International Journal of Global Energy Issues (27 papers in press)

Regular Issues

• An analysis of market power in Iran’s electricity market with machine learning  
  by Naser M. Rostamnia 
  Abstract: The Iranian electricity market was reformed over the last three decades primarily to promote competition and improve its production efficiency. This paper provides an analysis of competition in the Iranian electricity market. Although other works have provided similar assessments, none has provided a thorough probe over a long period. This paper analyses the Herfindahl Hirschman Index (HHI) of the market for the last decade which has not been done. Also, the paper forecasts the index in the market for the next year to project its direction. Long Short-Term Memory (LSTM) was implemented to forecast the indices in an efficient computational time. Grid search is used to select the optimal model for forecasting and interactions analyses provide insights into the parameter options that lead to significantly improved accuracies. The results show that the market was unconcentrated from 2012 to 2021. Also, the forecasts show that the market will remain unconcentrated for the next year. Furthermore, the analysis shows that the entrance of new powerplants into the market could reduce the concentration in the market.
  Keywords: market power analysis; Herfindahl Hirschman index; long short-term memory algorithm; hyperparameter optimisation; grid search.
  DOI: 10.1504/IJGEI.2023.10054134
   
  

Special Issue on: Management and Technology for Energy Efficiency Development

• An energy consumption prediction of large public buildings based on data-driven model  
  by Yongbing Guan, Yebo Fang 
  Abstract: Because the traditional energy consumption prediction method of large public buildings has the problems of large prediction error and long prediction time, an energy consumption prediction of large public buildings based on data-driven model is proposed. The process includes to build the energy consumption model of large public buildings through data driving, collect energy consumption data such as equipment capacity, load grade and equipment failure rate, pre-process the energy consumption data, take the pre-processed energy consumption data as training samples, input it into BP neural network for training, optimise BP neural network by genetic algorithm and build the energy consumption prediction model of large public buildings, and get the prediction results. The simulation results show that the energy consumption prediction method of large public buildings based on data-driven model has short time and good prediction effect.
  Keywords: data-driven model; large public buildings; energy consumption prediction; BP neural network; genetic algorithm.
  DOI: 10.1504/IJGEI.2023.10054133
   
  
• Short-term forecasting method for lighting energy consumption of large buildings based on time series analysis  
  by Yanpeng Li, Guofeng Zhang 
  Abstract: In order to overcome the problems of high data noise, low prediction accuracy and long prediction time in the traditional short-term prediction method of lighting energy consumption of large buildings, a short-term prediction method of lighting energy consumption of large buildings based on time series analysis is proposed in this paper. The improved threshold function is used to denoise the data, and the fuzzy c-means clustering algorithm is used to cluster the denoised data. The time series analysis method is used to construct the self-excitation threshold autoregressive model. When the model parameters are optimal, the clustered data are input into the model to output the short-term prediction results of lighting energy consumption of large buildings. The experimental results show that compared with the traditional method, the average data noise of this method is 12.3 dB, the prediction accuracy remains above 94% and the average prediction time is only 57 ms.
  Keywords: time series analysis; large buildings; lighting energy consumption; short-term forecast; fuzzy c-means clustering algorithm; self-excitation threshold autoregressive model; particle swarm optimisation algorithm.
  DOI: 10.1504/IJGEI.2023.10053422
   
  
• An energy market demand prediction based on grey BP-NN optimal combination  
  by Lei Zhang 
  Abstract: Aiming at the problems of low-prediction accuracy and long prediction time in traditional energy market demand prediction methods, an energy market demand prediction method based on grey BP-NN optimisation combination is proposed. The influencing factors of energy market demand are analysed through economic growth factors, energy price factors, industrial structure factors, population and urbanisation factors and environmental policy factors, the analysis sequence is determined, the analysis matrix is constructed, the grey correlation degree of each influencing factor is calculated and the mean value is standardised. According to the processing results, the energy market demand prediction model of grey theory is constructed. Taking the prediction results of grey model and factors affecting energy demand as the input of BP neural network, an improved BP neural network structure is constructed and the prediction results are output. The simulation results show that the proposed method has high accuracy and short prediction time.
  Keywords: BP neural network; grey theory; energy market; demand prediction; grey correlation degree; mean normalisation.
  DOI: 10.1504/IJGEI.2023.10054129
   
  
• Load random access method of intelligent charging pile based on distributed energy  
  by Hui Cao 
  Abstract: In order to improve the stability and convergence of random-access process of charging pile load, a load random access method of intelligent charging pile based on distributed energy is designed in this paper. Firstly, the distributed energy dispatching control process is designed, then the load balancing scheduling algorithm is designed according to the charging pile demand, and the random load access is realised by planning the charging pile load boundary. Experimental results show that the load rate of each line in the power grid with charging piles is relatively balanced, and the power load output value can be stable at 36.5 kW after the application of the proposed method. Moreover, the loss function value of the proposed method gradually tends to be stable after the 100th iteration, and its value is 0.03, indicating that the proposed method has a faster convergence rate and better convergence.
  Keywords: charging pile; distributed energy; load balancing dispatching; load random access.
  DOI: 10.1504/IJGEI.2023.10054130
   
  
• An emission reduction prediction method of green building engineering based on time weighting  
  by Hui-Hua Xiong, Ming Luo 
  Abstract: Aiming at the problems of low accuracy of emission reduction prediction and long time-consuming emission reduction prediction methods of existing green building engineering emission reduction prediction methods, the paper proposes a new time-weighted emission reduction prediction method for green building engineering. First, construct the objective function of the time change of green building emission reduction, and use time weighting to calculate the weight of green building engineering emission reduction forecast. Secondly, the grey model is used to obtain the fitted sequence of emission reductions of green building projects. Finally, the Markov Chain is used to construct the emission reduction prediction function, and the output result of the function is the prediction result. The results of the simulation study show that the prediction accuracy of emission reductions of the method in this paper is maintained above 95%, and the time cost is effectively reduced.
  Keywords: time weighting; green building engineering; emission reduction prediction.
  DOI: 10.1504/IJGEI.2023.10054131
   
  
• Research on emission reduction potential prediction method under green building planning based on multi-factor analysis  
  by Huanan Liang, Zhibin Xu 
  Abstract: In order to overcome the problem of large prediction error of traditional methods, a prediction method of green building emission reduction potential based on multi-factor analysis is proposed. The capacity forecast is divided into four stages: building materials transportation, construction, operation and maintenance, and demolition and recycling. The energy consumption of construction equipment is calculated by the rated estimation method, the carbon emission in the operation, maintenance and demolition and recycling stage is calculated and the weight of emission reduction potential index is calculated by the deviation maximisation method. Build a carbon emission prediction model and substitute the above results into the model to realise the multi-factor analysis of emission reduction potential prediction. The experimental results show that the prediction error of emission reduction potential of this method is only 1.032%, which shows that the prediction effect of emission reduction potential of this method is good.
  Keywords: green building planning; emission reduction potential; prediction; multiple factors; weight; correlation.
  DOI: 10.1504/IJGEI.2023.10054132
   
  
• Numerical study on the effect of collector height on the performance of solar water heater collector  
  by Ammar Ali, Abinash Mahapatro, Binayak Pattanayak, Adel Abdalrahman, Fadi Ali 
  Abstract: The current research aims to perform a numerical study on the effect of the collector height on the feasibility and effectiveness of a flat plate solar water heater (SWH) integrated with baffles. Numerical simulation is carried out using Ansys Workbench (CFD fluent), where the domain of study consists of water as fluid and metal collector (aluminium) as solid. Transverse baffles channel the water flow in the collector to ensure a proper flow distribution across the collector. Two height variations (H) viz. 3 cm and 5 cm are considered for the current investigation. The results show that reducing the height of the solar water heater leads to an increase in the speed of water flow in the collector. Nevertheless, the temperature of the water is increased by reducing the collector height due to the reduction of total volume and mass of water available in the collector. Moreover, the higher outlet temperature of the water corresponds to the lower collector height. Thus, the height of the solar water heater has a significant economic effect correlated to the performance and cost of the collector. The authors thereby emphasise that the height and the associated outlet temperature are reference parameters to evaluate the feasibility and effectiveness of the solar water collector.
  Keywords: CFD; solar water heater; flat plate collector; collector height; baffles.
  DOI: 10.1504/IJGEI.2022.10050567
   
  
• The Paris Agreement's impact on the green bonds market  
  by Hatem Rjiba, Federica Salvadé, Ryan Tolliver, Juan Ignacio Torriconi 
  Abstract: Green bonds are a relatively new instrument in financial markets, and are considered one of the best tools available to finance green projects and help reduce greenhouse gas emissions. This paper investigates the effects of the Paris Climate Agreement on the green bond market. We focus on US municipal, corporate and government green bonds. We document that the number of such bonds issued have significantly increased immediately after the agreement was adopted, suggesting that the adoption of the Paris Agreement has played a significant role in the development of the green bond market.
  Keywords: Paris Agreement; green bonds; green house gas emissions.
  DOI: 10.1504/IJGEI.2023.10053676
   
  

Special Issue on: Challenges and Sustainable in Energy

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

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

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

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

• Optimisation of computer network reliability based upon sensor technology and genetic algorithm  
  by Chengqing Gong 
  Abstract: In todays society, there has been a trend towards high digitisation, which means that the importance of computer networks is also increasing. Computer network reliability is the concept of measuring the security and stability of computer network system based on its importance. Nowadays, network reliability detection systems at home and abroad have their own standards. It is not conducive to the measurement between different system networks. This article is dedicated to optimising the reliability of the computer network, making it more convenient, quick and easy to operate. For this reason, this paper proposes a network reliability analysis algorithm based on sensor technology, and then uses genetic algorithm to optimise it. The proposed optimisation algorithm is to solve the problems of high algorithm complexity and low-computational efficiency. In the experiment, the original algorithm was compared with the optimised algorithm. A number of tests have also been conducted for network reliability analysis. Experimental results show that the optimised algorithm can increase the accuracy rate to more than 90%, and can recall a high percentage of correct matching pairs. The average time overhead is also around 300 ms.
  Keywords: sensor technology; genetic algorithm; computer network; reliability optimisation.
  DOI: 10.1504/IJGEI.2022.10052597
   
  
• Comparison between PID and PSO-PID controllers in analysing the load frequency control in interconnected microgrids in a deregulated environment  
  by Ranjit Singh, L. Ramesh 
  Abstract: This paper focuses on analysing the frequency error in interconnected microgrids and reducing the generation cost, which is considered one of the objective functions. The Simulink model shows the connection between 2microgrids, i.e., microgrid 1 comprises thermal, hydro and gas power plants, whereas microgrid 2 comprises thermal, nuclear and gas power plants. The change in the tie-line power is also considered while simulating the model. The papers main aim is to reduce the variations in frequency in each microgrid to ensure the steady flow of power among the connected microgrids along with the tie-line power. Also, the robustness of PID and PSO-PID Controllers are compared and analysed. The Particle swarm optimisation Algorithm codes tune the controllers gains in MATLAB. The model is simulated using MATLAB 2014b, and necessary graphs are obtained, which show the frequency error reduction time in both the microgrids.
  Keywords: MGs; microgrids; frequency error; LFC; load frequency control; tie line power; PID proportional integral derivative; controller; PSO; particle swarm optimisation.
  DOI: 10.1504/IJGEI.2023.10053492
   
  
• Design of remote data quantum system for geological oil extraction based on ARM and GPRS  
  by Hong Guo, Keping Zhai, Shien Liu, Xiuhua Shan 
  Abstract: With the rapid development of industrial automation, the control accuracy in industrial production has gradually improved and the control process has become more and more complex. Since then, the requirements for the security and real-time performance of the data collected on site have also been continuously improved, and high-quality, strong real-time data has played an important role in the entire control process. This article aims to study the design of a remote data quantum system for geological oil extraction based on ARM and GPRS, and put forward some related methods about quantum information and GPRS technology. In addition, experiments were conducted on a quantum system based on ARM and GPRS for remote data extraction of geological oil. The experimental results of this paper show that the remote data quantum system for geological oil extraction based on ARM and GPRS can detect the flow information of oil and water in the process of oil production in real-time. Moreover, it has played a great protective role in the remote transmission of data, and the security protection of data has been improved by 10%.
  Keywords: GPRS mobile communication; Modbus protocol; geological oil extraction; quantum information; quantum system design; remote data collection.
  DOI: 10.1504/IJGEI.2023.10054747
   
  
• Distributed energy system based on comprehensive utilisation of solar energy and biomass energy  
  by Dexia Kong, Zhiqiang Yao, Yihao Duan, Yulei Zhao 
  Abstract: Traditional energy is mostly non-renewable energy. The main advantage of distributed energy system is to use it in cogeneration of cooling, heating and power. Distributed energy takes into account the two factors of energy saving and environmental protection. Breaking the traditional single power supply, heating or cooling method has become the best way to adjust the energy structure, and has broad market application prospects. In order to understand the utilisation of solar energy and bio-intelligence, this paper collects a large number of scattered energy data information on site in real time, uses high-speed communication network to transmit data, relies on realtime database technology to process the measured data, and uses visual programming configuration tools to quickly, accurately and reliably obtain energy management information. The dimensional division and system configuration of energy management are classified in detail, and the design and development of system functions such as data collection and storage, data statistics and analysis, status monitoring and alarms have been completed. The results of the study found that, based on solar energy and biomass, the cooling time of internal combustion engines and gas turbines is basically the same, but the energy consumed by internal combustion engines is about 10% higher than that of gas turbines.
  Keywords: solar energy; biological intelligence; comprehensive utilisation; distributed energy system.
  DOI: 10.1504/IJGEI.2023.10054750
   
  
• Voiceprint recognition and cloud computing data network security based on scheduling joint optimisation algorithm  
  by Yinhui Ma 
  Abstract: Cloud computing is an upcoming revolution in the information technology industry due to its performance, accessibility, low cost, and many other luxury items. This is a way to maximise capacity without investing in new infrastructure, training new personnel, or licensing new software, for it provides customers with huge data storage and faster calculation speed through the Internet. With the popularity of cloud computing, users store and share confidential data in the cloud, and this approach makes data security an important and difficult issue. In order to ensure data security, cloud service providers must provide efficient and feasible mechanisms to provide reliable encryption methods and appropriate access control systems. This paper takes this as the main research content, focusing on the resource scheduling algorithm and its performance optimisation, voiceprint recognition technology and its optimisation, and the joint optimisation scheduling algorithm for the cloud data network security centre. The research proves that the performance of the voiceprint recognition and cloud computing data network system based on the genetic quantum particle optimisation joint scheduling algorithm proposed in this paper has been improved. It takes the systems network convergence speed as an index, and when the path scheme reuse rate is 30%, the network convergence speed is the fastest, and the convergence time is only 0.72 s.
  Keywords: scheduling joint optimisation algorithm; voiceprint recognition; cloud computing; data network security.
  DOI: 10.1504/IJGEI.2023.10054938
   
  

Special Issue on: Smart Energy Infrastructures for Smart Cities

• Cloud computing load balancing based on improved genetic algorithm  
  by Fengxia Zhu 
  Abstract: In the cloud computing environment, when most users request services, how to quickly and reasonably allocate a large number of tasks to a single virtual resource node and achieve parallelism is one of the research topics of current researchers. The key to this method in load balancing technology is load programming, whose quality directly affects the performance of the equalisation system. Therefore, this paper starts with distributed cloud computing technology and virtualisation technology, reveals the concept and method of load balancing implementation, and proposes an improved genetic load balancing algorithm. Traditional genetic algorithms can be used as meta-heuristic algorithms with slow convergence problems. We used the Cloudsim open source cloud simulation platform for simulation. The results show that compared with the traditional genetic algorithm, the improved genetic algorithm can better adapt to the load balancing requirements in the cloud computing environment and improve the balance and efficiency of resource utilisation.
  Keywords: improved genetic algorithm; cloud computing; load balancing; virtualisation technology.
  DOI: 10.1504/IJGEI.2023.10054590
   
  
• Secure application-centric service authentication with regression learning for security systems in smart city applications  
  by Pei Yang, Guoqiang You 
  Abstract: Smart city applications rely on different security paradigms for meeting the user demands and authenticated service disseminations. Diverse applications require different security modifications for improving the smart city contract-level application support. The challenging task is security adaptability and its improvements for smart city scenarios. In this article, a Secure Application-Centric Service Authentication (SACSA) is introduced for leveraging end-to-end authentication. This scheme introduces group key-based authentication for securing services in an end-to-end manner. The proposed scheme administers security using batch keys to improve the sharing efficiency of different services. The security and service time rely on the application type and distinct intervals, providing less complex and time-consuming security. In this process, blockchain is applied to perform the grouping, key generation and authentication recommendation in collaboration with the regression learning. Through this learning, batch consecutiveness is identified for improving application security. In the proposed scheme, authentication and key generation are performed using the Merkle Hash tree to prevent replication and decrease distribution. The proposed schemes performance is analysed using the metrics authentication time, complexity, service failure, and service latency. Thus, the SACSA system maintains system security with minimum authentication time, complexity, service failure, and latency of 9.45%, 7.75%, 9.2%, and 9.39%, respectively.
  Keywords: blockchain; group key; IoT; Merkle hash.
  DOI: 10.1504/IJGEI.2023.10055115
   
  

Special Issue on: Strategic Planning and Management in Energy

• Optimal renewable distributed generation planning: an up-to-date state-of-the-art review  
  by Ali Tarraq, Faissal El Mariami, Abdelaziz Belfqih, Touria Haidi 
  Abstract: Due to its multiple benefits, the integration of renewable-based distributed generation (RDG) into the distribution network (DN), is of great importance. Yet, given the complexity of this network, optimal distributed generation planning (ORDGP) is considered a complex combinatorial problem, which remains a real challenge for investigators, decision-makers, and investors. This issue involves finding the optimal locations, sizes, power factors, and number of RDGs to be incorporated into the DN to improve the networks overall efficiency while meeting a set of voltage, current, and power constraints. Moreover, the incorporation of uncertainties related to this type of source, especially wind turbine or PV system, decreases the ability of classical and analytical methods to solve the ORDGP problem. For this reason, meta-heuristic and hybrid methods have become very promising essentially because of their randomness. In this context, a systematic and comprehensive literature review on the different definitions, classifications, and interests of RDG, as well as on the different optimisation techniques, represents the main objective of this study. Concisely, this paper provides in-depth knowledge and serves as a useful guide for future researchers and investors in the ORDGP.
  Keywords: renewable distributed generation; optimal renewable DG planning; distribution network; optimisation methods.
  DOI: 10.1504/IJGEI.2023.10054664
   
  
• A low carbon treatment technology of green building construction waste based on genetic algorithm  
  by Jinhua Kang, Guanglei Zhao 
  Abstract: In this paper, a low-carbon treatment method of green construction waste based on genetic algorithm is proposed in this paper. Firstly, the group organisation method is used to search in the search solution space to calculate the carbon emission of green building construction waste. According to the calculation method of carbon emissions, the product of activity data and emissions is obtained. Then, through the model objectives and constraints, a single cycle multi-objective mathematical model with waste treatment cost and low-carbon emission as the objective function is established. Finally, the genetic algorithm is used to solve the model to realise the low-carbon treatment of green building construction waste. The experimental results show that the proposed low-carbon treatment time of green building waste is only 16.7 s and the carbon emission is only 0.13 g, which can effectively improve the lowcarbon treatment efficiency of green building waste
  Keywords: genetic algorithm; emission factor method; green building; construction waste; low-carbon treatment.
  DOI: 10.1504/IJGEI.2023.10055012
   
  
• Intelligent forecasting method of distributed energy load based on least squares support vector machine  
  by Yingwei Chen, Zhikui Chang 
  Abstract: Aiming at the problems of long prediction time and low prediction accuracy of traditional distributed energy load intelligent prediction methods, a distributed energy load intelligent prediction method based on least squares support vector machine is proposed. The method of linear interpolation is used to process the missing load data of distributed energy, and the wrong load data of distributed energy are processed horizontally and vertically. On this basis, the t-test standard in probability and statistics method is used to identify the abnormal load of distributed energy. Using least squares support vector machine, a distributed energy load forecasting model is constructed to realise the intelligent forecasting of distributed energy load. The experimental results show that the average MAPE and RMSE of the proposed method are 1.008% and 1048 respectively, and the time of distributed energy load forecasting is 15.8 s. The proposed method can effectively improve the accuracy and efficiency of distributed energy load forecasting.
  Keywords: least squares support vector machine; linear interpolation; t-test criterion; distributed energy; load forecasting.
  DOI: 10.1504/IJGEI.2023.10055013
   
  
• Study on evaluation method of energy saving potential of green buildings based on entropy weight method  
  by Wei Yuan, Zhigang Liu 
  Abstract: For green buildings, the effect of energy-saving potential evaluation using the current method is poor and the evaluation efficiency is low. This paper uses the entropy weight method to study the energy-saving potential evaluation method of green buildings. Firstly, the evaluation index system of energy-saving potential of green buildings is established under the principles of scientificity, systematicness and operability, and the evaluation indexes are obtained. Then the entropy weight method is used to determine the index weight and calculate the energy-saving potential index. Finally, the energy-saving potential is evaluated based on the value on the basis of analysing the economic benefits. The simulation results show that the accuracy of the proposed method for energy-saving potential evaluation is up to 100%, the evaluation time is within 5 s, the evaluation accuracy is the highest and the evaluation time is the shortest.
  Keywords: entropy weight method; economic performance; judgment matrix; G value.
  DOI: 10.1504/IJGEI.2023.10055014
   
  
• An energy efficiency evaluation method of intelligent building based on fuzzy clustering algorithm  
  by Shuai Wang, Lei Sun, Xinjie Yuan 
  Abstract: Aiming at the problems of low evaluation accuracy and long evaluation time in the traditional energy-saving evaluation methods of smart buildings, an energy-saving evaluation method of smart buildings based on fuzzy clustering algorithm is proposed. Firstly, according to the construction criteria of the evaluation index system, establish the energy-saving evaluation index system of smart buildings, obtain the evaluation indexes, then use the grey correlation theory to determine the weight of the energy-saving evaluation index of smart buildings, and measure the energy consumption of smart buildings separately. Finally, establish the fuzzy similarity matrix, and cluster with the network method in the direct clustering method to obtain the optimal clustering scheme and evaluate the energy-saving of smart buildings. The simulation results show that the accuracy of the proposed method is 100% and the evaluation time is within 7 s. The evaluation effect of the proposed method is good and the evaluation efficiency is high.
  Keywords: fuzzy clustering algorithm; grey correlation theory; smart building; energy-saving evaluation; evaluation index system.
  DOI: 10.1504/IJGEI.2023.10055015
   
  
• Investment risk prediction method of renewable energy market under the background of carbon neutralisation  
  by Chunxia Liu 
  Abstract: To improve the accuracy of market investment risk prediction and reduce the time-consuming of investment risk prediction, this paper proposes a renewable energy market investment risk prediction method under the background of carbon neutralisation. Firstly, based on the background of carbon neutrality, the earned value management theory is used to quantitatively describe the investment risk of renewable energy market. Secondly, aiming at carbon neutralisation, the system dynamics method is used to design the investment risk prediction function of renewable energy market. Finally, the residual test is used to verify the investment risk prediction results of the energy market, so as to realise the investment risk prediction of the renewable energy market. The results show that the accuracy of market investment risk prediction of this method is 96.5%, and the time of investment risk prediction is only 8.6 s. It can accurately predict the investment risk of renewable energy market.
  Keywords: carbon neutralisation; system dynamics method; renewable energy; market investment; risk prediction.
  DOI: 10.1504/IJGEI.2023.10055016
   
  
• The prediction of carbon emissions from construction land in central Yunnan urban agglomeration area based on multiple linear regression model  
  by Lede Niu, Jingzhi Lin, Lifang Zhou, Anlin Li, Yan Zhou 
  Abstract: In order to clarify the quantitative relationship between construction land changes and carbon emissions, a prediction method for carbon emissions from construction land in central Yunnan urban agglomeration area based on multiple linear regression model was proposed. Taking the central Yunnan urban agglomeration area as the study area, based on the data of construction land from 2011 to 2020, the carbon emission of construction land was predicted by using the multiple linear regression model. There is a positive correlation between the carbon emissions of the central Yunnan urban agglomeration area and the level of construction land use. From 2011 to 2020, average annual growth rate of construction land area was 8.56%, and the average annual growth rate of carbon emissions was 5.75%. The annual growth rate of carbon emissions from 2021 to 2030 is 0.97%, indicating that the governments carbon emission control measures have achieved good results.
  Keywords: multiple linear regression model; STIRPAT model; Yunnan central urban agglomeration area; construction land; carbon emission prediction.
  DOI: 10.1504/IJGEI.2023.10055017
   
  
• New energy industry investment risk assessment method based on fuzzy AHP  
  by Lianguang Mo 
  Abstract: In order to improve the accuracy, efficiency and comprehensiveness of investment risk assessment, a new energy industry investment risk assessment method based on fuzzy AHP is proposed. Firstly, the random forest algorithm is used to predict the investment risk of new energy industry. Secondly, the fuzzy AHP method is used to construct the risk assessment system, the normalisation and consistency test are used to deal with the assessment indicators, and the weight of the assessment indicators is calculated. Finally, based on the evaluation index system, a new energy industry investment risk evaluation model based on multiple regression analysis is established to realise the new energy industry investment risk evaluation. The experimental results show that the highest accuracy of the evaluation results of the proposed method is more than 80%, the evaluation efficiency is high, and the evaluation results are more comprehensive.
  Keywords: fuzzy AHP; risk assessment; random forest algorithm; cart algorithm; multiple regression analysis.
  DOI: 10.1504/IJGEI.2023.10055018
   
  
• Study on multi-step prediction method of passive energy-saving building energy consumption based on energy consumption perception  
  by Qiliang Yuan 
  Abstract: Passive energy-saving buildings have problems such as low energy consumption prediction accuracy and complex prediction process. A multi-step prediction method for energy consumption of passive energy-saving buildings based on energy consumption perception is proposed. Firstly, the related thermal parameters and building parameters of passive energy-saving buildings are calculated by steady-state calculation. Secondly, the equivalent thermal parameters of building air-conditioning load, air-conditioning load equivalent thermal parameters and air-conditioning load thermal parameters are determined by means of first-order differential equations, and the envelope structure is calculated. Finally, the ultrasonic sensor is set in the energy-saving building, and the energy consumption data of each part of the building is sensed to realise multi-step prediction. The experimental results show that the fluctuation of the predicted energy consumption value of the proposed algorithm is less than 1 J, the prediction accuracy is always above 90% and the time cost is about 1.54 s.
  Keywords: energy consumption perception; passive energy-saving building; multi-step prediction of energy consumption; dynamic calculation; load equivalent thermal parameters.
  DOI: 10.1504/IJGEI.2023.10055019