The establishment of fuzzy relations and the fuzzification of time series are the top priorities of the model for predicting fuzzy time series. A lot of literature studied these two aspects to ameliorate the capabilit...
详细信息
The establishment of fuzzy relations and the fuzzification of time series are the top priorities of the model for predicting fuzzy time series. A lot of literature studied these two aspects to ameliorate the capability of the forecasting model. In this paper, we proposed a new method(FTSOAX) to forecast fuzzy time series derived from the improved seagull optimization algorithm(ISOA) and XGBoost. For increasing the accurateness of the forecasting model in fuzzy time series, ISOA is applied to partition the domain of discourse to get more suitable intervals. We improved the seagulloptimizationalgorithm(SOA) with the help of the Powell algorithm and a random curve action to make SOA have better convergence ability. Using XGBoost to forecast the change of fuzzy membership in order to overcome the disadvantage that fuzzy relation leads to low accuracy. We obtained daily confirmed COVID-19 cases in 7 countries as a dataset to demonstrate the performance of FTSOAX. The results show that FTSOAX is superior to other fuzzy forecasting models in the application of prediction of COVID-19 daily confirmed cases.
Aiming at the problems that the fault diagnosis accuracy of oil-immersed transformers in power systems is low and the diagnosis results are difficult to cover the entire transformer. A new fault diagnosis algorithm is...
详细信息
ISBN:
(纸本)9781665464215
Aiming at the problems that the fault diagnosis accuracy of oil-immersed transformers in power systems is low and the diagnosis results are difficult to cover the entire transformer. A new fault diagnosis algorithm is proposed. ISOA is used to improve the extraction effect of fault data features, and WNN algorithm is used to realize the fault information classification and prediction. The fault diagnosis application results show that. The performance test results of the two algorithms are excellent, and the correct rate of fault information feature extraction is 94.65%, which is better than other algorithms. Predict and analyze the failure to reduce the difficulty of maintenance by technicians. The research content will effectively solve problems such as fault diagnosis of oil-immersed transformers, and has great value for the development of power systems.
Recently, rapid growth of information technology, has progressively entered the daily life of combinations. Smart city management is the innovative technology by combining Internet of Things (IoT), cloud computing and...
详细信息
Accurate load forecasting is conducive to the formulation of the power generation plan, lays the foundation for the formulation of quotation, and provides the basis for the power management system and distribution man...
详细信息
Accurate load forecasting is conducive to the formulation of the power generation plan, lays the foundation for the formulation of quotation, and provides the basis for the power management system and distribution management system. This study aims to propose a high precision load forecasting method. The power load forecasting model, based on the improved seagull optimization algorithm, which optimizes SVM (ISOA-SVM), is constructed. First, aiming at the problem that the random selection of internal parameters of SVM will affect its performance, the improved seagull optimization algorithm (ISOA) is used to optimize its parameters. Second, to solve the slow convergence speed of the seagulloptimizationalgorithm (SOA), three strategies are proposed to improve the optimization performance and convergence accuracy of SOA, and an ISOA algorithm with better optimization performance and higher convergence accuracy is proposed. Finally, the load forecasting model based on ISOA-SVM is established by using the Mean Square Error (MSE) as the objective function. Through the example analysis, the prediction performance of the ISOA-SVM is better than the comparison models and has good prediction accuracy and effectiveness. The more accurate load forecasting can provide guidance for power generation and power consumption planning of the power system.
In this paper, an improved bio-inspired optimizationalgorithm called seagulloptimizationalgorithm (ISOA) is proposed. ISOA is developed based on three techniques, these techniques use the collaborative optimization...
详细信息
In this paper, an improved bio-inspired optimizationalgorithm called seagulloptimizationalgorithm (ISOA) is proposed. ISOA is developed based on three techniques, these techniques use the collaborative optimization strategy, simulating new bird's generation, and rearranging subgroups. The CEC2020 is utilized to obtain the role and importance of the improvedalgorithm. Furthermore, the ISOA is applied to find the optimal coordi-nation of Distance and Directional Over-Current Relays (D&DOCRs). The problem of D&DOCRs coordination is more complicated and has numerous constraints. Hence, in this work, a developed object function (DOF) is proposed to set the Distance relays with minimum available settings. ISOA and DOF are tested on the IEEE 8-bus and IEEE 14-bus test systems. ISOA algorithm is used to coordinate the D&DOCRs in both the near and far ends of transmission lines. The results of test systems prove the effectiveness of ISOA to solve the coordination of D&DOCRs using the DOF.
Air conditioning load is a crucial demand response resource for optimizing energy consumption control, and its accurate analysis provides an essential basis for achieving efficient energy management. We aim at solving...
详细信息
Air conditioning load is a crucial demand response resource for optimizing energy consumption control, and its accurate analysis provides an essential basis for achieving efficient energy management. We aim at solving the problems of scarcity, single type, low accuracy and difficult construction of high-quality data sets available for air conditioning operation characteristic models at present. This paper proposes a construction method of air conditioning operation characteristic model based on an improved seagull optimization algorithm to optimize deep belief network (ISOA-DBN). Firstly, the data set for the study of air conditioning operation characteristics is obtained through experiments. Secondly, the Restricted Boltzmann Machine (RBM) and Deep Belief Network (DBN) are used to study the operating characteristics of air conditioning. The results show that the model effect is better when DBN is used to study the operating characteristics of air conditioning, and the coefficient of determination reaches 0.9439. Then, the SOA is improved, and its performance is tested. The results show that ISOA performs better than SOA in the test of 14 standard functions. Finally, the ISOA is used to adjust the DBN parameters finely. The results show that compared with DBN and SOA-DBN, ISOA-DBN has a better model effect when used to study the operating characteristics of air conditioners, and the coefficient of determination reaches 0.9534. This can provide strong support for studying air conditioning operating characteristics under different working conditions and has broad application prospects in optimizing energy consumption control.
To address the route planning issues under the community group purchase model for joint delivery, this study thoroughly considers electric logistics vehicles with different recharging methods. The objective is to mini...
详细信息
To address the route planning issues under the community group purchase model for joint delivery, this study thoroughly considers electric logistics vehicles with different recharging methods. The objective is to minimize the sum of operating costs, recharging costs, time window penalty costs, and carbon emission costs. Separate multi-objective optimization models for route planning are constructed for both charging and battery-swapping logistics vehicles. An improved seagull optimization algorithm, guided by the golden sine strategy of the L & eacute;vy flight guidance mechanism, is employed to avoid local optima and enhance the solution efficiency. The feasibility of the models and the algorithm is verified through simulation examples. Experimental results show that, at the current stage, battery-swapping logistics vehicles display significant advantages over charging electric logistics vehicles. Although battery-swapping logistics vehicles extend delivery time, they can reduce the total delivery costs to a certain extent. Therefore, the future development prospects of battery-swapping logistics vehicles will be even broader.
To improve the prediction accuracy of ammonia nitrogen in water monitoring networks, the combination of a bio-inspired algorithm and back propagation neural network (BPNN) has often been deployed. However, due to the ...
详细信息
To improve the prediction accuracy of ammonia nitrogen in water monitoring networks, the combination of a bio-inspired algorithm and back propagation neural network (BPNN) has often been deployed. However, due to the limitations of the bio-inspired algorithm, it would also fall into the local optimal. In this paper, the seagulloptimizationalgorithm (SOA) was used to optimize the structure of BPNN to obtain a better prediction model. Then, an improved SOA (ISOA) was proposed, and the common functional validation method was used to verify its optimization performance. Finally, the ISOA was applied to improve BPNN, which is known as the improved seagull optimization algorithm-back propagation (ISOA-BP) model. The simulation results showed that the prediction accuracy of ammonia nitrogen was greatly improved and the proposed model can be better applied to the prediction of complex water quality parameters in water monitoring networks.
The high mortality rate associated with brain tumors requires early detection in the early stages to treat and reduce mortality. Due to the complexity of brain tissue, manual diagnosis of the brain and tumor tissues i...
详细信息
The high mortality rate associated with brain tumors requires early detection in the early stages to treat and reduce mortality. Due to the complexity of brain tissue, manual diagnosis of the brain and tumor tissues is very time-consuming and operator dependent. Furthermore, there is a need for experts who can review the images to detect these effects, rendering traditional methods inefficient in their presence. Therefore, the use of automated procedures for the careful examination of tumors can prove useful. In this study, a new metaheuristic-based system is presented for the early detection of brain tumors. The proposed method implements three main steps, namely tumor segmentation, feature extraction, and classification based on a deep belief network. An improved version of the seagulloptimizationalgorithm is adopted for optimal selection of the features and classification of the images. The simulation results of the proposed method are compared with a few existing methods. The final results demonstrate that the proposed method exhibits superior performance in terms of the CDR, FAR, and FRR indices compared with the other methods.
暂无评论