International Journal of Global Energy Issues (40 papers in press)

Regular Issues

• Price and volatility of rare earths  
  by Auguste Mpacko Priso, Souleymane Doumbia 
  Abstract: The purpose of this paper is to discuss results of a statistical model for volatility of rare earths prices traded at the London Stock Exchange and compare it to the volatility of other metals prices as well as that of other stock prices. Although known for centuries, rare earths have drawn particular attention interest over recent years due to their potential solution to mitigate climate change effects. These metals with exceptional characteristics are used in high-tech product manufacturing, especially those seen as alternative to the consumption of fossil fuels like car batteries. We show that the volatility of all three indexes is persistent. The volatility model which best fits the rare earths prices is a gjrGARCH(1,1) model. This is to our knowledge the first time the persistent volatility framework is applied to price of rare earths. Our work paves the way to many other applications, including volatility forecasts of rare earths price. This latter can help investors improve their decision-making process.
  Keywords: metal prices; rare earths; climate change; volatility models; ARCH; GARCH models.
  DOI: 10.1504/IJGEI.2023.10056317
   
  
• Oil market crashes: from the subprime crisis to the COVID-19 pandemic  
  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  
  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  
  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  
  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  
  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  
  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
   
  
• Outward foreign direct investment and carbon emissions in the home country: evidence from China  
  by Zehao Li, Wenhui Lui, Minji Hu, Zongwen Zuo 
  Abstract: The objective of this study is to investigate the impact and operational mechanism of Outward Foreign Direct Investment (OFDI) on domestic carbon emissions. We utilise panel data collected from 30 provinces spanning the period between 2004 and 2017 for our analysis. The results show that OFDI has a significant inverse U-curve relationship to carbon emissions. After the robustness test, the results still hold. Heterogeneity analysis shows that in eastern China and economically developed regions, OFDI and carbon emissions show a significant U-shaped reversal. The analysis of mechanisms reveals that there exists a threshold effect in the impact process of OFDI on regional carbon emissions, particularly influenced by the share of the tertiary sector. When the tertiary sector share is higher than 73.2%, that is, when the industrial structure tends to be highly developed, the improvement of OFDI will reach the goal of carbon emission reduction. This paper offers policy recommendations for China, aiming to facilitate the expansion of OFDI, drive the enhancement of regional industrial structure and ultimately work towards achieving the objective of attaining carbon peak and carbon neutrality.
  Keywords: OFDI; carbon emission reduction; industrial structure; the goal of ‘carbon peak; carbon neutrality’.
  DOI: 10.1504/IJGEI.2024.10062755
   
  

Special Issue on: Challenges and Sustainable in Energy

• An optimal load distribution method for distributed energy systems based on the improved particle swarm optimisation  
  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  
  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  
  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  
  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  
  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  
  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  
  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  
  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  
  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  
  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  
  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  
  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  
  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)  
  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  
  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  
  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  
  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: Energy Saving Technology in Building

• Optimisation method of residential building energy conservation in hot summer and cold winter areas: particle swarm optimisation  
  by Wen Cao 
  Abstract: In this paper, an optimisation method of residential building energy conservation in hot summer and cold winter areas based on particle swarm optimisation algorithm is studied. First, considering the influence of external and internal factors of the residential environment and the change of energy consumption, select the energy-conservation parameters of residential buildings; Finally, the particle swarm optimisation algorithm is introduced to build the optimisation model of building energy conservation, and the optimisation results are corrected by inertia weight to complete the design. The test results show that the energy consumption of this method is 2796 KWh, the correlation coefficient is higher than 0.95, and the optimisation time is 1.27 s. This method can effectively reduce the energy consumption of residential buildings, and the optimisation speed is faster.
  Keywords: particle swarm optimisation algorithm; hot summer and cold winter areas; residential building; energy saving optimisation; particle fitness.
  DOI: 10.1504/IJGEI.2024.10062749
   
  
• An optimisation method for energy efficiency of residential buildings in cold regions based on genetic algorithm  
  by Dongmei Zhao, Gaoxian LI, Yifan Wu 
  Abstract: Owing to the high-energy consumption of residential buildings in cold regions, a genetic algorithm-based optimisation method for energy efficiency of residential buildings in cold regions is proposed. Firstly, identify the factors that affect the energy efficiency of residential buildings in cold regions and clarify the energy consumption of buildings; Then, select energysaving parameters for residential building orientation, exterior wall thickness and window to wall ratio, and use these parameters as optimisation indicators; Finally, the energy-saving parameters are encoded to generate an initial population, and the optimised energy-saving parameter operators are selected, crossed and mutated. A building energy-saving optimisation algorithm based on genetic algorithm is designed to achieve optimisation research. The test results show that the proposed method can effectively reduce building energy consumption in cold regions, and the wall to window ratio has a better shading coefficient.
  Keywords: genetic algorithm; cold regions; optimisation of building energy efficiency; building orientation; outer wall thickness; window to wall ratio; sunshade coefficient.
  DOI: 10.1504/IJGEI.2024.10062750
   
  
• A method for monitoring energy consumption data of near zero energy buildings based on BIM technology  
  by Ye Liao 
  Abstract: In order to improve the accuracy of monitoring energy consumption data of near zero energy buildings, this paper proposes a monitoring method for energy consumption data of near zero energy buildings based on BIM technology. Firstly, a near zero energy consumption building BIM model database including family file library and database is established. Secondly, the three-dimensional BIM model is constructed using Ecotect Analysis software. Then, the building energy consumption data output from the BIM model is analysed with the decision tree Analysis of algorithms; Finally, the momentum factor is used to optimise the BP neural network model, and the processed energy consumption data is used as the input of the BP neural network to output the monitoring results of near zero energy consumption building energy consumption data. The experimental results show that the application of this method can accurately monitor short-term and short-term energy consumption data for buildings with near zero energy consumption, and its monitoring error is less than 15 kW h, which has great application value.
  Keywords: BIM technology; near zero energy consumption buildings; energy consumption data; monitoring methods; family file library; momentum factor.
  DOI: 10.1504/IJGEI.2024.10062751
   
  
• Energy saving control method for central air conditioning systems in public buildings based on improved particle swarm optimisation  
  by YanHua Lou 
  Abstract: In order to reduce the energy consumption of central air conditioning system in public buildings, an energy-saving control method based on improved particle swarm optimisation was proposed. This method first analyses the structure and control principle of the central air conditioning system of public buildings, and obtains the result that the air conditioning system flow can be controlled by frequency conversion and speed regulation to reduce energy consumption. Then, on this basis, the energy-saving control problem is transformed into an optimisation problem, and the objective function is designed to complete the establishment of the energy-saving control model of the central air conditioning system. Finally, the solution is completed based on the improved particle swarm optimisation algorithm. The optimal scheme of multi-device variable frequency speed regulation that can minimise energy consumption is obtained. By controlling the water flow rate and fan speed of the central air conditioning system, the energy saving control of the central air conditioning system is completed. The test shows that the energy consumption of each equipment in the air conditioning system is reduced by 20.3 to 1.2% after using this method, which is superior to the comparison method and has great application value.
  Keywords: improving particle swarm optimisation; public buildings; central air conditioning system; energy saving control; variable frequency speed regulation; counter.
  DOI: 10.1504/IJGEI.2024.10062752
   
  
• Maximum power tracking method for roof solar cells in intelligent buildings based on particle swarm optimisation  
  by Yingjie Wang, Caihong Chu 
  Abstract: The maximum power tracking of the rooftop solar cells of intelligent buildings cannot be tracked quickly when the effective photovoltaic array is under uniform illumination because of the slow convergence speed. Therefore, a new method of maximum power tracking of the rooftop solar cells of intelligent buildings based on particle swarm optimisation algorithm is proposed. Firstly, the solar cell model is established, and the influence factors of temperature and light intensity are identified as the factors affecting the tracking effect. Then, the particle swarm optimisation algorithm is introduced to determine the initial position of the battery power parameters. Finally, based on the particle swarm optimisation algorithm, the maximum power tracking of solar cells on the roof of intelligent buildings is realised by solving the function repeatedly. The results show that the proposed algorithm has higher tracking accuracy and better dynamic response ability, and the tracking accuracy is improved by 3.7% and the maximum power point can be tracked again in a short time.
  Keywords: mathematical model of photovoltaic cells; I-U characteristic equation; guided wave function; particle swarm optimisation; maximum power point tracking.
  DOI: 10.1504/IJGEI.2024.10062753
   
  

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  
  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
   
  
• Collaborative planning method for integrated energy system based on improved compressed sensing algorithm  
  by Yan Li, Xiaojun Zhu, Qingshan Wang, Qiong Wang, Na Li, Yinzhe Xie, Zhu Chen 
  Abstract: Aiming at the problems of high-energy cost, high-energy consumption and environmental pollution in existing methods, a collaborative planning method for integrated energy systems based on improved compressed sensing algorithm is proposed. Build a comprehensive energy system architecture that includes modules for energy production, storage and conversion, transmission and distribution, consumption and management. Establish a collaborative planning mathematical model based on the characteristics of the architecture, set three objective functions: total energy consumption, total cost and total pollutant emissions, and set corresponding energy consumption, cost and environmental protection constraints. The improved compressed sensing algorithm is used for the integrated energy system collaborative planning, and the optimal solution is output, which is the optimal integrated energy system collaborative planning scheme. The experimental results show that the proposed method effectively reduces energy costs and energy consumption, and significantly reduces carbon dioxide emissions, indicating that the proposed method has practical value.
  Keywords: improved compressed sensing algorithm; integrated energy system; search for updates; constraint condition.
  DOI: 10.1504/IJGEI.2024.10063314
   
  
• A data-driven energy consumption prediction method for building electrical equipment based on data-driven  
  by Xiulan Yin, Huiting Liang 
  Abstract: A data-driven energy consumption prediction method for building electrical equipment based on data-driven is proposed to address the issues of unstable prediction results and low accuracy in existing methods. Multiple sensors are selected to collect voltage, power, temperature and humidity data of electrical equipment. The mean filling method is used to fill in the missing values of the collected data. The K-means algorithm is used to detect anomalies in the filled data, identify and remove abnormal clusters or samples. Based on the data processing results, particle swarm optimisation algorithm is used to train energy consumption data, construct an energy consumption prediction model and achieve energy consumption detection through this model. The experimental results show that the highest prediction accuracy of this method is 98.5%, and the difference between the predicted results and actual energy consumption is small, indicating that the stability and robustness of this method are strong.
  Keywords: data-driven; electrical equipment; energy consumption prediction; multiple sensors; k-means algorithm; particle swarm optimisation.
  DOI: 10.1504/IJGEI.2024.10063315
   
  
• Optimisation of solar thermal photovoltaic heating systems for buildings considering stability  
  by Hongwei Jia 
  Abstract: In order to improve its power generation efficiency and output power, and ensure the sustainability and stability of the system, the optimisation study of building solar thermal photovoltaic heating system considering stability is carried out this time. This method first analyses the operating principle of photovoltaic heating systems used in buildings, then constructs an output power model of the photovoltaic heating system, and fully considers the stability of the heating system operation to design an objective function. Finally, based on the improved Particle Swarm Optimisation with Adaptive Elite Strategy Algorithm (PSO-AESA) algorithm, the output power model of the photovoltaic heating system is solved, Realise optimal control of photothermal photovoltaic heating systems for buildings. The experimental results show that the total power generation efficiency and output power of the heating system are higher after the proposed method is used to optimise the control. The system control is better than the comparison method, and has high-application value.
  Keywords: stability; photothermal photovoltaics; heating system; control optimisation; source load energy storage.
  DOI: 10.1504/IJGEI.2024.10063316
   
  
• Intelligent scheduling optimisation strategy for comprehensive energy systems  
  by Kun Yan, Hongwei Dong, Tao Han, Jin Zhu 
  Abstract: In order to improve the output of integrated energy system and reduce the high-operation cost, a coordinated optimal scheduling method based on improved genetic algorithm was proposed. Aiming at the problems of poor output and high operating cost after the application of existing energy system scheduling methods, an integrated coordinated optimal scheduling method based on improved genetic algorithm is proposed. Firstly, the structure of the integrated energy system is analysed, and then the output of wind turbine, incentive demand response, gas turbine, etc., is analysed, and the output model of the integrated energy system is built. Finally, the optimal scheduling model of the energy system is established, and the improved genetic algorithm is used to solve it, and the optimal scheduling of the integrated energy system is realised. The experimental results show that the system output is the best, the operation cost is the lowest, and it can meet the operation requirements of the integrated energy system.
  Keywords: improved genetic algorithm; integrated energy system; coordination and optimisation; scheduling algorithm.
  DOI: 10.1504/IJGEI.2024.10063317
   
  
• Capacity configuration method for new energy storage system based on segmented peak shaving  
  by Zesen Li, Bingjie Li, Guojing Liu 
  Abstract: To overcome the problems of low accuracy in capacity estimation, low balancing degree and low utilisation rate in traditional methods, a capacity configuration method for new energy storage system based on segmented peak shaving is proposed. The battery internal resistance and terminal voltage signals of the new energy storage system are taken as inputs, and the capacity estimation is the output. A capacity estimation model based on an improved fuzzy neural network is established. The capacity configuration objective function is constructed by combining segmented peak shaving and economic cost. Hybrid frog-leaping algorithm is used to obtain the optimal parameters for segmented peak shaving and economic cost through population initialisation, position updates and frog swarm sorting to determine the optimal configuration scheme. Experimental results show that the average accuracy of capacity estimation using this method is 97.31%, the maximum balancing degree is 0.98 and the minimum utilisation rate is only 90.9%.
  Keywords: segmented peak shaving; new energy storage system; capacity configuration; improved fuzzy neural network; hybrid frog-leaping algorithm.
  DOI: 10.1504/IJGEI.2024.10063318
   
  
• A demand side energy scheduling method for energy-saving buildings based on priority weights  
  by Yining Sun 
  Abstract: In order to improve the voltage stability of energy scheduling and shorten scheduling time, a priority weighted demand side energy scheduling method for energy-saving buildings is proposed. Firstly, analyse the demand side incentive response behaviour of energy-saving buildings through load superposition. Secondly, construct priority decision-making indicators for energy scheduling. Construct a hesitant fuzzy matrix and calculate the membership score, and calculate the correlation coefficient between priority indicators. Finally, based on the correlation coefficient of the indicators, priority weights are calculated, and on the basis of normalised calculation of priority weights, an energy scheduling function is constructed to transform the scheduling engineering problem into a mathematical problem, solving the scheduling function to complete the demand side energy scheduling of energy-saving buildings. The experimental results show that the proposed method can shorten the time of energy scheduling, and the maximum scheduling time of the proposed method does not exceed 4 minutes.
  Keywords: priority weight; energy-saving building; demand side; energy scheduling.
  DOI: 10.1504/IJGEI.2024.10063319
   
  
• An evaluation of low-carbon collaborative emission reduction effect of new energy wind and solar power generation based on set pair analysis  
  by Peidong He, Xiaojun Li, Shijiong Yuan, Keli Liu, Xiaoxiao Yang 
  Abstract: In order to overcome the problems of poor accuracy and poor evaluation of emission reduction effects, this article introduces set pair analysis to evaluate the low-carbon collaborative emission reduction effect of new energy solar power generation. Firstly, construct an evaluation index system; Then, the subjective weight of the indicators is calculated using the intuitive fuzzy analytic hierarchy process, the objective weight is calculated using the entropy weight method and the comprehensive weight value of the indicators is calculated using game theory; Finally, the emission reduction effect evaluation function is constructed using set pair analysis theory, and the evaluation level is determined using confidence to obtain the final evaluation result. The results show that the evaluation accuracy of the method in this paper can reach 99.5%, and this method can accurately evaluate the low-carbon collaborative emission reduction effect of new energy wind power generation.
  Keywords: set pair analysis; new energy; wind and solar power generation; low carbon; collaborative emission reduction; effect evaluation.
  DOI: 10.1504/IJGEI.2024.10063320
   
  
• Optimisation method for economic dispatch of wind power connected to microgrid considering carbon emission  
  by Hui Li, Xin Wen, Zhengyang Peng, Jing Zhang, Shitao Chen 
  Abstract: In order to improve the economic dispatching effect of distribution network, the optimisation method of economic dispatching of wind power connected to microgrid considering carbon emissions is studied. Firstly, taking the minimum operating cost and environmental cost of wind power connected to microgrid as the design goal, and fully considering equality constraints and inequality constraints, an economic scheduling optimisation model of wind power connected to microgrid is constructed. Then, the improved particle swarm optimisation algorithm is used to solve the economic scheduling optimisation model of wind power connected to microgrid, and the economic scheduling optimisation is realised. Finally, the practicability of the proposed method is proved by experiments. The experimental results show that this method has strong calculation ability and good iterative performance in model solving, and the application of the proposed method can get more ideal economic dispatching effect and has high application value.
  Keywords: carbon emissions; wind power access; microgrid; economic dispatch optimisation; particle swarm optimisation; abandoned air volume.
  DOI: 10.1504/IJGEI.2024.10063321
   
  
• High proportion new energy grid voltage fluctuation tracking method based on double layer master slave game  
  by Yiming Peng, Mingdong Guan, Dongxu Li, Jia Wang, Hanzhi Zhang 
  Abstract: In order to reduce the error of voltage fluctuation tracking and shorten the tracking time, a high proportion new energy medium voltage power grid voltage fluctuation tracking method based on a double layer master slave game is proposed. Firstly, construct a two-layer master-slave game model, analyse the voltage regulation strategy of the high proportion new energy grid using the utility function, and collect operational data of the new energy grid. Secondly, wavelet transform is used to analyse the sudden changes in voltage signals and identify the sources of voltage fluctuations in the medium voltage power grid. Finally, the Hilbert Huang transform is used to decompose the voltage fluctuation source signal, obtain the instantaneous fluctuation frequency of the power grid voltage and complete the tracking of voltage fluctuations. The results show that the voltage fluctuation tracking error of the proposed method is significantly reduced, with a maximum error of only 1.3 V.
  Keywords: double layer master slave game; high proportion; new energy grid; voltage fluctuation tracking; medium voltage distribution network.
  DOI: 10.1504/IJGEI.2024.10063322