In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using concept...In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using conceptions of the relative error,the change tendency of the forecasted object,gray basic weight and adaptive control coefficient on the basis of the method of fuzzy variable weight.Based on Visual Basic 6.0 platform,a fuzzy adaptive variable weight combined forecasting and management system was developed.The application results reveal that the forecasting precisions from the new nonlinear combined forecasting model are higher than those of other single combined forecasting models and the combined forecasting and management system is very powerful tool for the required decision in complex industry system.展开更多
To solve the medium and long term power load forecasting problem,the combination forecasting method is further expanded and a weighted combination forecasting model for power load is put forward.This model is divided ...To solve the medium and long term power load forecasting problem,the combination forecasting method is further expanded and a weighted combination forecasting model for power load is put forward.This model is divided into two stages which are forecasting model selection and weighted combination forecasting.Based on Markov chain conversion and cloud model,the forecasting model selection is implanted and several outstanding models are selected for the combination forecasting.For the weighted combination forecasting,a fuzzy scale joint evaluation method is proposed to determine the weight of selected forecasting model.The percentage error and mean absolute percentage error of weighted combination forecasting result of the power consumption in a certain area of China are 0.7439%and 0.3198%,respectively,while the maximum values of these two indexes of single forecasting models are 5.2278%and 1.9497%.It shows that the forecasting indexes of proposed model are improved significantly compared with the single forecasting models.展开更多
Oil is an important strategic material and civil energy.Accurate prediction of oil consumption can provide basis for relevant departments to reasonably arrange crude oil production,oil import and export,and optimize t...Oil is an important strategic material and civil energy.Accurate prediction of oil consumption can provide basis for relevant departments to reasonably arrange crude oil production,oil import and export,and optimize the allocation of social resources.Therefore,a new grey model FENBGM(1,1)is proposed to predict oil consumption in China.Firstly,the grey effect of the traditional GM(1,1)model was transformed into a quadratic equation.Four different parameters were introduced to improve the accuracy of the model,and the new initial conditions were designed by optimizing the initial values by weighted buffer operator.Combined with the reprocessing of the original data,the scheme eliminates the random disturbance effect,improves the stability of the system sequence,and can effectively extract the potential pattern of future development.Secondly,the cumulative order of the new model was optimized by fractional cumulative generation operation.At the same time,the smoothness rate quasi-smoothness condition was introduced to verify the stability of the model,and the particle swarm optimization algorithm(PSO)was used to search the optimal parameters of the model to enhance the adaptability of the model.Based on the above improvements,the new combination prediction model overcomes the limitation of the traditional grey model and obtains more accurate and robust prediction results.Then,taking the petroleum consumption of China's manufacturing industry and transportation,storage and postal industry as an example,this paper verifies the validity of FENBGM(1,1)model,analyzes and forecasts China's crude oil consumption with several commonly used forecasting models,and uses FENBGM(1,1)model to forecast China's oil consumption in the next four years.The results show that FENBGM(1,1)model performs best in all cases.Finally,based on the prediction results of FENBGM(1,1)model,some reasonable suggestions are put forward for China's oil consumption planning.展开更多
Underground energy storage is an important function of all energy supply systems,and especially concerning the seemingly eternal imbalance between production and demand.Salt rock underground energy storage,for one,is ...Underground energy storage is an important function of all energy supply systems,and especially concerning the seemingly eternal imbalance between production and demand.Salt rock underground energy storage,for one,is widely applied in both traditional and renewable energy fields;and this particular technique can be used to store natural gas,hydrogen,and compressed air.However,resource diversification and structural complexity make the supply system increasingly uncertain with the passing years,leading to great challenges for energy storage facilities in the present,and perhaps going into the future as well.Hence,it is necessary to research the operation stability of underground energy storage further.In this paper,a stability evaluation index system of Underground Gas Storage(UGS)is constructed with natural gas as the main medium,according to FLAC 3D cavity creep simulation software,along with fuzzy membership function to comprehensively determine the impact factor scoring model;the subjective weight is calculated based on the improved Analytic Hierarchy Process(AHP),the objective weight is calculated by the Entropy Weight Method(EWM),the combined constant weight is obtained by combining the variance maximization theory,and introducing the variable weight theory to obtain a more accurate combined variable weight.Finally,with this all being considered and accounted for,and with the four different conditions designed for UGS deployment case analysis and verification taken into consideration,the combined variable weight evaluation achieved excellent results;compared with the traditional constant weight method,in fact,the new evaluation results are more rigorous and objective.展开更多
基金Project(08SK1002) supported by the Major Project of Science and Technology Department of Hunan Province,China
文摘In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using conceptions of the relative error,the change tendency of the forecasted object,gray basic weight and adaptive control coefficient on the basis of the method of fuzzy variable weight.Based on Visual Basic 6.0 platform,a fuzzy adaptive variable weight combined forecasting and management system was developed.The application results reveal that the forecasting precisions from the new nonlinear combined forecasting model are higher than those of other single combined forecasting models and the combined forecasting and management system is very powerful tool for the required decision in complex industry system.
文摘To solve the medium and long term power load forecasting problem,the combination forecasting method is further expanded and a weighted combination forecasting model for power load is put forward.This model is divided into two stages which are forecasting model selection and weighted combination forecasting.Based on Markov chain conversion and cloud model,the forecasting model selection is implanted and several outstanding models are selected for the combination forecasting.For the weighted combination forecasting,a fuzzy scale joint evaluation method is proposed to determine the weight of selected forecasting model.The percentage error and mean absolute percentage error of weighted combination forecasting result of the power consumption in a certain area of China are 0.7439%and 0.3198%,respectively,while the maximum values of these two indexes of single forecasting models are 5.2278%and 1.9497%.It shows that the forecasting indexes of proposed model are improved significantly compared with the single forecasting models.
基金This work was supported by the National Natural Science Foundation of China(No.71901184,No.72001181).
文摘Oil is an important strategic material and civil energy.Accurate prediction of oil consumption can provide basis for relevant departments to reasonably arrange crude oil production,oil import and export,and optimize the allocation of social resources.Therefore,a new grey model FENBGM(1,1)is proposed to predict oil consumption in China.Firstly,the grey effect of the traditional GM(1,1)model was transformed into a quadratic equation.Four different parameters were introduced to improve the accuracy of the model,and the new initial conditions were designed by optimizing the initial values by weighted buffer operator.Combined with the reprocessing of the original data,the scheme eliminates the random disturbance effect,improves the stability of the system sequence,and can effectively extract the potential pattern of future development.Secondly,the cumulative order of the new model was optimized by fractional cumulative generation operation.At the same time,the smoothness rate quasi-smoothness condition was introduced to verify the stability of the model,and the particle swarm optimization algorithm(PSO)was used to search the optimal parameters of the model to enhance the adaptability of the model.Based on the above improvements,the new combination prediction model overcomes the limitation of the traditional grey model and obtains more accurate and robust prediction results.Then,taking the petroleum consumption of China's manufacturing industry and transportation,storage and postal industry as an example,this paper verifies the validity of FENBGM(1,1)model,analyzes and forecasts China's crude oil consumption with several commonly used forecasting models,and uses FENBGM(1,1)model to forecast China's oil consumption in the next four years.The results show that FENBGM(1,1)model performs best in all cases.Finally,based on the prediction results of FENBGM(1,1)model,some reasonable suggestions are put forward for China's oil consumption planning.
基金supported by the National Natural Science Foundation of China[51704253].
文摘Underground energy storage is an important function of all energy supply systems,and especially concerning the seemingly eternal imbalance between production and demand.Salt rock underground energy storage,for one,is widely applied in both traditional and renewable energy fields;and this particular technique can be used to store natural gas,hydrogen,and compressed air.However,resource diversification and structural complexity make the supply system increasingly uncertain with the passing years,leading to great challenges for energy storage facilities in the present,and perhaps going into the future as well.Hence,it is necessary to research the operation stability of underground energy storage further.In this paper,a stability evaluation index system of Underground Gas Storage(UGS)is constructed with natural gas as the main medium,according to FLAC 3D cavity creep simulation software,along with fuzzy membership function to comprehensively determine the impact factor scoring model;the subjective weight is calculated based on the improved Analytic Hierarchy Process(AHP),the objective weight is calculated by the Entropy Weight Method(EWM),the combined constant weight is obtained by combining the variance maximization theory,and introducing the variable weight theory to obtain a more accurate combined variable weight.Finally,with this all being considered and accounted for,and with the four different conditions designed for UGS deployment case analysis and verification taken into consideration,the combined variable weight evaluation achieved excellent results;compared with the traditional constant weight method,in fact,the new evaluation results are more rigorous and objective.