediction method of large public buildings based on data-driven model has short time and good prediction effect. Journal: Int. J. of Global Energy Issues Pages: 207-219 Issue: 3 Volume: 45 Year: 2023 Keywords: data-driven model; large public buildings; energy consumption prediction; BP neural network; genetic algorithm. File-URL: http://www.inderscience.com/link.php?id=130682 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:3:p:207-219 Template-Type: ReDIF-Article 1.0 Author-Name: Chunjiao Gao Author-X-Name-First: Chunjiao Author-X-Name-Last: Gao Title: Crude oil futures tail risk measurement based on extreme value theory Abstract: In this paper, we use three common tail risk measurements of Value-at-Risk (VaR), Expected Shortfall (ES) and Spectral Risk Measure (SRM) to calculate the tail risk of crude oil futures based on extreme value theory. Specifically, we propose a method to determine the optimal threshold in the extreme value theory, and further to calculate the values of VaR, ES and SRM based on Peak Over Threshold (POT) model. Empirical results show that the extreme value POT model can be used to characterise the tail risk of the price return under extreme fluctuations in Brent crude oil futures market. Moreover, the risk of VaR, ES and SRM in the Brent crude oil futures market based on extreme value theory is higher than that under the normal distribution assumption, which indicates that the traditional normal distribution assumption underestimates the tail risk. Owing the flexibility and the accuracy, we suggest that investors use ERM to measure the extreme risk of crude oil futures. Journal: Int. J. of Global Energy Issues Pages: 53-65 Issue: 1 Volume: 45 Year: 2023 Keywords: spectral risk measurement; hyperbolic risk spectral function; extreme value theory; tail risk. File-URL: http://www.inderscience.com/link.php?id=127631 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:1:p:53-65 Template-Type: ReDIF-Article 1.0 Author-Name: Fatma Khalifa Author-X-Name-First: Fatma Author-X-Name-Last: Khalifa Author-Name: Abderrazak Dhaoui Author-X-Name-First: Abderrazak Author-X-Name-Last: Dhaoui Author-Name: Mohamed Sahbi Nakhli Author-X-Name-First: Mohamed Sahbi Author-X-Name-Last: Nakhli Author-Name: Saad Bourouis Author-X-Name-First: Saad Author-X-Name-Last: Bourouis Author-Name: Saloua Benammou Author-X-Name-First: Saloua Author-X-Name-Last: Benammou Title: Do oil prices predict the dynamics of equity market? Fresh evidence from DCC, ADCC and Go-GARCH models Abstract: This paper investigates the dynamic condition correlations between oil price, industrial production, short-term interest rates and equity market in South Korea using three types of GARCH models. The results from the DCC and ADCC GARCH models show strong evidence of significant dynamic conditional correlations suggesting higher long-term persistence of volatility than short-term persistence. The findings suggest, particularly, that oil prices have positive dynamic conditional correlations to equity markets, while the dynamic conditional correlations between equity market and short-term interest rates are significantly negative. These results have considerable economic implications. Firstly, oil price as a risk factor increases the equity market volatility. It also represents an implicit risk factor that cannot be diversified and which requires therefore to be hedged or priced. Secondly, the oil acts as an inflationary factor leading central banks to adjust their short-term interest rates in order to smooth the inflationary effect on both real economy and financial activity. Journal: Int. J. of Global Energy Issues Pages: 66-85 Issue: 1 Volume: 45 Year: 2023 Keywords: oil price; equity market; industrial production; short-term interest rates; dynamic conditional correlations. File-URL: http://www.inderscience.com/link.php?id=127640 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:1:p:66-85 Template-Type: ReDIF-Article 1.0 Author-Name: Wenbao Lv Author-X-Name-First: Wenbao Author-X-Name-Last: Lv Author-Name: Chuanzhen Wang Author-X-Name-First: Chuanzhen Author-X-Name-Last: Wang Title: Intelligent control of heavy media separation Abstract: The shortcomings of the density control system for heavy media suspension in the current coal preparation plant were introduced. The intelligent control system of heavy media separation suitable for different coal quality characteristics in LinHuan Coal Preparation Plant was proposed. The system was mainly composed of the following parts: relationship between ash and mineral content, automatic identification of raw coal production source information, automatic adjustment of online ash analyser parameters, closed loop control system based on clean coal ash content. After intelligent control of heavy media separation was applied. The processed raw coal production source information and ratio could be automatically identified by the system, and the parameters of the online ash meter could be automatically adjusted. The clean coal ash content after heavy media separating was relatively stable, and the product qualification rate was improved. Journal: Int. J. of Global Energy Issues Pages: 86-100 Issue: 1 Volume: 45 Year: 2023 Keywords: raw coal; clean coal; separation; heavy media; ash content. File-URL: http://www.inderscience.com/link.php?id=127645 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:1:p:86-100 Template-Type: ReDIF-Article 1.0 Author-Name: Naser M. Rostamnia Author-X-Name-First: Naser M. Author-X-Name-Last: Rostamnia Title: An analysis of market power in Iran's electricity market with machine learning 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. Journal: Int. J. of Global Energy Issues Pages: 489-502 Issue: 4/5 Volume: 45 Year: 2023 Keywords: market power analysis; Herfindahl Hirschman index; long short-term memory algorithm; hyperparameter optimisation; grid search. File-URL: http://www.inderscience.com/link.php?id=132009 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:4/5:p:489-502 Template-Type: ReDIF-Article 1.0 Author-Name: Dexia Kong Author-X-Name-First: Dexia Author-X-Name-Last: Kong Author-Name: Zhiqiang Yao Author-X-Name-First: Zhiqiang Author-X-Name-Last: Yao Author-Name: Yihao Duan Author-X-Name-First: Yihao Author-X-Name-Last: Duan Author-Name: Yulei Zhao Author-X-Name-First: Yulei Author-X-Name-Last: Zhao Title: Distributed energy system based on comprehensive utilisation of solar energy and biomass energy 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 real-time 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. Journal: Int. J. of Global Energy Issues Pages: 542-560 Issue: 6 Volume: 45 Year: 2023 Keywords: solar energy; biological intelligence; comprehensive utilisation; distributed energy system. File-URL: http://www.inderscience.com/link.php?id=133802 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:6:p:542-560 Template-Type: ReDIF-Article 1.0 Author-Name: Ali Tarraq Author-X-Name-First: Ali Author-X-Name-Last: Tarraq Author-Name: Faissal El Mariami Author-X-Name-First: Faissal El Author-X-Name-Last: Mariami Author-Name: Abdelaziz Belfqih Author-X-Name-First: Abdelaziz Author-X-Name-Last: Belfqih Author-Name: Touria Haidi Author-X-Name-First: Touria Author-X-Name-Last: Haidi Title: Optimal renewable distributed generation planning: an up-to-date state-of-the-art review 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. Journal: Int. J. of Global Energy Issues Pages: 315-348 Issue: 4/5 Volume: 45 Year: 2023 Keywords: renewable distributed generation; optimal renewable DG planning; distribution network; optimisation methods. File-URL: http://www.inderscience.com/link.php?id=132011 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:4/5:p:315-348 Template-Type: ReDIF-Article 1.0 Author-Name: Yinhui Ma Author-X-Name-First: Yinhui Author-X-Name-Last: Ma Title: Voiceprint recognition and cloud computing data network security based on scheduling joint optimisation algorithm 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 system's 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. Journal: Int. J. of Global Energy Issues Pages: 602-626 Issue: 6 Volume: 45 Year: 2023 Keywords: scheduling joint optimisation algorithm; voiceprint recognition; cloud computing; data network security. File-URL: http://www.inderscience.com/link.php?id=133803 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:6:p:602-626 Template-Type: ReDIF-Article 1.0 Author-Name: Jinhua Kang Author-X-Name-First: Jinhua Author-X-Name-Last: Kang Author-Name: Guanglei Zhao Author-X-Name-First: Guanglei Author-X-Name-Last: Zhao Title: A low carbon treatment technology of green building construction waste based on genetic algorithm 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. Journal: Int. J. of Global Energy Issues Pages: 408-420 Issue: 4/5 Volume: 45 Year: 2023 Keywords: genetic algorithm; emission factor method; green building; construction waste; low-carbon treatment. File-URL: http://www.inderscience.com/link.php?id=132012 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:4/5:p:408-420 Template-Type: ReDIF-Article 1.0 Author-Name: Yujia Wang Author-X-Name-First: Yujia Author-X-Name-Last: Wang Title: Fusion analysis of sports data based on smart sensors and blockchain technology 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 introduces 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 blockchain 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. Journal: Int. J. of Global Energy Issues Pages: 524-541 Issue: 6 Volume: 45 Year: 2023 Keywords: smart sensors; blockchain technology; data fusion; sports data. File-URL: http://www.inderscience.com/link.php?id=133804 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:6:p:524-541 Template-Type: ReDIF-Article 1.0 Author-Name: Yingwei Chen Author-X-Name-First: Yingwei Author-X-Name-Last: Chen Author-Name: Zhikui Chang Author-X-Name-First: Zhikui Author-X-Name-Last: Chang Title: Intelligent forecasting method of distributed energy load based on least squares support vector machine 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. Journal: Int. J. of Global Energy Issues Pages: 383-394 Issue: 4/5 Volume: 45 Year: 2023 Keywords: least squares support vector machine; linear interpolation; t-test criterion; distributed energy; load forecasting. File-URL: http://www.inderscience.com/link.php?id=132013 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:4/5:p:383-394 Template-Type: ReDIF-Article 1.0 Author-Name: Ting Wang Author-X-Name-First: Ting Author-X-Name-Last: Wang Title: Application of blockchain-based data pre-processing algorithm in motion analysis system 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 athlete's maximum physical state value. Journal: Int. J. of Global Energy Issues Pages: 503-523 Issue: 6 Volume: 45 Year: 2023 Keywords: blockchain technology; data pre-processing; sports analysis system; sports training; data noise reduction. File-URL: http://www.inderscience.com/link.php?id=133805 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:6:p:503-523 Template-Type: ReDIF-Article 1.0 Author-Name: Wei Yuan Author-X-Name-First: Wei Author-X-Name-Last: Yuan Author-Name: Zhigang Liu Author-X-Name-First: Zhigang Author-X-Name-Last: Liu Title: Study on evaluation method of energy-saving potential of green buildings based on entropy weight method 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. Journal: Int. J. of Global Energy Issues Pages: 448-460 Issue: 4/5 Volume: 45 Year: 2023 Keywords: entropy weight method; economic performance; judgment matrix; G value. File-URL: http://www.inderscience.com/link.php?id=132014 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:4/5:p:448-460 Template-Type: ReDIF-Article 1.0 Author-Name: Rawaa A. Abdul-Nabi Author-X-Name-First: Rawaa A. Author-X-Name-Last: Abdul-Nabi Title: A Monte Carlo simulation for electron scattering and collision for electron transport in low-temperature plasmas 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. Journal: Int. J. of Global Energy Issues Pages: 586-601 Issue: 6 Volume: 45 Year: 2023 Keywords: electrons; electron scattering; Monte Carlo simulation; gas; collision; LXCat; Plasma Data Share Project. File-URL: http://www.inderscience.com/link.php?id=133806 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:6:p:586-601 Template-Type: ReDIF-Article 1.0 Author-Name: Min Wang Author-X-Name-First: Min Author-X-Name-Last: Wang Title: Energy efficiency evaluation method of refrigeration and air conditioning in intelligent buildings based on improved entropy value method Abstract: In order to improve the accuracy and precision of the evaluation of cold source energy efficiency, this paper proposes an evaluation method of cold source energy efficiency of refrigeration and air conditioning in intelligent buildings based on the improved entropy method. On the basis of analysing the energy efficiency ratio composition of refrigeration and air conditioning system, the calculation process of energy efficiency ratio is refined by using refrigeration energy efficiency transport function. Then the AHM improved entropy method was used to optimise the weight ratio process, and the efficient and accurate evaluation of the cooling source energy efficiency of refrigeration and air conditioning was completed by adjusting the weight ratio. The experimental results show that the average evaluation accuracy of the proposed method is 95.9% and the average evaluation accuracy is 96.3%, which proves that the proposed method achieves the design expectation. Journal: Int. J. of Global Energy Issues Pages: 42-52 Issue: 1 Volume: 45 Year: 2023 Keywords: improved entropy method; operational energy efficiency ratio; intelligent building; refrigeration and air conditioning; evaluation of energy efficiency of cold source. File-URL: http://www.inderscience.com/link.php?id=127662 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:1:p:42-52 Template-Type: ReDIF-Article 1.0 Author-Name: Shuai Wang Author-X-Name-First: Shuai Author-X-Name-Last: Wang Author-Name: Lei Sun Author-X-Name-First: Lei Author-X-Name-Last: Sun Author-Name: Xinjie Yuan Author-X-Name-First: Xinjie Author-X-Name-Last: Yuan Title: An energy efficiency evaluation method of intelligent building based on fuzzy clustering algorithm 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. Journal: Int. J. of Global Energy Issues Pages: 421-435 Issue: 4/5 Volume: 45 Year: 2023 Keywords: fuzzy clustering algorithm; grey correlation theory; smart building; energy-saving evaluation; evaluation index system. File-URL: http://www.inderscience.com/link.php?id=132015 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:4/5:p:421-435 Template-Type: ReDIF-Article 1.0 Author-Name: Mingyue Wang Author-X-Name-First: Mingyue Author-X-Name-Last: Wang Title: Smart city traffic evaluation system based on neural network model 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. The article verifies the efficiency and rationality of this method, determines the number of network layers, transfer function, training function, hidden layer neurons, and provides a feasible evaluation program, uses MATLAB Neural Network Toolbox (NNT) to design the calculation network, and uses 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<SUP align=right><SMALL>&minus;3</SMALL></SUP> magnitude, the output accuracy can reach 10<SUP align=right><SMALL>&minus;4</SMALL></SUP> magnitude, and the error of the model is within a predetermined range. The strategic measures for the development of urban ecological transportation are proposed. Journal: Int. J. of Global Energy Issues Pages: 561-585 Issue: 6 Volume: 45 Year: 2023 Keywords: urban ecological transport; ecological transport evaluation; index system; neural network model. File-URL: http://www.inderscience.com/link.php?id=133807 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:6:p:561-585 Template-Type: ReDIF-Article 1.0 Author-Name: Xiaojuan Han Author-X-Name-First: Xiaojuan Author-X-Name-Last: Han Author-Name: Hui Chen Author-X-Name-First: Hui Author-X-Name-Last: Chen Title: The green building benefit grading evaluation based on improved FPA algorithm Abstract: In order to solve the problem of low recall rate and accuracy of traditional methods, a green building benefit grading and evaluation method based on improved FPA algorithm was proposed. Firstly, the index system of green building benefit grading and evaluation is constructed, and the economic incremental benefit, social incremental benefit and environmental incremental benefit of green building are calculated according to the index system. Then, based on the incremental benefit calculation results, the green building benefit grading evaluation function is constructed. Finally, the improved FPA algorithm is used to optimise the objective function, so as to obtain the optimal solution and complete the green building benefit grading evaluation. The experimental results show that the evaluation results of economic, social and environmental benefits of the proposed method are consistent with the actual situation. The highest recall rate is 95%, and the average accuracy is 93%. Journal: Int. J. of Global Energy Issues Pages: 26-41 Issue: 1 Volume: 45 Year: 2023 Keywords: improved FPA algorithm; economic benefit; social benefit; environmental benefit; evaluation index system; objective function; benefit evaluation. File-URL: http://www.inderscience.com/link.php?id=127663 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:1:p:26-41 Template-Type: ReDIF-Article 1.0 Author-Name: Chunxia Liu Author-X-Name-First: Chunxia Author-X-Name-Last: Liu Title: Investment risk prediction method of renewable energy market under the background of carbon neutralisation 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. Journal: Int. J. of Global Energy Issues Pages: 395-407 Issue: 4/5 Volume: 45 Year: 2023 Keywords: carbon neutralisation; system dynamics method; renewable energy; market investment; risk prediction. File-URL: http://www.inderscience.com/link.php?id=132016 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:4/5:p:395-407 Template-Type: ReDIF-Article 1.0 Author-Name: Peng Fang Author-X-Name-First: Peng Author-X-Name-Last: Fang Title: Short-term carbon emission prediction method of green building based on IPAT model Abstract: In order to solve the problems of high complexity and low prediction accuracy of green building carbon emission prediction process, this paper proposes a green building short-term carbon emission prediction method based on IPAT model. The IPCC method is used to determine the influencing factors of carbon emission of green buildings, and the classification of influencing factors is completed according to the importance of influencing factors. The IPAT model is established to decompose the carbon emission into the products of different factors, and the model is used to predict the short-term carbon emission of green building construction stage and the whole stage. The experimental results show that the prediction time of this method is always less than 4 s and the prediction accuracy is always higher than 95%, which proves that this method has fast prediction process and high accuracy, and realises the design expectation. Journal: Int. J. of Global Energy Issues Pages: 1-13 Issue: 1 Volume: 45 Year: 2023 Keywords: IPAT model; green building; short-term carbon emissions; emission factor; mutual information; impact factors. File-URL: http://www.inderscience.com/link.php?id=127664 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:1:p:1-13 Template-Type: ReDIF-Article 1.0 Author-Name: Lede Niu Author-X-Name-First: Lede Author-X-Name-Last: Niu Author-Name: Jingzhi Lin Author-X-Name-First: Jingzhi Author-X-Name-Last: Lin Author-Name: Lifang Zhou Author-X-Name-First: Lifang Author-X-Name-Last: Zhou Author-Name: Anlin Li Author-X-Name-First: Anlin Author-X-Name-Last: Li Author-Name: Yan Zhou Author-X-Name-First: Yan Author-X-Name-Last: Zhou Title: The prediction of carbon emissions from construction land in central Yunnan urban agglomeration area based on multiple linear regression model 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. Journal: Int. J. of Global Energy Issues Pages: 349-365 Issue: 4/5 Volume: 45 Year: 2023 Keywords: multiple linear regression model; STIRPAT model; central Yunnan urban agglomeration area; construction land; carbon emission prediction. File-URL: http://www.inderscience.com/link.php?id=132017 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:4/5:p:349-365 Template-Type: ReDIF-Article 1.0 Author-Name: Yuanhua Li Author-X-Name-First: Yuanhua Author-X-Name-Last: Li Title: Study on load estimation method of HVAC system in large public gymnasium Abstract: This paper proposes a load estimation method for HVAC system of large public gymnasium. The operation data of HVAC system in large public gymnasium are collected and processed by principal component analysis. According to the data processed, this paper analyses the influencing factors of HVAC system, constructs the linear regression estimation model of system load, and optimises the objective function of load estimation by using genetic algorithm to obtain the optimal solution. Experiments show that the method has strong robustness good adaptability to the model parameters, high fitting degree between the load estimation results and the actual values, and the maximum running time is less than 12.8 s. It can complete the load estimation efficiently and has good performance in practical application. Journal: Int. J. of Global Energy Issues Pages: 14-25 Issue: 1 Volume: 45 Year: 2023 Keywords: HVAC system; load; linear regression model; model parameters; estimate. File-URL: http://www.inderscience.com/link.php?id=127665 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:1:p:14-25 Template-Type: ReDIF-Article 1.0 Author-Name: Lianguang Mo Author-X-Name-First: Lianguang Author-X-Name-Last: Mo Title: New energy industry investment risk assessment method based on fuzzy AHP 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. Journal: Int. J. of Global Energy Issues Pages: 436-447 Issue: 4/5 Volume: 45 Year: 2023 Keywords: fuzzy AHP; risk assessment; random forest algorithm; cart algorithm; multiple regression analysis. File-URL: http://www.inderscience.com/link.php?id=132018 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:4/5:p:436-447 Template-Type: ReDIF-Article 1.0 Author-Name: Qiliang Yuan Author-X-Name-First: Qiliang Author-X-Name-Last: Yuan Title: Study on multi-step prediction method of passive energy-saving building energy consumption based on energy consumption perception 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. Journal: Int. J. of Global Energy Issues Pages: 366-382 Issue: 4/5 Volume: 45 Year: 2023 Keywords: energy consumption perception; passive energy-saving building; multi-step prediction of energy consumption; dynamic calculation; load equivalent thermal parameters. File-URL: http://www.inderscience.com/link.php?id=132019 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:4/5:p:366-382 Template-Type: ReDIF-Article 1.0 Author-Name: Intissar Darwich Author-X-Name-First: Intissar Author-X-Name-Last: Darwich Author-Name: Islem Lachhab Author-X-Name-First: Islem Author-X-Name-Last: Lachhab Author-Name: Lotfi Krichen Author-X-Name-First: Lotfi Author-X-Name-Last: Krichen Title: Minimisation of fuel cell electric vehicle cost using Cauchy particles swarm optimisation 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. Journal: Int. J. of Global Energy Issues Pages: 474-488 Issue: 4/5 Volume: 45 Year: 2023 Keywords: FCHEV; PSO optimisation; Cauchy operator; hydrogen consumption. File-URL: http://www.inderscience.com/link.php?id=132020 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:4/5:p:474-488 Template-Type: ReDIF-Article 1.0 Author-Name: Yao Li Author-X-Name-First: Yao Author-X-Name-Last: Li Author-Name: Hui Liu Author-X-Name-First: Hui Author-X-Name-Last: Liu Author-Name: Wei Xia Author-X-Name-First: Wei Author-X-Name-Last: Xia Author-Name: Yanwu Ruan Author-X-Name-First: Yanwu Author-X-Name-Last: Ruan Author-Name: Xiaochuan Guo Author-X-Name-First: Xiaochuan Author-X-Name-Last: Guo Title: Layered energy balance control method for renewable energy grid based on island mode 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. Journal: Int. J. of Global Energy Issues Pages: 461-473 Issue: 4/5 Volume: 45 Year: 2023 Keywords: island mode; renewable energy grid; energy control; layered control; voltage load; energy efficiency. File-URL: http://www.inderscience.com/link.php?id=132021 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:4/5:p:461-473 Template-Type: ReDIF-Article 1.0 Author-Name: Jun Zhou Author-X-Name-First: Jun Author-X-Name-Last: Zhou Author-Name: Daixin Zhang Author-X-Name-First: Daixin Author-X-Name-Last: Zhang Author-Name: Guangchuan Liang Author-X-Name-First: Guangchuan Author-X-Name-Last: Liang Author-Name: Guancheng Wu Author-X-Name-First: Guancheng Author-X-Name-Last: Wu Author-Name: Nengjia He Author-X-Name-First: Nengjia Author-X-Name-Last: He Title: Optimisation of natural gas supply chain considering pipeline transportation cost reformation in China Abstract: Natural gas plays an important role in the transition to renewable energy. The China national pipeline network company was established in 2019 and the Natural Gas Supply Chain (NGSC) is now in the transition stage. In addition, the reform of the Pipeline Transportation Cost Mode (PTCM) would have a greater impact on the natural gas market and benefit the marketers. This paper proposed an NGSC linear programming model to maximise the marketers' profit under two PTCM during the supply period. The CPLEX solver is used to find out the optimal gas distribution. Then, a long-distance pipeline in China is selected to verify the accuracy of this model. The price of different gas types, the gas volume of different market users, as well as the marketer's gas distribution are studied. And finally comes the sensitivity analysis of peak shaving gas price and extra gas price and greenhouse gas emission costs. The results show that the reform of the PTCM has a great impact on the profit of the marketers and the lucrative boundary is achieved. By fully understanding the profit map, thereby guiding the management of the marketer. Journal: Int. J. of Global Energy Issues Pages: 182-206 Issue: 2 Volume: 45 Year: 2023 Keywords: natural gas supply chain; natural gas marketer; pipeline transportation cost; optimisation. File-URL: http://www.inderscience.com/link.php?id=129493 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:2:p:182-206 Template-Type: ReDIF-Article 1.0 Author-Name: Dongwei Shi Author-X-Name-First: Dongwei Author-X-Name-Last: Shi Title: Hedging behaviour in China's crude oil futures market Abstract: Hedging behaviour of hedgers in China's crude oil futures market has naturally become a hot topic in academia and industry. This paper examines the behaviour of hedgers in China's crude oil futures market from the perspective of risk premium. The topic selection of this paper is helpful to reflect the real behaviour pattern of China's crude oil futures hedging, and also provides a more reasonable trading strategy for real traders and a practical basis for exchanges to regulate the market. The results facilitate reasonable trading strategies for hedgers and practical basis for the regulator in China's crude oil futures market. Journal: Int. J. of Global Energy Issues Pages: 166-181 Issue: 2 Volume: 45 Year: 2023 Keywords: China's crude oil futures market; risk premium; selective hedging; classic hedging. File-URL: http://www.inderscience.com/link.php?id=129498 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:2:p:166-181 Template-Type: ReDIF-Article 1.0 Author-Name: Huaxi Chen Author-X-Name-First: Huaxi Author-X-Name-Last: Chen Title: Prediction method of energy consumption in industrial production based on improved grey model Abstract: In order to reduce the prediction error of energy consumption, a method of energy consumption in industrial production based on improved grey model is proposed. After collecting the energy consumption data, the cluster analysis and interpolation method are used to realise the abnormal value processing and vacancy data processing of the energy consumption data. On this basis, the grey model is constructed, in which the state parameters of energy consumption are introduced, and the influence of production factor fluctuation on energy consumption is considered to realise the accurate prediction. The test results show that the relative error of the design method is less than 0.12% for the total electric energy consumption prediction results, less than 0.80% for the total steam energy consumption prediction results, and less than 0.85% for the total coal energy consumption prediction results. Journal: Int. J. of Global Energy Issues Pages: 101-112 Issue: 2 Volume: 45 Year: 2023 Keywords: improved grey model; energy consumption; cluster analysis; interpolation method; energy consumption status parameters; fluctuation of production factors. File-URL: http://www.inderscience.com/link.php?id=129503 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:2:p:101-112 Template-Type: ReDIF-Article 1.0 Author-Name: Hong Li Author-X-Name-First: Hong Author-X-Name-Last: Li Author-Name: Chunyu Zhang Author-X-Name-First: Chunyu Author-X-Name-Last: Zhang Title: Prediction of energy conservation and emission reduction potential of new energy vehicle industry based on grey model Abstract: In order to overcome the problems of low accuracy and long time-consuming of traditional methods, a prediction method of energy conservation and emission reduction potential of new energy vehicle industry based on grey model is proposed. Determine the carbon emission of energy consumption of new energy vehicle industry and calculate the energy efficiency value of new energy vehicle industry. According to the calculation results of energy efficiency value, the grey correlation analysis method is used to determine the correlation degree between the prediction factors of energy conservation and emission reduction potential of automobile industry, and the correlation degree coefficient is introduced into the grey model for energy conservation and emission reduction potential prediction to realise the prediction of energy conservation and emission reduction potential. Experimental results show that the prediction accuracy of this method is up to 99.19%, and the maximum prediction time is 0.7 s and the minimum is 0.4 s. Journal: Int. J. of Global Energy Issues Pages: 125-137 Issue: 2 Volume: 45 Year: 2023 Keywords: grey model; decoupling concept; grey correlation analysis; correction coefficient; conserve energy; reduce emissions. File-URL: http://www.inderscience.com/link.php?id=129505 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:2:p:125-137 Template-Type: ReDIF-Article 1.0 Author-Name: Lingxiao Zheng Author-X-Name-First: Lingxiao Author-X-Name-Last: Zheng Title: Carbon emission measurement method of heavy industry based on LMDI decomposition method Abstract: Aiming at the problems of poor accuracy and low efficiency of traditional measurement methods, this paper designed a measurement method of carbon emissions of heavy industry enterprises based on LMDI decomposition method. Firstly, the carbon emission factors are collected based on the emission coefficient method, and the carbon content in the production process of heavy industry enterprises is detected. Then, using LMDI decomposition method, the operation process of heavy industry enterprises is divided into production, transportation and storage links, and the carbon emissions generated by the above three links are combined with the carbon emission coefficient of the energy chain in the coal combustion process to complete the calculation of carbon emissions. Experimental results show that the measurement accuracy of this method can reach up to 96.8%, and the maximum monitoring amount of carbon emissions per unit time is 24.3 kg, indicating that it improves the measurement accuracy and efficiency. Journal: Int. J. of Global Energy Issues Pages: 113-124 Issue: 2 Volume: 45 Year: 2023 Keywords: heavy industry enterprises; coal combustion; carbon emissions; to measure; LMDI decomposition method. File-URL: http://www.inderscience.com/link.php?id=129506 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:2:p:113-124 Template-Type: ReDIF-Article 1.0 Author-Name: Hui-Fang Zhang Author-X-Name-First: Hui-Fang Author-X-Name-Last: Zhang Author-Name: Yun-Xia Yang Author-X-Name-First: Yun-Xia Author-X-Name-Last: Yang Title: Industrial coal utilisation efficiency prediction based on Markov Chain Model Abstract: In order to solve the problems of high error interval band width, low-prediction accuracy and long prediction time in traditional methods, an industrial coal utilisation efficiency prediction method based on Markov Chain Model is proposed. Based on the combination of probability matrix and Markov Chain, the prediction model of industrial coal utilisation efficiency is constructed. The grey GM[1,1] method was used to optimise, adjust and modify the model, and the relevant data of industrial coal utilisation were input into the model, and the prediction results of industrial coal utilisation efficiency were obtained. Experimental results show that the error interval band width value of this method is 0.07, and the prediction accuracy of industrial coal utilisation efficiency is up to 95%. Only 4 s can predict the coal utilisation efficiency of 30 different regions, indicating that this method has high-prediction accuracy and good application effect. Journal: Int. J. of Global Energy Issues Pages: 138-152 Issue: 2 Volume: 45 Year: 2023 Keywords: Markov chain model; industrial coal; utilisation efficiency; prediction model design. File-URL: http://www.inderscience.com/link.php?id=129507 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:2:p:138-152 Template-Type: ReDIF-Article 1.0 Author-Name: Weina Li Author-X-Name-First: Weina Author-X-Name-Last: Li Author-Name: Zhinan Cui Author-X-Name-First: Zhinan Author-X-Name-Last: Cui Author-Name: Xiaochun Tang Author-X-Name-First: Xiaochun Author-X-Name-Last: Tang Title: Risk evaluation method of renewable energy investment based on fuzzy analytic hierarchy process Abstract: Aiming at the problems of large error, long evaluation time and incomplete evaluation results of traditional methods, a risk evaluation method of renewable energy investment based on fuzzy analytic hierarchy process is proposed. Firstly, based on the theory of system dynamics, this paper analyses the risk factors of renewable energy investment. Secondly, the set of risk evaluation index factors and evaluation criteria are established. Thirdly, the evaluation index system is constructed, and the weight matrix is established by using the entropy weight method to calculate the specific weight of the evaluation index. Finally, the risk evaluation model of renewable energy investment based on fuzzy analytic hierarchy process is established to realise the fuzzy comprehensive evaluation of renewable energy investment risk. The experimental results show that the calculation error of this method is small and the evaluation time is short, it can realise the comprehensive evaluation of risk. Journal: Int. J. of Global Energy Issues Pages: 153-165 Issue: 2 Volume: 45 Year: 2023 Keywords: fuzzy analytic hierarchy process; renewable energy; risk assessment; system dynamics; entropy weight method. File-URL: http://www.inderscience.com/link.php?id=129508 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijgeni:v:45:y:2023:i:2:p:153-165