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International Journal of Global Energy Issues
Journal Prestige (SJR): 0.199
Number of Followers: 8  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0954-7118 - ISSN (Online) 1741-5128
Published by Inderscience Publishers Homepage  [439 journals]
  • Green building energy consumption data detection method based on Naive
           Bayesian algorithm

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      Authors: Vijayakumar Peroumal, Sujan Krishna, Harivamsi Reddy, Polineni Ramakrishna, M. Jagannath
      Pages: 379 - 395
      Abstract: In order to overcome the accuracy of energy consumption data acquisition and the low timeliness of the detection process existing in the traditional detection methods, a green building energy consumption data detection method based on Naive Bayes algorithm is designed in this paper. After collecting energy consumption data, cluster processing is carried out. Then, on the basis of the analysis of Bayesian basic principle, the Naive Bayesian classification model was designed based on the fast clustering calculation results, and the green building energy usage was analysed, and the energy consumption data quota index was designed, and the energy consumption data verification was completed by combining the Naive Bayesian classification model. The experimental results show that the building energy consumption load collected by this method is closer to the actual energy consumption load, and the detection process takes less than 3.5 min, which fully demonstrates the effectiveness of this method.
      Keywords: Naive Bayes algorithm; building energy consumption data; data detection; energy consumption quota; data clustering
      Citation: International Journal of Global Energy Issues, Vol. 44, No. 5/6 (2022) pp. 379 - 395
      PubDate: 2022-09-08T23:20:50-05:00
      DOI: 10.1504/IJGEI.2022.125408
      Issue No: Vol. 44, No. 5/6 (2022)
       
  • Energy consumption monitoring model of green energy-saving building based
           on fuzzy neural network

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      Authors: Vijayakumar Peroumal, Sujan Krishna, Harivamsi Reddy, Polineni Ramakrishna, M. Jagannath
      Pages: 396 - 412
      Abstract: In order to overcome the problems of the traditional model, such as large monitoring data error and poor energy consumption control effect, the energy consumption monitoring model of green energy-saving building based on fuzzy neural network is designed. According to the data time series, the building energy consumption interval is calculated and the energy consumption load data is obtained. The actual energy consumption equipment parameters and energy consumption calculation results are taken as the input of the model, and the input parameters are optimised by using fuzzy neural network. The energy consumption monitoring model is constructed by using the optimised parameters, and the model is modified by using the correction coefficient to output the energy consumption monitoring results. The experimental results show that the monitoring error of the model is less than 0.7%, the energy consumption control effect is good and the building energy saving is high.
      Keywords: load interval; green energy-saving building; monitoring model; energy control
      Citation: International Journal of Global Energy Issues, Vol. 44, No. 5/6 (2022) pp. 396 - 412
      PubDate: 2022-09-08T23:20:50-05:00
      DOI: 10.1504/IJGEI.2022.125405
      Issue No: Vol. 44, No. 5/6 (2022)
       
  • Multi-objective optimisation method of electric vehicle charging station
           based on non-dominated sorting genetic algorithm

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      Authors: Jia Liu, Jin Huang, Jinzhi Hu
      Pages: 413 - 426
      Abstract: There are some problems in the existing objective optimisation planning methods of electric vehicle charging station, such as low accuracy and long optimisation time. By calculating the input cost, combined closure flow and minimum node voltage of the charging station through the objective function, the optimisation objective was determined. According to the determined optimisation objective, the multi-objective comprehensive planning model of the electric vehicle charging station is constructed. After the initial solution setting, coding, decoding and other iterative operations, the multi-objective comprehensive planning model of the electric vehicle charging station is solved and the optimisation result is obtained. The multi-objective optimisation of electric vehicle charging station is realised. The results show that the highest accuracy is about 95%.
      Keywords: non-dominated sorting genetic algorithm; electric vehicle charging station; multi-objective optimisation; decoding
      Citation: International Journal of Global Energy Issues, Vol. 44, No. 5/6 (2022) pp. 413 - 426
      PubDate: 2022-09-08T23:20:50-05:00
      DOI: 10.1504/IJGEI.2022.125413
      Issue No: Vol. 44, No. 5/6 (2022)
       
  • Power load monitoring method of high-power electrical appliances based
           on energy compensation

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      Authors: Haitong Gu, Zhuo Cui, Kaiyan Chen, Zhengyang Peng, Shitao Chen
      Pages: 427 - 439
      Abstract: In order to overcome the problems of low monitoring accuracy and long monitoring time existing in traditional power load monitoring methods, a new power load monitoring method based on energy compensation is proposed. The basic framework of high-power electrical load monitoring method is given. According to Fourier series, the active power of high-power electrical equipment during operation is obtained, and the current waveforms under various operation states are obtained. The sliding double-sided window cumulative calculation method is used to determine the load switching event data. At the same time, the characteristic indexes of power load curve are extracted by similarity measurement index. In feature extraction based on the result using the energy to complete the electrical load monitoring compensation method. The research results show that the proposed method has higher monitoring accuracy and shorter monitoring time, the shortest time is 0.02 s.
      Keywords: energy compensation; high-power appliances; electrical load; active power; switching events
      Citation: International Journal of Global Energy Issues, Vol. 44, No. 5/6 (2022) pp. 427 - 439
      PubDate: 2022-09-08T23:20:50-05:00
      DOI: 10.1504/IJGEI.2022.125414
      Issue No: Vol. 44, No. 5/6 (2022)
       
  • Load identification method of household smart meter based on decision tree
           algorithm

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      Authors: Shaoqing Shi, Zhuo Xu, Yong Xiao
      Pages: 440 - 453
      Abstract: In order to ensure the safe and economic operation of power grid, a load identification method of household smart meters based on decision tree algorithm is proposed. This paper pre-processes the missing data, noise data and inconsistent data in the load data of household smart meter, and uses the decision tree algorithm to predict the load data after pre-processing. According to the prediction results, combined with mathematical tools, from the PQ characteristics, current characteristics, V-I characteristics The load characteristics of household smart meters are extracted from the characteristics, harmonic characteristics and instantaneous characteristics and the objective function of load identification is constructed based on the combination of characteristics, so as to realise the load identification of household smart meters based on decision tree algorithm. Comparative results show that this method can reduce the error rate of load, to improve the efficiency of identification, identifying the shortest time of only 1.5 s.
      Keywords: decision tree; household smart meter; load data; feature combination
      Citation: International Journal of Global Energy Issues, Vol. 44, No. 5/6 (2022) pp. 440 - 453
      PubDate: 2022-09-08T23:20:50-05:00
      DOI: 10.1504/IJGEI.2022.125409
      Issue No: Vol. 44, No. 5/6 (2022)
       
  • Optimisation of frequency response parameters of new energy distribution
           network based on linear correction

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      Authors: Xin Mao, Yufan Rao, Min Gong, Xuemin Song, Lixing Zhou
      Pages: 454 - 470
      Abstract: In order to overcome the problems of high response adjustment rate and low optimisation efficiency existing in the existing frequency response parameter optimisation methods of distribution network, a new optimisation method for frequency response parameter of new energy distribution network based on linear correction is proposed. Firstly, the frequency response demand of new energy distribution network is analysed, and the frequency regulation index of PV and wind power is obtained. Combined with linear correction algorithm, the frequency response parameter model of new energy distribution network is constructed. The fuzzy control theory and genetic algorithm are combined to solve the model to effectively optimise the frequency response parameters of new energy distribution network. Simulation results show that the proposed method cannot only effectively reduce the frequency response adjustment rate of distribution network, but also effectively reduce the optimisation time and cost.
      Keywords: linear correction; new energy distribution network; frequency response parameters; fuzzy control theory; genetic algorithm
      Citation: International Journal of Global Energy Issues, Vol. 44, No. 5/6 (2022) pp. 454 - 470
      PubDate: 2022-09-08T23:20:50-05:00
      DOI: 10.1504/IJGEI.2022.125404
      Issue No: Vol. 44, No. 5/6 (2022)
       
  • Research on energy consumption parameter optimisation of green building
           based on single and double-layer hybrid optimisation

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      Authors: Xin Mao, Yufan Rao, Min Gong, Xuemin Song, Lixing Zhou
      Pages: 471 - 483
      Abstract: In order to solve the problems of poor extraction accuracy of energy consumption parameters existing in traditional optimisation methods, an optimisation method of green building energy consumption parameters based on single and double-layer hybrid optimisation was proposed. According to the optimisation principle of green building energy consumption parameters, the green building envelope energy consumption parameters, window energy consumption parameters and heating energy consumption parameters are determined. The objective function of green building energy consumption parameter optimisation was established by single and double-layer hybrid optimisation method. The energy consumption parameters were input into the function to obtain the optimal solution of each parameter and complete the parameter optimisation. The experimental results show that the minimum value of the sample building energy consumption parameters optimised by the method in this paper is about 1.9 J, and the maximum extraction accuracy of the sample building energy consumption parameters is about 97%.
      Keywords: single and double-layer hybrid optimisation method; green building; energy consumption parameters; parameter optimisation
      Citation: International Journal of Global Energy Issues, Vol. 44, No. 5/6 (2022) pp. 471 - 483
      PubDate: 2022-09-08T23:20:50-05:00
      DOI: 10.1504/IJGEI.2022.125410
      Issue No: Vol. 44, No. 5/6 (2022)
       
  • Energy consumption prediction of new energy vehicles in smart city based
           on LSTM network

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      Authors: Shulong Wu, Fengjun Wang, Maosong Wan
      Pages: 484 - 497
      Abstract: In order to overcome the traditional problems such as large prediction error and long prediction time, this paper proposes a new energy consumption prediction method of smart city new energy vehicles based on LSTM network. By analysing the energy operation process of new energy vehicles in smart city, the energy consumption prediction parameters such as vehicle battery energy, resistance energy consumption, rolling resistance and air resistance are determined. On this basis, the energy consumption prediction model of new energy vehicles is constructed, and the LSTM network is used to solve the energy consumption prediction model of new energy vehicles, and the energy consumption prediction results are obtained. Experimental results show that the prediction error of the proposed method is always less than 2%, and when the number of iterations is 50, the prediction time of the proposed method is only about 0.95 s, which is relatively short.
      Keywords: LSTM network; smart city; new energy vehicle; prediction parameters; prediction model
      Citation: International Journal of Global Energy Issues, Vol. 44, No. 5/6 (2022) pp. 484 - 497
      PubDate: 2022-09-08T23:20:50-05:00
      DOI: 10.1504/IJGEI.2022.125412
      Issue No: Vol. 44, No. 5/6 (2022)
       
  • Energy consumption parameter detection of green energy saving building
           based on artificial fish swarm algorithm

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      Authors: Lijun Yin, Haoran Yin
      Pages: 498 - 510
      Abstract: In order to overcome the low-detection accuracy of traditional methods, an artificial fish swarm algorithm was proposed to detect the energy consumption parameters of green and energy-saving buildings. The type of energy consumption equipment in green and energy-saving buildings is analysed, and the electricity consumption of building energy consumption equipment is taken as the building energy consumption parameter. The hierarchical clustering method was used to establish the classification model of energy consumption parameters, and the energy consumption parameters were classified and processed, and the energy consumption parameters detection model was built, and the preliminary detection results of energy consumption parameters were obtained. The artificial fish swarm algorithm was used to construct the optimisation function of building parameter detection results to obtain the optimal detection results of energy consumption parameters. Experimental results show that the accuracy of the proposed method is between 92.76% and 98.75%, and the practical application effect is good.
      Keywords: artificial fish swarm algorithm; green energy saving building; energy consumption parameters; hierarchical clustering
      Citation: International Journal of Global Energy Issues, Vol. 44, No. 5/6 (2022) pp. 498 - 510
      PubDate: 2022-09-08T23:20:50-05:00
      DOI: 10.1504/IJGEI.2022.125411
      Issue No: Vol. 44, No. 5/6 (2022)
       
  • A cross-regional joint operation control method of small power
           photovoltaic power grid and municipal power grid

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      Authors: Lijun Yin, Haoran Yin
      Pages: 511 - 523
      Abstract: Aiming at the problems of high-failure rate of transmission lines and large fluctuation of output waveform in traditional methods, a control method for cross-regional joint operation of low-power photovoltaic grid and municipal grid based on fuzzy logic control is designed. This method combines the overall topological structure of the power supply system and installs a protection circuit to reduce electricity costs. In the entire combined power supply system, the fuzzy logic control method is used to obtain the distance distribution function of the fault nodes in different areas of the power grid, and the effective control of the power grid is realised by adjusting the grid deviation coefficient. The results show that the failure rate of the transmission line is reduced from 6 to 3%, the output waveform fluctuates steadily and the grid operation status is relatively stable, which guarantees the safe operation of the grid.
      Keywords: small power photovoltaic grid; municipal grid; joint operation control; fuzzy logic control; distance distribution function
      Citation: International Journal of Global Energy Issues, Vol. 44, No. 5/6 (2022) pp. 511 - 523
      PubDate: 2022-09-08T23:20:50-05:00
      DOI: 10.1504/IJGEI.2022.125407
      Issue No: Vol. 44, No. 5/6 (2022)
       
  • Energy consumption prediction method of energy saving building based on
           deep reinforcement learning

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      Authors: Chuan He, Ying Xiong, Yeda Lin, Lie Yu, Hui-Hua Xiong
      Pages: 524 - 536
      Abstract: In order to overcome the problems of low-prediction accuracy and long prediction time of traditional building energy consumption prediction methods, this paper proposes a new energy-saving building energy consumption prediction method based on deep reinforcement learning. Through the deep reinforcement learning algorithm, a number of energy consumption behaviour return information of specific value network and strategy network are calculated, respectively to build the energy consumption probability model of energy-saving building energy consumption equipment. The linear rectification function with leakage is used to update the probability model and parameters, and the linear relationship prediction function of energy consumption parameters is constructed by using the learning process and results to complete the dynamic prediction of energy consumption of energy-saving buildings. The experimental results show that the proposed method has fast prediction speed and high accuracy, which can provide reference for the implementation of energy-saving building.
      Keywords: deep intensive learning; energy saving building; energy consumption forecasting; time series; multivariate linear regression
      Citation: International Journal of Global Energy Issues, Vol. 44, No. 5/6 (2022) pp. 524 - 536
      PubDate: 2022-09-08T23:20:50-05:00
      DOI: 10.1504/IJGEI.2022.125406
      Issue No: Vol. 44, No. 5/6 (2022)
       
 
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