Forthcoming and Online First Articles

International Journal of Global Energy Issues

International Journal of Global Energy Issues (IJGEI)

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International Journal of Global Energy Issues (36 papers in press)

Regular Issues

  • Price and volatility of rare earths   Order a copy of this article
    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
  • Oil market crashes: from the subprime crisis to the COVID-19 pandemic   Order a copy of this article
    by Donia Aloui, Rafla Hchaichi, Khaled Guesmi 
    Abstract: This paper aims to examine whether and how the COVID-19 crisis is similar to previous economic crises. Using a TVP-BVAR-SV model, we compare the responses of WTI-oil and Natural Gas prices in the face of the COVID-19 crisis to that of the 2008 global economic crisis and the 2014 Asian crisis. The findings confirm a remarkable similarity of WTI-oil prices behavior between 2008 and 2020 in terms of volatility and responses to the petroleum consumption shocks. However, the Natural gas market remains relatively stable during COVID-19 with modest responses to the shocks. The predictive probability density shows a high predictive variance indicating extreme uncertainty surrounding the COVID-19 period.
    Keywords: COVID-19; subprime crisis; oil price war; negative WTI oil price; natural gas; TVP-BVAR-SV model; stochastic volatility; forecasting.
    DOI: 10.1504/IJGEI.2023.10057452
  • Ultra-short-term wind power prediction based on lightweight learning machine with error compensation   Order a copy of this article
    by Huifang Qian, Yunhao Luo, Xuan Zhou, Ren-Ying Li, Jiahao Guo 
    Abstract: The wind power prediction model has been improved in order to obtain higher prediction accuracy, but this model structure then becoming complicated and the training time is prolonged. Therefore, this paper proposes a Lightweight Learning Machine with Error Compensation (LLM-EC), which consists of two parts: prediction and error compensation. The Lightweight Learning Machine (LLM) accomplishes the prediction part by learning the historical patterns of wind energy and related factors. To improve prediction accuracy, this paper incorporates an Improved Temporal Attention Mechanism (ITAM) into LLM. In the error compensation part, the prediction results of the LLM are re-compensated using the Error Compensation Machine (ECM) to reduce the error accumulation during the rolling prediction process. Finally, a comparison of the benchmark model with LLM-EC in terms of prediction accuracy, training time, and memory usage reveals that LLM-EC has significantly less prediction error; less training time; and less memory occupied by the model.
    Keywords: ultra short-term wind power; lightweight construction; attention mechanism; error compensation.

  • Reassessing fossil fuels in a time of disruption: hydrogen, natural gas and future possibilities   Order a copy of this article
    by Ryan Holmes, Darren McCauley, Thomas Muinzer 
    Abstract: Energy governance is undergoing a period of uncertainty as a result of climate related challenges and these uncertainties have been profoundly exacerbated by the COVID-19 pandemic. The stage of uncertainty energy governance is experiencing can be characterised as one of disruption. This study reassesses the condition of energy governance in the context of disruption with particular reference to the role that hydrogen and natural gas might play in going forward. In doing so, it places an emphasis on the protracted impact of disruption occasioned by climate change regulation. Greater understanding of the strengths and weaknesses that stabilising options provide, and of their relationship to the broader complex disruptive forces that they interact with, will assist in better illuminating present and future challenges underlying the governance of energy.
    Keywords: energy governance; energy transition; just transition; hydrogen; natural gas.

  • Energy consumption, income and carbon emissions in the Caribbean community   Order a copy of this article
    by Ermanno Affuso, Alex Sharland 
    Abstract: This original research uses a panel vector autoregressive model to study the relationship between energy consumption, carbon emissions and the macroeconomic dynamics of 14 economies between 1960 and 2017; all full members of the Caribbean Community (CARICOM). The model is calibrated using economic and environmental public data from the World Development Indicators repository, the Climate Research Unit of the University of East Anglia and the FRED repository of the St. Louis Federal Reserve Bank. The study finds evidence of (i) positive unidirectional causality from energy consumption to economic growth, (ii) positive bidirectional causality between energy consumption and growth in carbon emissions, and (iii) negative bidirectional causality between economic growth and carbon emission growth. Implications are discussed.
    Keywords: energy economics; development economics; macroeconomic dynamics; Caribbean; panel data.

  • Reliability evaluation, lifetime prediction and failure rate assessment of Li-ion batteries   Order a copy of this article
    by Aryan Tabrizi, Mohammad Sarvi 
    Abstract: Recently, rechargeable lithium-ion batteries (Li-ion) have been used as a suitable energy storage source in many applications due to their advantages. Reliability is a key factor in battery utilizations same as any other process. It is essential to know how reliable a battery cell or pack is. Hence, it could be more precise to predict the failure time of the battery. The main multiple purposes of this paper are to assess the reliability of the typical battery packs/cells, to estimate their failure rate, and to evaluate their lifetime by some probability distribution function. In each case, the proper approach is determined and the reliability of the battery alongside its predicted failure time is estimated. Also, it can be estimated that what percentage of batteries (in stationary energy storage) is going to fail in a desirable operation time. The reliability calculations are done in Minitab software.
    Keywords: Li-ion battery; reliability; Weibull; failure rate; predicted lifetime.

  • Study on coupling coordination between ecosystem service value and carbon reserve based on land use change - taking Chengdu-Chongqing economic circle as an example   Order a copy of this article
    by Anlin Li, Lede Niu, Yan Zhou, Jingzhi Lin 
    Abstract: Taking the Chengdu Chongqing Economic Circle as an example, this study calculates its ecosystem service value, carbon storage, and coordinated development level. The conclusion is as follows: (1) from 2000 to 2010, land use was transformed into forest land>construction land>cultivated land>other land>water bodies>grasslands; from 2010 to 2020, construction land>water body>other land>forest land>grassland>cultivated land. (2) The ecosystem service values from 2000 to 2020 were 2842.78 x 108, 3114.35 x 108, and 2780.50 x 108 yuan, showing an inverted U shape with a spatial distribution of high on the periphery and low on the interior; from 2000 to 2020, the carbon storage was 57.87 x 108 t, 58.28 x 108 t, and 58.21 x 108 t, presenting a low value urban bipolar pattern. (3) The relationship between ecosystem service value and carbon storage is clearly non coordinated, and there is no positive interaction between the two, resulting in weak coordination ability.
    Keywords: land use; ecosystem service value; carbon stock; coupled coordination; Chengdu-Chongqing area; twin-city economic circle.
    DOI: 10.1504/IJGEI.2024.10062680

Special Issue on: Smart Energy Infrastructures for Smart Cities

  • Cloud computing load balancing based on improved genetic algorithm   Order a copy of this article
    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   Order a copy of this article
    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 scheme's 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   Order a copy of this article
    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 manufacturer's 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   Order a copy of this article
    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
  • Comprehensive energy service customer value evaluation model   Order a copy of this article
    by Wei Yao, Wei Han, Yong Zheng, Songyao Gao, Ran Li 
    Abstract: At present, there are a series of energy supply and consumption problems in society. The maintenance costs of heat, gas, electricity and other energy sources are high. The integrated energy system effectively reduces the contradiction between energy supply and demand and improves the comprehensive utilisation efficiency of energy through scientific and logical multi-source interconnection awareness. Based on the customer segmentation and customer value theory in the integrated energy field, this paper considers the current and potential costs of customers, and forms a comprehensive customer energy value estimation system. The combined weighting method based on the AHP-entropy weighting method weights the customer value evaluation index and establishes a density peak clustering algorithm based on the improved artificial bee colony. On this basis, a customer value evaluation model is established. The comprehensive energy customer value is collected, and the comprehensive energy adaptive service model of the integrated energy system is analysed.
    Keywords: model study; evaluation model; customer value evaluation; integrated energy service.
    DOI: 10.1504/IJGEI.2023.10056905
  • Causes of concrete cracks in the construction of roads and bridges facing intelligent manufacturing technology   Order a copy of this article
    by Jinglian Zhen 
    Abstract: This paper takes concrete cracks in road and bridge construction as the research object, and realises intelligent activities such as analysis, reasoning, judgement, conception and decision-making in the manufacturing process through intelligent manufacturing. First, the basic characteristics of intelligent manufacturing technology are analysed, the apparent shape and characteristics of bridge deck cracks are introduced and the bridge deck paving structure with several typical cracks is numerically simulated using finite element software, and the theory of fracture mechanics is used to study the mechanical behaviour of bridge deck pavement working with cracks includes temperature stress, load stress and additional stress caused by differential settlement of the main beam. Finally, the influence of the pavement thickness, modulus, cement concrete cushion thickness and other parameters on the force of the pavement layer is analysed, and the sensitivity analysis of the pavement layer force is carried out.
    Keywords: intelligent manufacturing technology; road and bridge construction; bridge performance; bridge crack problem.
    DOI: 10.1504/IJGEI.2023.10057071
  • Research of aerodynamic performance of high-speed train crossing canyon with CFD simulation   Order a copy of this article
    by Zheng Wang, Xiaoling Zhang, Mingkang Zhang, Yang Sun 
    Abstract: Based on computational fluid dynamics, the aerodynamic performance and safety of a train running in a typical canyon terrain are simulated by numerical simulation. By comparing the variation law of surface pressure and aerodynamic characteristics of the train with speed of 250 km/h under the action of the canyon wind field at different speeds, under the action of different canyon wind speeds, the variation law of the surface pressure and aerodynamic load of the train at different positions is consistent; with the increase of wind speed, its aerodynamic. The load increases sharply, and the tunnel environment at both ends of the canyon will have a corresponding impact on the train operation; when the speed of crosswind increases to 25 m/s. It's dangerous for the train running cross the canyon with speed of 250 km/h.
    Keywords: high-speed trains; aerodynamics characteristics; CFD simulation; cross-wind effects; operation security.
    DOI: 10.1504/IJGEI.2023.10057235
  • Indoor environment design of old-age green buildings based on environmental energy efficiency   Order a copy of this article
    by Yi Wu 
    Abstract: With the development of population ageing and the intensification of environmental pollution, the development of elderly care buildings suitable for the elderly population is also facing severe challenges. This paper aims to carry out the interior environment design of green senior care buildings based on environmental energy efficiency. This paper reversely adjusted the number and proportion of the population over 60 years old in China, and tested and compared the noise level of each measuring point. The experimental data shows that from the national annual average level, TECH and EFFCH have increased by 1.022 and 1.010, respectively, indicating that the total factor energy efficiency has been improved and increased. The value of TECH is greater than that of EFFCH, indicating that technological progress has a greater impact and can be done better.
    Keywords: environmental energy efficiency; factor analysis of old-age buildings; green buildings; interior environment design.
    DOI: 10.1504/IJGEI.2023.10057301
  • Construction and risk prevention of real-time renewable energy internal control management system based on big data   Order a copy of this article
    by Qianqian Yu 
    Abstract: With the advent of the era of big data, more and more industries have opened a new management era. This paper aims to study the related systems of real-time renewable energy in the era of big data. Designs a real-time renewable energy internal control management system based on big data. It carries out risk prevention for the renewable energy management system. By testing the actual operation performance of the system, the results show that the information response and receiving ratio of the system is at least about 85%, and the highest is more than 99%, which can meet the basic requirements. In addition, even in the case of the data volume of 5000 KB per second, the system running space does not exceed 90% and there is enough running space. Therefore, the renewable energy internal control management system designed in this paper has certain feasibility and system stability.
    Keywords: real-time big data; renewable energy; internal control and management system; risk prevention and control.
    DOI: 10.1504/IJGEI.2023.10057390
  • Development of a low-carbon economy with the dual transformation of economy and energy structure   Order a copy of this article
    by Xiang Huang, Huami Yi, Min Liu 
    Abstract: This article investigates the industry 4.0 recent shift toward a more environmentally responsible way of operation. Life Cycle Assessment (LCA) is used in this article to measure carbon emissions throughout the product development life cycle. An accurate accounting of carbon emissions must first be completed for this to work. Consequently, IoT technologies have been used to collect real-time data with confidence to monitor the environmental consequences of products throughout the life cycle. Energy efficiency, energy structure optimisation and market-based economic measures such as energy/carbon taxes have minimised energy consumption and carbon emissions under a long-term low-carbon strategy. The proposed method shows less-energy conservation rate of 15.6%, an emission rate of 11.8%, a high-energy efficiency rate of 94.5%, a production rate of 90.8% and a sensitivity rate of 97.6% compared to other methods.
    Keywords: carbon emission; GHG; greenhouse gas emissions; IoT; internet of things; low-carbon economy; LCA; life cycle assessment; GEM; GHG emission monitoring.
    DOI: 10.1504/IJGEI.2023.10057391
  • Food safety supply chain from perspective of big data algorithm and energy efficiency   Order a copy of this article
    by Mian Deng, Yong Wang 
    Abstract: At present, food safety incidents emerge in an endless stream, so the relevant fields related to food safety issues have become a research hotspot. In order to effectively ensure food safety, it is necessary to control all aspects of the supply chain. In order to test the effect of Principal Component Analysis (PCA) and mutual Information Principal Component Analysis (MI-PCA) on the data set, the loss value and the predicted value of the data set were compared. The results show that the predicted value of PCA algorithm fluctuates obviously, while the predicted value of MI-PCA algorithm tends to be stable after 100 iterations. The prediction accuracy is also greater than 95%, and the prediction effect is good.
    Keywords: supply chain; data dimensionality reduction; principal component; food safety; energy efficiency.
    DOI: 10.1504/IJGEI.2023.10058831

Special Issue on: Challenges and Sustainable in Energy

  • An optimal load distribution method for distributed energy systems based on the improved particle swarm optimisation   Order a copy of this article
    by Junjun Liu 
    Abstract: In order to solve the problem of large load variance and high distribution scheme cost after the distributed energy system is integrated into the traditional large power grid, an optimal load distribution method for distributed energy systems based on the improved particle swarm optimisation algorithm is proposed in this paper. Firstly, four output models of the distributed energy system are established. With the minimum cost and the minimum system load variance as the objectives, a multi-objective function model is constructed. Considering the power limit, generation power limit and other restrictions, constraints are constructed to complete the construction of the optimal load distribution model of the distributed energy system. Finally, the PSO algorithm is introduced to update the optimal particles in the solution space through different iterative processes. Combined with quantum theory, the PSO algorithm is optimised to obtain the optimal load distribution scheme. The results show that the cost of the distribution scheme obtained by this method can be reduced by more than 30,000 Yuan, and its load variance value is smaller, so the method has certain research value.
    Keywords: improved particle swarm optimisation; distributed energy system; output model; multi-objective function; constraints; load distribution.

  • Power supply reliability evaluation of distribution network based on non intrusive low voltage power load identification and time series algorithm   Order a copy of this article
    by Xiaoming Lin, Fan Zhang, Mi Zhou, Jianlin Tang, Bin Qian, Wenqian Jiang 
    Abstract: In order to overcome the problem of low reliability evaluation accuracy existing in traditional power supply reliability evaluation methods, a new power supply reliability evaluation method based on non-invasive low-voltage power load identification and time series algorithm is proposed in this paper. Firstly, a non-invasive low-voltage power load acquisition device is designed, and the adaptive Gauss filtering method is used to denoise. Secondly, the characteristics of low-voltage power load are extracted, and the characteristic parameters are input into the limit learning machine model to complete the identification of low-voltage power load. Finally, the time series algorithm is used to calculate the power supply reliability evaluation index of the distribution network, and the power supply reliability evaluation of the distribution network is completed. The experimental results show that the proposed method has high accuracy of low-voltage power load identification and reliability evaluation, and the highest evaluation accuracy is 97%.
    Keywords: non-intrusive low-voltage power load identification; timing algorithm; distribution network; power supply reliability; Gaussian filtering.

  • Combined forecasting of terminal load based on grey depth belief network   Order a copy of this article
    by Li Zhang, Zhiyun Sun, Jingjing Huang, Xiaolong Lu, Hewei Chen, Qizhen Wei 
    Abstract: In order to improve the prediction accuracy of electric energy consumption of civil aviation airport terminal, a combined prediction model of terminal load based on grey depth belief network is proposed. Firstly, the operation data of the airport is analysed to determine the main factors affecting the power consumption of the airport terminal; Then, the improved grey prediction model is established by using the historical data of electric energy consumption, and the grey prediction results, the characteristics of multidimensional historical power consumption data and the main factors affecting electric energy consumption are taken as the inputs of the deep belief network; Finally, the power consumption of the terminal is predicted based on this model. The experimental results show that the proposed grey depth belief network combination model has low prediction error, and the Mean Square Error (MSE) and Mean Relative Error (MRE) of the proposed model are 0.0988 and 0.0033.
    Keywords: grey model; deep belief network; urban transportation complex; load combination forecasting; passenger throughput; historical data of energy consumption; renewable energy.

  • Evaluation method of renewable energy absorptive capacity based on Monte Carlo   Order a copy of this article
    by Jinding He, Wenchao Qin 
    Abstract: Because the traditional assessment method of renewable energy absorptive capacity has the problems of low assessment accuracy and long assessment time, a Monte Carlo-based assessment method of renewable energy absorptive capacity is proposed. First, build a renewable energy absorptive capacity evaluation system, obtain the evaluation indicators, then analyse the renewable energy wind output characteristics, extract the characteristics of renewable energy absorptive capacity and then set the maximum renewable energy absorptive capacity, the system power balance, the minimum conventional power technology output, and the minimum production cost as the optimisation objectives to establish a multi-objective function for evaluation. Finally, under the constraint conditions, the objective function is solved by Monte Carlo method, and the solution is the evaluation result. The simulation results show that the proposed method has higher accuracy and shorter evaluation time for renewable energy absorptive capacity evaluation.
    Keywords: Monte Carlo; renewable energy; absorptive capacity; wind output; multi objective function.

  • Research on adaptive dispatching of smart grid considering the cost of renewable energy power generation   Order a copy of this article
    by Wenchao Qin, Jinding He 
    Abstract: In order to overcome the problems of poor convergence, high cost and long completion time of scheduling tasks in traditional methods, an adaptive dispatching method of smart grid considering the cost of renewable energy power generation is proposed. Firstly, the operation cost of smart grid is calculated from the total operation cost of conventional power generation unit, renewable energy power generation unit and energy storage unit. Then, combined with the benefits of flexible load, a smart grid adaptive dispatching model is built. Finally, under various constraints, the distributed reinforcement learning is used to solve the scheduling model and the adaptive scheduling results of smart grid are obtained. The experimental results show that the scheduling model solving algorithm of this method converges in 43 iterations, and the total operation cost of smart grid is 5.68x107 yuan, and the scheduling task completion time is always less than 0.48 s.
    Keywords: cost of renewable energy power generation; smart grid; adaptive scheduling; conventional power generation unit; energy storage unit; distributed reinforcement learning.

  • A prediction method of regional carbon emission peak based on energy consumption elasticity coefficient   Order a copy of this article
    by Yingjie Zhang, Dongyuan Zhao 
    Abstract: In order to solve the shortcomings of the traditional methods in prediction accuracy and prediction efficiency, this paper proposes a regional carbon emission peak prediction method based on the elastic coefficient of energy consumption. First, carbon emission information is extracted directionally. Then, the elastic coefficient of energy consumption is calculated, and the carbon emissions are preliminarily calculated. After obtaining the carbon emissions in different paths, Lasso regression analysis method is used to analyse the impact of the elastic coefficient of energy consumption on the prediction results. By adjusting the harmonic parameter values to optimise the calculation results, the peak prediction results of carbon emissions are obtained after obtaining significant variables. Experimental results show that the prediction accuracy of this method is high, and the maximum kappa coefficient can reach 0.973. During the experiment, the method can complete 12 predictions, which shows that its prediction efficiency is relatively high.
    Keywords: industrial carbon emissions; carbon emission performance; information extraction; elasticity coefficient of energy consumption; carbon emissions; lasso regression analysis; peak prediction.

  • Study of solidification performance of PCM in a triplex-tube thermal energy storage system with double Y-shaped fins   Order a copy of this article
    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
  • Thermodynamic and carbon emission analysis with low GWP refrigerants in automobile air conditioning system   Order a copy of this article
    by Salma Khatoon, Munawwar Nawab Karimi 
    Abstract: Automotive air conditioning systems negatively impact the environment through emissions. These emissions are impacted by ambient temperature and engine speed. Hence, this paper compares thermodynamic performance and carbon emissions of the low GWP refrigerants such as R1234yf, R1243zf, R450A, R143m, and R161 against R134a at different evaporator, condenser, and ambient temperatures and engine speeds. It is found that higher ambient temperatures lead to higher work consumption. Also, Indirect emissions have a positive correlation with engine speed. After R161, refrigerant R134a has the highest input power, total exergy destruction, and cooling capacity. R1243zf, R143m, and R450A show approximately similar cooling capacities. Furthermore, at idle speed, R134a indicates the highest Total Equivalent Warming Impact (TEWI) of 7.65 tons CO2 per year. Whereas, at normal and high speeds, R161 shows the highest value of 12.38 and 17.33tons CO2 per year, respectively. R1243zf, 1234yf, and R450A are the best alternative refrigerants to R134a.
    Keywords: Automobile air conditioners; GWP; refrigerants; energy; exergy; fuel consumption; Total Equivalent Warming Impact; carbon emissions; engine speed.
    DOI: 10.1504/IJGEI.2023.10056811
  • Load flexible control method for wind and solar energy storage power plant considering the power side demand   Order a copy of this article
    by Yinghan Luo, Jincai Wu, Yanhong Ma 
    Abstract: In order to improve the accuracy of load flexible control and shorten the control time, a load flexible control method for wind and solar energy storage power plant considering the demand of the power side is proposed. Firstly, the data on the power side is cleaned and normalised according to the clustering results of Euclidean distance. Secondly, the normalised power side data is input into the support vector machine classification to complete the calculation of power side demand. Finally, the expected control quantity of wind turbine generator set is taken as the objective function of load flexible control, and the objective function is solved by particle swarm optimisation algorithm to complete the load flexible control of wind turbine storage power plant. The experimental results show that this method can improve the control accuracy, the maximum accuracy reaches 96%, and the maximum time consuming control does not exceed 3 minutes.
    Keywords: demand of the power side; wind and solar energy storage power plant; load flexible control; support vector machines.
    DOI: 10.1504/IJGEI.2023.10058732
  • The power load prediction of green building based on multidimensional data mining   Order a copy of this article
    by Bo-Yang Zhang, Lei Shi, Jin-Yu Fan 
    Abstract: In order to solve the problems of low recall and precision and high-prediction error in traditional prediction methods, a power load prediction of green building based on multidimensional data mining is proposed. The initial clustering centre and feature weight of fuzzy k-means algorithm (FKM) clustering algorithm are optimised, and the improved FKM clustering algorithm is used to mine multi-dimensional green building power load data. The multi-dimensional data mining results were taken as sample data, and the Least Squares Support Vector Machine (LSSVM) model parameters were optimised by Particle Swarm Optimisation with Extended Memory (PSOEM) algorithm. The sample data were input into the optimised model to obtain the power load prediction results of green buildings. The experimental results show that the average recall rate and precision rate of the proposed method are 96.31% and 96.13%, respectively, and the prediction error rate fluctuates between -2% and 2%, indicating high-prediction accuracy.
    Keywords: multidimensional data mining; green building; power load prediction; FKM clustering algorithm; PSOEM-LSSVM model.

Special Issue on: Renewable Energy, Innovations, Energy and Economic Security

  • Economic and environmental drivers of physical safety in Central Europe   Order a copy of this article
    by Oleksandra Karintseva, Oleksandra Kubatko, Oleksandr Derykolenko, Vitaliy Omelyanenko, Viktoria Sulym, Anastasiia Yaremenko 
    Abstract: Physical safety is not only about the actual safety of humans but also their mental health and calmness. The article examines the key drivers of peoples physical safety, well-being and satisfaction with life. The study covers seven Central European countries during 2011-2018. The random effects estimations for the panel data are used for empirical estimations. The study found that crime/violence, unemployment and noise from neighbours negatively impact individuals physical safety. The empirical results proved that an increase in median income by 1000 euros in Central European states promotes an increase in life expectancy by 0.7 years. However, if unemployment rises by 10%, the decline in life expectancy would range from 0.7 to 1.19 years. The paper proves that the marriage factors like indicators of moral factors are an inevitable part of a healthy society. Noise from the neighbours is considered to be an object of irritation and reduces the level of physical safety of EU citizens. Thus, governments need to stay on top of the problems mentioned above to cope with them.
    Keywords: physical safety; economic development; well-being; life satisfaction.

  • Industry 4.0: the transformation of management systems and influence tools   Order a copy of this article
    by Larysa Shaulska, Hanna Bei, Galina Zaharieva, Andrey Zahariev 
    Abstract: The article focuses on management system transformation, considering changes caused by the technological renewal of enterprises due to the standards of Industry 4.0. The aim of the article is to explore how enterprise management systems change in response to Industry 4.0 technologies and to identify effective management influence tools within the smart ecosystem. Method of scientific systematisation was applied to categorise key aspects of management tools transformation in Industry 4.0, namely technological, human, organisational and behavioural aspects. Results cover changes across corporate, functional and individual levels. The source of empirical data was semi-structured in-depth expert interviews with managers of selected enterprises in Ukraine and Bulgaria that are in the process of transition to a smart ecosystem. All data was analysed using qualitative content analysis. The main findings reveal targeted aspects of management system transformation in Industry 4.0 to be done in the early stage. Originality of the article is among the first to give specific examples of management system transformation in Industry 4.0 aimed to accelerate overcoming of existing implementation barriers.
    Keywords: industry 4.0; management; management system; influence tools; digitalisation; transformation; technologies; behaviour.

  • Energy technology efficiency influence on energy poverty and energy justice in West African households   Order a copy of this article
    by Evrard Karol Ekouédjen, Safiou Bouraima, Gaston Ganhoun, Latif Adéniyi Fagbemi 
    Abstract: This paper presents a new indicator which measure energy poverty (as energy justice component) called Modified Energy Poverty Index (MOEPI). It is a composite index and developed based on United Nations Human Development Index (HDI) methodology. MOEPI defines energy poverty as three dimensions conjunction: excessive energy inconvenience, energy deficit and equipment energy (in)efficiency. It captures energy deficit effect on education and include energy acquisition cost in energy inconvenience assessment. MOEPI is implemented on a sample of 640 households in Benin. Results showed 65.15% energy poor households surveyed. Energy poor are divided in 3 sub-groups, slightly energy poor (6%), moderate energy poor (53%) and severe energy poor (41%). Energy deficit and equipment energy inefficiency are the main dimensions responsible for household energy poverty. An improvement in equipment energy efficiency resulted in a 32.02% decrease in the number of energy poor households.
    Keywords: energy efficiency; energy poverty; energy justice; index; energy deficit; energy inconvenience.

  • Energy consumption and climate change in Sub-Saharan Africa (SSA)   Order a copy of this article
    by A. Akinyemi Ajibola, Wisdom Okere, Oreoluwa Adedeji, Obiajulu Chibuzo Okeke, Cynthia Okere 
    Abstract: This study analysed energy consumption and climate change in SSA to validate the Environmental Kuznet Curve (EKC) theory. This study included multiple econometric tests, Autoregressive Distributed Lagged model (ARDL), Fully Modified Ordinary Least Squares (FMOLS) regression analysis and Granger Causality Test. In the Long run, Gross Domestic Product (GDP) and Electricity consumption (ELE) have a positive and significant relationship with climate change, measured by carbon dioxide (CO2) emissions, while Fossil energy consumption (FOS) and Renewable Energy Consumption (REN) do not. ELE has a positive relationship with climate change as assessed by CO2 emissions, while FOS and REN have a negative association. Only the ELE and FOS coefficients are significant at 5%. Since fossil fuels and renewable energy do not contribute to long-term climate change, energy consumption patterns have started to reflect their environmental policies. More eco-friendly techniques are needed to reduce electricitys environmental impact. The EKC found that SSA countries are evolving so that economic growths negative effects on climate change will be reversed. The study advises policymakers to adopt renewable energy to cut CO2 emissions.
    Keywords: climate change; CO2 emission; energy consumption; Sub-Saharan Africa; sustainability.
    DOI: 10.1504/IJGEI.2024.10061362
  • The impact of innovations and intellectualisation on sustainable national development   Order a copy of this article
    by Oleksandr Kubatko, Rytis Krušinskas, Leonid Melnyk, Bohdan Kovalov, Pavlo Denysenko 
    Abstract: Innovations are an integral part of the modern global economy. The purpose of this research is to investigate impact of innovations and intellectualisation on sustainable national development. Based on the World Bank sets, two models with panel data from selected economies (Bulgaria, the Czech Republic, Hungary, Kazakhstan, Poland, Romania, Moldova, the Slovak Republic, Ukraine and Uzbekistan) in 2006-2018 were built. Using random-effects GLS regression it was proved that factors of intellectualisation and innovations (both exogenous and endogenous) increase the level of economic growth. Empirical results proved that intellectualisation of the economy of endogenous origin is one of the stimulators of improving the environmental situation (when there is an increase in the number of researchers in the country by 100 people, the amount of CO2 per capita decreases by 84131 kg), while exogenous intellectualisation turned out to be a statistically insignificant factor. The paper proved several decarbonisation drivers, which include energy efficiency and life expectancy. Following the results, policy recommendations were provided and indicated the importance of national education development and innovation fostering. This can be achieved by revising learning standards according to market requirements, retraining educators and using a competence-based approach.
    Keywords: innovations; intellectualisation; sustainable development; sustainability; economic growth; CO2 emissions.

  • Military and economic prerequisites for transforming the energy supply of the housing sector of Ukraine based on Industry 3.0   Order a copy of this article
    by Oleksandr Matsenko, Leonid Melnyk, Yevhen Skrypka, Iryna Dehtyarova, Serhiy Kozmenko, Liudmyla Kalinichenko 
    Abstract: The paper investigates the direction of the transformation of the energy supply of the residential sector of Ukraine as a result of the aggression of the Russian Federation and the significant destruction of the energy infrastructure. The paper aims to investigate the sustainability of the energy supply of the residential sector in Ukraine and propose directions for its transformation. The research method is based on analysing the state of Ukraines residential sectors energy supply system and identifying the possibilities of its change into martial law conditions. The research examines the Ukrainian economy and infrastructure losses. Two alternative options for ensuring the energy dependence of the residential industry are suggested. The main measures to save energy and electricity for the population of Ukraine are presented step by step.
    Keywords: energy independence; energy efficiency; economy; residential sector; house; construction; modernisation; war; martial law.
    DOI: 10.1504/IJGEI.2024.10061968
  • A case for replacing local generators by a service using only renewable energy sources in the city of Sulaymaniyah   Order a copy of this article
    by Hariam Luqman Azeez, Banu Omer Ahmed, Ali H.A. Al-Waeli 
    Abstract: Despite the abundance of oil and gas reserves in the Kurdistan region, a critical challenge persists in fully meeting the regions electricity demand. Local investors, recognising the daily shortfall of electricity in homes lasting six to eight hours, have introduced diesel generators to bridge the gap. Presently, each neighbourhood relies on multiple diesel generators to address the electricity deficit. However, these local generators pose significant issues, including inadequate power supply, substantial pollution, noise emissions, high-fuel consumption and an unsightly appearance. Given the regions suitability for renewable energy, coupled with the intermittent nature of electricity demand, there arises a logical opportunity to develop a service as an alternative to local generators. This paper aims to explore the viability of such a service, intending to replace local diesel generators with a renewable energy solution. The study undertakes the following tasks: (i) conducting a life cycle assessment of local diesel generators to evaluate their environmental impact, cost-effectiveness and social acceptability, (ii) assessing the potential of Sulaymaniyah for renewable energy applications, particularly solar photovoltaic (PV) panels and wind turbines and (iii) incorporating a neighbourhood survey in Sulaymaniyah to gauge resident opinions and reactions toward the proposed renewable energy service.
    Keywords: local diesel generators; renewable energy service; cost analysis; social considerations; and environmental impact analysis.
    DOI: 10.1504/IJGEI.2024.10062449

Special Issue on: The Analysis of Energy Efficiency Perspectives and Policies towards Sustainable Development

  • Robust and efficient hybrid autoencoder-ADAM (HAA) algorithm for analysing anomalies in Indian electricity consumption data   Order a copy of this article
    by M. Ravinder, Vikram Kulkarni 
    Abstract: Anomaly detection in electricity-consumption data plays a crucial role in ensuring the reliability and stability of modern smart-grid systems. In this study, we propose the Hybrid Autoencoder-ADAM (HAA) algorithm, specifically designed for anomaly detection in Indian electricity consumption data from 2014 to 2023, considering distinct seasonal patterns. The HAA algorithm combines autoencoders with adaptive optimisation (ADAM) to effectively capture and reconstruct normal consumption patterns. Comparative analysis show that the HAA algorithm outperforms Long Short-Term Memory (LSTM) and XGBoost in accuracy and robustness for anomaly detection. It demonstrates adaptability across different seasons, regions and periods, offering valuable insights for advancing smart grid analytics and energy conservation strategies. Future research includes hyper-parameter optimisation and exploring ensemble methods to enhance its real-world applicability in operational smart-grid scenarios. The HAA algorithm presents a promising approach for large-scale smart grid anomaly detection, emphasising its efficiency and effectiveness in improving energy management and resource optimisation.
    Keywords: anomaly detection; HAA algorithm; smart grid; electricity consumption; LSTM; XGBoost; seasonal patterns.
    DOI: 10.1504/IJGEI.2024.10062681