Hybrid reactive power compensation(HRPC)combines step-controlled shunt reactors and series compensation,and will be employed in ultra-high-voltage(UHV)power systems.The single-phase auto-reclosure characteristics of s...Hybrid reactive power compensation(HRPC)combines step-controlled shunt reactors and series compensation,and will be employed in ultra-high-voltage(UHV)power systems.The single-phase auto-reclosure characteristics of secondary arcs in systems with HRPC require further investigation.In this paper,both the arc-recalling voltage and subsidiary variations in arc current are investigated with and without HRPC.The frequency components of the secondary arc current and variations in arcing time are analyzed for various influential factors,such as the neutral reactor,arc resistance,fault location,degrees of compensation of HRPC,and the length of the transmission line.The non-dominated sorting genetic algorithm II(NSGA-II)and support vector machine regression are combined to create a multi-variable dependent forecasting algorithm to predict the characteristics of the secondary arc in UHV systems with HRPC.This paper provides a theoretical reference for optimizing the parameters of HRPC,and for developing adaptive auto-reclosure schemes and protection equipment.展开更多
To generate a test set for a given circuit (including both combinational and sequential circuits), choice of an algorithm within a number of existing test generation algorithms to apply is bound to vary from circuit t...To generate a test set for a given circuit (including both combinational and sequential circuits), choice of an algorithm within a number of existing test generation algorithms to apply is bound to vary from circuit to circuit. In this paper, the genetic algorithms are used to construct the models of existing test generation algorithms in making such choice more easily. Therefore, we may forecast the testability parameters of a circuit before using the real test generation algorithm. The results also can be used to evaluate the efficiency of the existing test generation algorithms. Experimental results are given to convince the readers of the truth and the usefulness of this approach.展开更多
Considering the factors affecting the increasing rate of power consumption, the BP neural network structure and the neural network forecasting model of the increasing rate of power consumption were established. Immune...Considering the factors affecting the increasing rate of power consumption, the BP neural network structure and the neural network forecasting model of the increasing rate of power consumption were established. Immune genetic algorithm was applied to optimizing the weight from input layer to hidden layer, from hidden layer to output layer, and the threshold value of neuron nodes in hidden and output layers. Finally, training the related data of the increasing rate of power consumption from 1980 to 2000 in China, a nonlinear network model between the increasing rate of power consumption and influencing factors was obtained. The model was adopted to forecasting the increasing rate of power consumption from 2001 to 2005, and the average absolute error ratio of forecasting results is 13.521 8%. Compared with the ordinary neural network optimized by genetic algorithm, the results show that this method has better forecasting accuracy and stability for forecasting the increasing rate of power consumption.展开更多
Electricity is the guarantee of economic development and daily life. Thus, accurate monthly electricity consumption forecasting can provide reliable guidance for power construction planning. In this paper, a hybrid mo...Electricity is the guarantee of economic development and daily life. Thus, accurate monthly electricity consumption forecasting can provide reliable guidance for power construction planning. In this paper, a hybrid model in combination of least squares support vector machine(LSSVM) model with fruit fly optimization algorithm(FOA) and the seasonal index adjustment is constructed to predict monthly electricity consumption. The monthly electricity consumption demonstrates a nonlinear characteristic and seasonal tendency. The LSSVM has a good fit for nonlinear data, so it has been widely applied to handling nonlinear time series prediction. However, there is no unified selection method for key parameters and no unified method to deal with the effect of seasonal tendency. Therefore, the FOA was hybridized with the LSSVM and the seasonal index adjustment to solve this problem. In order to evaluate the forecasting performance of hybrid model, two samples of monthly electricity consumption of China and the United States were employed, besides several different models were applied to forecast the two empirical time series. The results of the two samples all show that, for seasonal data, the adjusted model with seasonal indexes has better forecasting performance. The forecasting performance is better than the models without seasonal indexes. The fruit fly optimized LSSVM model outperforms other alternative models. In other words, the proposed hybrid model is a feasible method for the electricity consumption forecasting.展开更多
Budgeting planning plays an important role in coordinating activities in organizations. An accurate sales volume forecasting is the key to the entire budgeting process. All of the other parts of the master budget are ...Budgeting planning plays an important role in coordinating activities in organizations. An accurate sales volume forecasting is the key to the entire budgeting process. All of the other parts of the master budget are dependent on the sales volume forecasting in some way. If the sales volume forecasting is sloppily done, then the rest of the budgeting process is largely a waste of time. Therefore, the sales volume forecasting process is a critical one for most businesses, and also a difficult area of management. Most of researches and companies use the statistical methods, regression analysis, or sophisticated computer simulations to analyze the sales volume forecasting. Recently, various prediction Artificial Intelligent (AI) techniques have been proposed in forecasting. Support Vector Regression (SVR) has been applied successfully to solve problems in numerous fields and proved to be a better prediction model. However, the select of appropriate SVR parameters is difficult. Therefore, to improve the accuracy of SVR, a hybrid intelligent support system based on evolutionary computation to solve the difficulties involved with the parameters selection is presented in this research. Genetic Algorithms (GAs) are used to optimize free parameters of SVR. The experimental results indicate that GA-SVR can achieve better forecasting accuracy and performance than traditional SVR and artificial neural network (ANN) prediction models in sales volume forecasting.展开更多
Accurate and timely monthly rainfall forecasting is a major challenge for the scientific community in hydrological research such as river management project and design of flood warning systems. Support Vector Regressi...Accurate and timely monthly rainfall forecasting is a major challenge for the scientific community in hydrological research such as river management project and design of flood warning systems. Support Vector Regression (SVR) is a very useful precipitation prediction model. In this paper, a novel parallel co-evolution algorithm is presented to determine the appropriate parameters of the SVR in rainfall prediction based on parallel co-evolution by hybrid Genetic Algorithm and Particle Swarm Optimization algorithm, namely SVRGAPSO, for monthly rainfall prediction. The framework of the parallel co-evolutionary algorithm is to iterate two GA and PSO populations simultaneously, which is a mechanism for information exchange between GA and PSO populations to overcome premature local optimum. Our methodology adopts a hybrid PSO and GA for the optimal parameters of SVR by parallel co-evolving. The proposed technique is applied over rainfall forecasting to test its generalization capability as well as to make comparative evaluations with the several competing techniques, such as the other alternative methods, namely SVRPSO (SVR with PSO), SVRGA (SVR with GA), and SVR model. The empirical results indicate that the SVRGAPSO results have a superior generalization capability with the lowest prediction error values in rainfall forecasting. The SVRGAPSO can significantly improve the rainfall forecasting accuracy. Therefore, the SVRGAPSO model is a promising alternative for rainfall forecasting.展开更多
[Objective] The research aimed to study forecast models for frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River based on SVR optimized by particle swarm optimization algori...[Objective] The research aimed to study forecast models for frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River based on SVR optimized by particle swarm optimization algorithm. [Method] Correlation analysis and cause analysis were used to select suitable forecast factor combination of the ice regime. Particle swarm optimization algorithm was used to determine the optimal parameter to construct forecast model. The model was used to forecast frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River. [Result] The model had high prediction accuracy and short running time. Average forecast error was 3.51 d, and average running time was 10.464 s. Its forecast effect was better than that of the support vector regression optimized by genetic algorithm (GA) and back propagation type neural network (BPNN). It could accurately forecast frozen and melted dates of the river water. [Conclusion] SVR based on particle swarm optimization algorithm could be used for ice regime forecast.展开更多
针对小白菜生长中存在的养分供应不平衡与施肥量预测模型精度不足的问题,该研究搭建了一套基于实时土壤养分检测的变量施肥机,可在单次作业中实现土壤参数的在线采集、施肥需求预测与施肥执行的协同运行。同时运用机器学习方法,结合小...针对小白菜生长中存在的养分供应不平衡与施肥量预测模型精度不足的问题,该研究搭建了一套基于实时土壤养分检测的变量施肥机,可在单次作业中实现土壤参数的在线采集、施肥需求预测与施肥执行的协同运行。同时运用机器学习方法,结合小白菜历史生长环境与养分数据,构建了基于混合乌燕鸥算法优化的BP神经网络(backpropagation neural network model based on hybrid sooty tern optimization algorithm,HA-STOA-BP)预测模型。预测结果与BP神经网络预测模型、基于鲸鱼算法优化的BP神经网络预测模型(WOA-BP)以及基于乌燕鸥算法优化的BP神经网络(STOA-BP)预测模型进行比较,结果显示HA-STOA-BP模型预测值与实际施肥量的变化趋势高度一致,模型平均决定系数达0.970,而STOA-BP模型、WOA-BP模型以及BP模型平均决定系数分别为0.867、0.815以及0.656;同时HA-STOA-BP预测模型的最大绝对百分比误差为9.89%,均小于STOA-BP模型、WOA-BP模型以及BP模型最大绝对百分比误差的17.17%、18.15%、24.19%,表明该预测模型具有更好的预测性能。在此基础上,通过田间试验对变量施肥装置在不同作业速度下的排肥稳定性与作业性能进行了系统评估。选取0.30、0.65和0.80 m/s三种典型作业速度开展排肥精度测试。试验结果表明,在0.30 m/s作业速度下,平均排肥精度达到97.5%;在0.65 m/s作业速度下,平均排肥精度为95.1%。随着作业速度的提高,排肥精度出现一定程度的下降趋势,但在0.80 m/s条件下平均排肥精度仍保持在91.0%。上述结果表明,所提出的变量施肥机排肥策略模型能够提高小白菜施肥量预测的精度,可为实现快速、精准和高效的变量施肥提供参考。展开更多
为准确预测电力负荷对优化发电和调度计划的影响,提升经济效益,保障电网安全运行,提出一种基于体感温度和改进菲克定律算法(improved Fick’s law algorithm,IFLA)优化CNN-BiLSTM的短期电力负荷预测模型。采用Logistic映射、柯西-高斯...为准确预测电力负荷对优化发电和调度计划的影响,提升经济效益,保障电网安全运行,提出一种基于体感温度和改进菲克定律算法(improved Fick’s law algorithm,IFLA)优化CNN-BiLSTM的短期电力负荷预测模型。采用Logistic映射、柯西-高斯变异策略、螺旋波动搜索等改进FLA。首先用体感温度公式对气象数据进行特征增强处理,其次通过IFLA对CNN-BiLSTM网络进行超参数优化,最后由CNNBiLSTM对数据进行特征提取并输出负荷预测结果。通过对2022年3月湖南某地居民用电负荷数据集进行仿真实验,实验结果表明,IFLA-CNN-BiLSTM预测模型输出的均方根误差为1.305、平均绝对误差为0.882、平均绝对百分数误差为2.558%、决定系数分别为0.989,验证了该模型在实际应用环境下的泛化性及可靠性。展开更多
文摘Hybrid reactive power compensation(HRPC)combines step-controlled shunt reactors and series compensation,and will be employed in ultra-high-voltage(UHV)power systems.The single-phase auto-reclosure characteristics of secondary arcs in systems with HRPC require further investigation.In this paper,both the arc-recalling voltage and subsidiary variations in arc current are investigated with and without HRPC.The frequency components of the secondary arc current and variations in arcing time are analyzed for various influential factors,such as the neutral reactor,arc resistance,fault location,degrees of compensation of HRPC,and the length of the transmission line.The non-dominated sorting genetic algorithm II(NSGA-II)and support vector machine regression are combined to create a multi-variable dependent forecasting algorithm to predict the characteristics of the secondary arc in UHV systems with HRPC.This paper provides a theoretical reference for optimizing the parameters of HRPC,and for developing adaptive auto-reclosure schemes and protection equipment.
基金This work was supported by National Natural Science Foundation of China (NSFC) under the grant !No. 69873030
文摘To generate a test set for a given circuit (including both combinational and sequential circuits), choice of an algorithm within a number of existing test generation algorithms to apply is bound to vary from circuit to circuit. In this paper, the genetic algorithms are used to construct the models of existing test generation algorithms in making such choice more easily. Therefore, we may forecast the testability parameters of a circuit before using the real test generation algorithm. The results also can be used to evaluate the efficiency of the existing test generation algorithms. Experimental results are given to convince the readers of the truth and the usefulness of this approach.
基金Project(70373017) supported by the National Natural Science Foundation of China
文摘Considering the factors affecting the increasing rate of power consumption, the BP neural network structure and the neural network forecasting model of the increasing rate of power consumption were established. Immune genetic algorithm was applied to optimizing the weight from input layer to hidden layer, from hidden layer to output layer, and the threshold value of neuron nodes in hidden and output layers. Finally, training the related data of the increasing rate of power consumption from 1980 to 2000 in China, a nonlinear network model between the increasing rate of power consumption and influencing factors was obtained. The model was adopted to forecasting the increasing rate of power consumption from 2001 to 2005, and the average absolute error ratio of forecasting results is 13.521 8%. Compared with the ordinary neural network optimized by genetic algorithm, the results show that this method has better forecasting accuracy and stability for forecasting the increasing rate of power consumption.
基金National Social Science Foundation of China(No.18AGL028)Social Science Foundation of the Higher Education Institutions Jiangsu Province,China(No.2018SJZDI070)Social Science Foundation of the Jiangsu Province,China(Nos.16ZZB004,17ZTB005)
文摘Electricity is the guarantee of economic development and daily life. Thus, accurate monthly electricity consumption forecasting can provide reliable guidance for power construction planning. In this paper, a hybrid model in combination of least squares support vector machine(LSSVM) model with fruit fly optimization algorithm(FOA) and the seasonal index adjustment is constructed to predict monthly electricity consumption. The monthly electricity consumption demonstrates a nonlinear characteristic and seasonal tendency. The LSSVM has a good fit for nonlinear data, so it has been widely applied to handling nonlinear time series prediction. However, there is no unified selection method for key parameters and no unified method to deal with the effect of seasonal tendency. Therefore, the FOA was hybridized with the LSSVM and the seasonal index adjustment to solve this problem. In order to evaluate the forecasting performance of hybrid model, two samples of monthly electricity consumption of China and the United States were employed, besides several different models were applied to forecast the two empirical time series. The results of the two samples all show that, for seasonal data, the adjusted model with seasonal indexes has better forecasting performance. The forecasting performance is better than the models without seasonal indexes. The fruit fly optimized LSSVM model outperforms other alternative models. In other words, the proposed hybrid model is a feasible method for the electricity consumption forecasting.
文摘Budgeting planning plays an important role in coordinating activities in organizations. An accurate sales volume forecasting is the key to the entire budgeting process. All of the other parts of the master budget are dependent on the sales volume forecasting in some way. If the sales volume forecasting is sloppily done, then the rest of the budgeting process is largely a waste of time. Therefore, the sales volume forecasting process is a critical one for most businesses, and also a difficult area of management. Most of researches and companies use the statistical methods, regression analysis, or sophisticated computer simulations to analyze the sales volume forecasting. Recently, various prediction Artificial Intelligent (AI) techniques have been proposed in forecasting. Support Vector Regression (SVR) has been applied successfully to solve problems in numerous fields and proved to be a better prediction model. However, the select of appropriate SVR parameters is difficult. Therefore, to improve the accuracy of SVR, a hybrid intelligent support system based on evolutionary computation to solve the difficulties involved with the parameters selection is presented in this research. Genetic Algorithms (GAs) are used to optimize free parameters of SVR. The experimental results indicate that GA-SVR can achieve better forecasting accuracy and performance than traditional SVR and artificial neural network (ANN) prediction models in sales volume forecasting.
文摘Accurate and timely monthly rainfall forecasting is a major challenge for the scientific community in hydrological research such as river management project and design of flood warning systems. Support Vector Regression (SVR) is a very useful precipitation prediction model. In this paper, a novel parallel co-evolution algorithm is presented to determine the appropriate parameters of the SVR in rainfall prediction based on parallel co-evolution by hybrid Genetic Algorithm and Particle Swarm Optimization algorithm, namely SVRGAPSO, for monthly rainfall prediction. The framework of the parallel co-evolutionary algorithm is to iterate two GA and PSO populations simultaneously, which is a mechanism for information exchange between GA and PSO populations to overcome premature local optimum. Our methodology adopts a hybrid PSO and GA for the optimal parameters of SVR by parallel co-evolving. The proposed technique is applied over rainfall forecasting to test its generalization capability as well as to make comparative evaluations with the several competing techniques, such as the other alternative methods, namely SVRPSO (SVR with PSO), SVRGA (SVR with GA), and SVR model. The empirical results indicate that the SVRGAPSO results have a superior generalization capability with the lowest prediction error values in rainfall forecasting. The SVRGAPSO can significantly improve the rainfall forecasting accuracy. Therefore, the SVRGAPSO model is a promising alternative for rainfall forecasting.
基金Supported by National Nature Science Fund Item,China (51009065)Key Science and Technology Research Plan Program in Henan Province,China(112102110033)
文摘[Objective] The research aimed to study forecast models for frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River based on SVR optimized by particle swarm optimization algorithm. [Method] Correlation analysis and cause analysis were used to select suitable forecast factor combination of the ice regime. Particle swarm optimization algorithm was used to determine the optimal parameter to construct forecast model. The model was used to forecast frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River. [Result] The model had high prediction accuracy and short running time. Average forecast error was 3.51 d, and average running time was 10.464 s. Its forecast effect was better than that of the support vector regression optimized by genetic algorithm (GA) and back propagation type neural network (BPNN). It could accurately forecast frozen and melted dates of the river water. [Conclusion] SVR based on particle swarm optimization algorithm could be used for ice regime forecast.
文摘针对小白菜生长中存在的养分供应不平衡与施肥量预测模型精度不足的问题,该研究搭建了一套基于实时土壤养分检测的变量施肥机,可在单次作业中实现土壤参数的在线采集、施肥需求预测与施肥执行的协同运行。同时运用机器学习方法,结合小白菜历史生长环境与养分数据,构建了基于混合乌燕鸥算法优化的BP神经网络(backpropagation neural network model based on hybrid sooty tern optimization algorithm,HA-STOA-BP)预测模型。预测结果与BP神经网络预测模型、基于鲸鱼算法优化的BP神经网络预测模型(WOA-BP)以及基于乌燕鸥算法优化的BP神经网络(STOA-BP)预测模型进行比较,结果显示HA-STOA-BP模型预测值与实际施肥量的变化趋势高度一致,模型平均决定系数达0.970,而STOA-BP模型、WOA-BP模型以及BP模型平均决定系数分别为0.867、0.815以及0.656;同时HA-STOA-BP预测模型的最大绝对百分比误差为9.89%,均小于STOA-BP模型、WOA-BP模型以及BP模型最大绝对百分比误差的17.17%、18.15%、24.19%,表明该预测模型具有更好的预测性能。在此基础上,通过田间试验对变量施肥装置在不同作业速度下的排肥稳定性与作业性能进行了系统评估。选取0.30、0.65和0.80 m/s三种典型作业速度开展排肥精度测试。试验结果表明,在0.30 m/s作业速度下,平均排肥精度达到97.5%;在0.65 m/s作业速度下,平均排肥精度为95.1%。随着作业速度的提高,排肥精度出现一定程度的下降趋势,但在0.80 m/s条件下平均排肥精度仍保持在91.0%。上述结果表明,所提出的变量施肥机排肥策略模型能够提高小白菜施肥量预测的精度,可为实现快速、精准和高效的变量施肥提供参考。
文摘为准确预测电力负荷对优化发电和调度计划的影响,提升经济效益,保障电网安全运行,提出一种基于体感温度和改进菲克定律算法(improved Fick’s law algorithm,IFLA)优化CNN-BiLSTM的短期电力负荷预测模型。采用Logistic映射、柯西-高斯变异策略、螺旋波动搜索等改进FLA。首先用体感温度公式对气象数据进行特征增强处理,其次通过IFLA对CNN-BiLSTM网络进行超参数优化,最后由CNNBiLSTM对数据进行特征提取并输出负荷预测结果。通过对2022年3月湖南某地居民用电负荷数据集进行仿真实验,实验结果表明,IFLA-CNN-BiLSTM预测模型输出的均方根误差为1.305、平均绝对误差为0.882、平均绝对百分数误差为2.558%、决定系数分别为0.989,验证了该模型在实际应用环境下的泛化性及可靠性。
