International Journal of Global Energy Issues (19 papers in press)

Special Issue on: Energy-Saving Technology in Building

• 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, 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 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
   
  
• 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, the energy-conservation parameters of residential buildings are selected. Then, 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 calculate the energy consumption of buildings. Then, select energy-saving 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 energy consumption of buildings 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
   
  
• Identification of commodity purchase indicators in Iran's oil and gas industry using the fuzzy Delphi method  
  by Mahdi Soufizadeh, Seyyed Mahdi Mirmehdi 
  Abstract: Owing to the high value of trading goods and equipment the oil and gas industry needs, this market has always been particularly attractive for marketers. For this reason, this applied research was conducted in a mixed manner to use the results to develop the purchase strategy of oil companies as well as develop the marketing strategy of companies active in the field of selling goods and equipment needed by the oil industry. It was tried to identify and prioritise purchasing indicators using the fuzzy Delphi method. Also, the data of the research was collected by two methods of interview and questionnaire from the statistical sample of the research, who were purposefully selected from among the experts engaged in purchasing goods of major Iranian oil and gas companies. In the end, after analysing the data, 18 indicators were identified as effective indicators on the purchase of goods by these experts. After prioritisation, the technical ability of the producer or supplier of goods was identified as the most critical indicator in this field.
  Keywords: oil and gas; buying goods; fuzzy Delphi; sanctions.
  DOI: 10.1504/IJGEI.2025.10070195
   
  
• Asymmetric pass-through of crude oil prices to gasoline prices: evidence from the Chinese gasoline pricing reform  
  by Ying Zheng, Mengyang Liu, Xiao-Bing Zhang 
  Abstract: As the second-largest oil consumer and the largest crude oil importer in the world, China's retail gasoline market has been increasingly linked to the crude oil market due to its retail oil pricing reform. It has been evident that gasoline price often exhibits an asymmetric response to the crude oil price change, i.e., the 'rocket and feather' effect, which states that gasoline price adjusts faster when oil price increases than when oil price decreases. This paper investigates the asymmetric pass-through of crude oil prices to gasoline prices by employing panel data covering the monthly gasoline prices of 16 cities in China, spanning from January 2004 to December 2019. The results show that the asymmetric adjustment in China's gasoline retail prices indeed exists, and it varies across different phases of China's oil pricing reform.
  Keywords: gasoline price; pass-through; energy pricing reform.
  DOI: 10.1504/IJGEI.2025.10070194
   
  

Special Issue on: Development and Application of Distributed Energy Systems in Smart Grids

• Research on intelligent charging method of electric vehicles based on virtual power plants  
  by Lianrong Pan, Jiayi Yang, Peikai Li 
  Abstract: The rising number of Electric Vehicles (EVs) requires new charging options to address grid integration and environmental concerns. Innovative charging methods are needed to reduce the burden on conventional infrastructure from electric automobiles. By applying these methods to VPPs, we can improve grid efficiency, electric car charging and energy sustainability. This research proposes the VPP-EV Charging Optimisation Framework (VPECOF) to evaluate the necessity and feasibility of an intelligent charging strategy for electric cars. A solution to the increased demand for electric vehicle charging infrastructure that meets grid stability and sustainable energy objectives is being developed using VPP technology and Smart Charging Optimisation Algorithms (SCOA). The suggested technique considers grid capacity, renewable energy availability and user preferences to improve charge schedules. This research simulates and analyses the intelligent charging strategy in the VPP framework, proving its efficacy and feasibility. The data may illuminate the benefits of synchronised electric car charging, such as peak demand reduction, grid resilience and renewable power integration. The study impacts transportation and energy regulators, utilities and stakeholders. The work improves electric car intelligent charging techniques and has consequences for their evolution.
  Keywords: electric vehicles; virtual power plant; VPP-EV charging optimisation framework; grid efficiency; renewable energy sources; smart charging optimisation algorithm.
  
  
• Design of network security risk warning for a power market monitoring system based on cloud computing technology  
  by Jinyin Peng, Xiangjin Zhu 
  Abstract: To improve the network security level of power market monitoring systems, this article optimised the structural scheme of a power market monitoring system from aspects such as physical environment, network access, and communication process according to actual application background conditions. To verify the reliability of the optimised power market monitoring system, this article conducted comparative experiments on the application of traditional power market monitoring systems and the proposed optimised power market monitoring system. Experimental verification found that the optimised power market monitoring system had a shorter system response time and faster data transmission speed compared with traditional power market monitoring systems. In the risk response assessment experiment, it showed an average improvement of 17.8% in four evaluation indicators. This article briefly discussed the problems that arise in applying traditional power market monitoring systems and proposed a solution for optimising network security based on cloud computing technology. The reliability of the optimised power market monitoring system was verified through experiments. Findings show that the optimised system achieved timely transmission and sharing of monitoring information, and improved accuracy and efficiency.
  Keywords: cloud computing; electricity market; monitoring systems; network security.
  
  

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
   
  
• 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), the output power model of the photovoltaic heating system is solved, and 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
   
  
• 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's 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
   
  
• 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., are 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
   
  
• 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 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, an evaluation index system is constructed. 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
   
  
• 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
   
  
• 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