Multimodel Ensemble Forecast of Global Horizontal Irradiance at PV Power Stations Based on Dynamic Variable Weight

In the present study,multimodel ensemble forecast experiments of the global horizontal irradiance(GHI)were conducted using the dynamic variable weight technique.The study was based on the forecasts of four numerical models,namely,the China Meteorological Administration Wind Energy and Solar Energy Prediction System,the Mesoscale Weather Numerical Prediction System of China Meteorological Administration,the China Meteorological Administration Regional Mesoscale Numerical Prediction System-Guangdong,and the Weather Research and Forecasting Model-Solar,and observational data from four photovoltaic(PV)power stations in Yangjiang City,Guangdong Province.The results show that compared with those of the monthly optimal numerical model forecasts,the dynamic variable weight-based ensemble forecasts exhibited 0.97%-15.96%smaller values of the mean absolute error and 3.31%-18.40%lower values of the root mean square error(RMSE).However,the increase in the correlation coefficient was not obvious.Specifically,the multimodel ensemble mainly improved the performance of GHI forecasts below 700 W m^(-2),particularly below 400 W m^(-2),with RMSE reductions as high as 7.56%-28.28%.In contrast,the RMSE increased at GHI levels above 700 W m^(-2).As for the key period of PV power station output(02:00-07:00),the accuracy of GHI forecasts could be improved by the multimodel ensemble:the multimodel ensemble could effectively decrease the daily maximum absolute error(AE max)of GHI forecasts.Moreover,with increasing forecasting difficulty under cloudy conditions,the multimodel ensemble,which yields data closer to the actual observations,could simulate GHI fluctuations more accurately.

Evaluation on Fuzzy Variable Weight of Cultivated Land Fertility Based on GIS

[Objective] The modified variable weights based on constant weight and in- troduced theory of equalization function would better incorporate authentic index weights and make evaluation results of fertility more scientific. [Method] In Gaozhou City, the final weights of influential factors can be determined with the help of GIS and as per AHP and theory of variable weights. In addition, farmland fertility was e- valuated in an automatic and quantitative way and the spatial distribution pattern was analyzed as per fuzzy comprehensive evaluation. [Result] For farmlands at 58 505.027 8 hm2 in the city, farmlands from grade 1 to grade 8 account for 3.62%, 18.27%, 33.15%, 26.96%, 13.66%, 3.29%, 0.81% and 0.24%, respectively, which is in consistent with local condition. [Conclusion] These results have been applied di- rectly in test regions and constitute a rewarding exploration for fertility evaluation in South China.

Risk assessment of floor water inrush in coal mines based on MFIM-TOPSIS variable weight model

Floor water inrush is one of the main types of coal mine water hazards.With the development of deep mining,the prediction and evaluation of floor water inrush is particularly significant.This paper proposes a variable weight model,which combines a multi-factor interaction matrix(MFIM)and the technique for order performance by similarity to ideal solution(TOPSIS)to implement the risk assessment of floor water inrush in coal mines.Based on the MFIM,the interaction between seven evaluation indices,including the confined water pressure,water supply condition and aquifer water yield property,floor aquifuge thickness,fault water transmitting ability,fracture development degree,mining depth and thickness and their influence on floor water inrush were considered.After calculating the constant weights,the active degree evaluation was used to assign a variable weight to the indices.The values of the middle layer and final risk level were obtained by TOPSIS.The presented model was successfully applied in the 9901 working face in the Taoyang Mine and four additional coal mines and the results were highly consistent with the engineering situations.Compared with the existing nonlinear evaluation methods,the proposed model had advantages in terms of the weighting,principle explanation,and algorithm structure.

Using Fuzzy Theory and Variable Weights for Water Quality Evaluation in Poyang Lake, China

Achieving water purity in Poyang Lake has become a major concern in recent years, thus appropriate evaluation of spatial and temporal water quality variations has become essential. Variations in 11 water quality parameters from 15 sampling sites in Poyang Lake were investigated from 2009 to 2012. An integrative fuzzy variable evaluation(IFVE) model based on fuzzy theory and variable weights was developed to measure variations in water quality. Results showed that: 1) only chlorophyll-a concentration and Secchi depth differed significantly among the 15 sampling sites(P < 0.01), whereas the 11 water quality parameters under investigation differed significantly throughout the seasons(P < 0.01). The annual variations of all water quality variables except for temperature, electrical conductivity, suspended solids and total phosphorus were considerable(P < 0.05). 2) The IFVE model was reasonable and flexible in evaluating water quality status and any possible ′bucket effect′. The model fully considered the influences of extremely poor indices on overall water quality. 3) A spatial analysis indicated that anthropogenic activities(particularly industrial sewage and dredging) and lake bed topography might directly affect water quality in Poyang Lake. Meanwhile, hydrological status and sewage discharged into the lake might be responsible for seasonal water quality variations.

A variable weight approach for evidential reasoning

A variable weight approach was proposed to handle the probability deficiency problem in the evidential reasoning （ER） approach. The probability deficiency problem indicated that the inadequate information in the assessment result should be less than that in the input. However, it was proved that under certain circumstances, the ER approach could not solve the probability deficiency problem. The variable weight approach was based on two assumptions： 1） the greater weight should be given to the rule with more adequate information; 2） the greater weight should be given to the rules with less disparate information. Assessment results of two notional case studies show that 1） the probability deficiency problem is solved using the proposed variable weight approach, and 2） the information with less inadequacy and more disparity is provided for the decision makers to help reach a consensus.

Nonlinear combined forecasting model based on fuzzy adaptive variable weight and its application

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.

Information Security Testing Model Based on Variable Weights Fuzzy Comprehensive Evaluation

Nowadays,clear evaluation models and methods are lacking in classified protection of information system,which our country is making efforts to promote.The quantitative evaluation of classified protection of information system security is studied.An indicators system of testing and evaluation is established.Furthermore,a model of unit testing and evaluation and a model of entirety testing and evaluation are presented respectively.With analytic hierarchy process and two-grade fuzzy comprehensive evaluation,the subjective and uncertain data of evaluation will be quantitatively analyzed by comprehensive evaluation.Particularly,the variable weight method is used to model entirety testing and evaluation.It can solve the problem that the weights need to be adjusted because of the relationship role which enhances or reduces security of information system.Finally,the paper demonstrates that the model testing and evaluation can be validly used to evaluate the information system by an example.The model proposed in this paper provides a new valuable way for classified protection of information system security.

A comprehensive evaluation of enterprise emergency management capacity （EEMC） based on variable weight gray cluster

On the basis of the initial definition of Enterprise Emergency Management Capacity（EEMC）, the paper has established evaluation index system of EEMC, and provided a method to calculate index weight, with the regard to subjectivity existing in the comprehensive evaluation of EEMC multi-indicators, in accordance with the principle of Variable weight Gray Cluster, which makes the weight of indicators generate automatically in the evaluation process and not judged by human, thus decreasing subjective factors during the evaluation.

A Study on Evaluation Model of IT Industry’s Innovation Capability Based Variable Weight Theory

In this paper, IT Industry＇s innovation capability is considered to be the innovation output capability after complex operation of industry input in industry system. In this complex process, R＆D personnel input and R＆D expense input are un-substitutable, and for evaluation of innovation capability, innovation input and innovation output also are un-substitutable. Based on this theory, an evaluation model of sustaining strength index is put forward. Considering both input scale and output contribution of IT industry＇s innovation system, this model reflects the un-substitutability of every evaluation aspects. The measurement result not only shows the industry innovation capability, but also reflects the support degree to economy. At last the data of IT industry in China are provided between 1994 and 2004 for empirical study.

3D suitability evaluation of urban underground space using a variable weight method and considering ground restrictions

The evaluation of urban underground space(UUS)suitability involves multiple indicators.Assigning weight to these indicators is crucial for accurate assessment.This paper presents a method for spatially variable weight assignment of indicators using the order relation analysis method(G1-method),the entropy weight method,an improved grey relational analysis(GRA)and a set of spatial weight adjustment coefficients.First,the subjective and objective weights of indicators for engineering geological and hydrogeological conditions were determined by the G1-method and entropy weight method,respectively,and their combined weights were then obtained using the principle of minimum discriminatory information.This study highlighted the impact of surface restrictions,such as buildings,on UUS,and the degree of the influence of these buildings gradually decreased with the increase in depth of the rock and soil mass in UUS,which resulted in changes in weights of indicators with depth.To address this issue,a coefficient was defined as the standardized value of the ratio of additional stress applied by restrictions to the self-weight stress of soil at the same depth to modify the combined weights so that all weights of indicators could vary in space.Finally,an improved GRA was used to determine the suitability level of each evaluation cell using the maximum correlation criterion.This method was applied to the 3D suitability evaluation of UUS in Sanlong Bay,Foshan City,Guangdong Province,China,including 16 evaluation indexes.This study comprehensively considered the influence of multiple factors,thereby providing reference for evaluating the suitability of UUS in big cities.

An Investigation of Coal Demand in China Based on the Variable Weight Combination Forecasting Model

Variable weight combination forecasting combines individual forecasting models after giving them proper weights at each time point. Weight is the type of function that changes with forecast time. A relatively rational description of the system can be proposed with the forecasting method, which is of higher precision and better stability. Two individual forecasting models, grey system forecasting and multiple regression forecasting, were generated based on the historical data and influencing factors of coal demand in China from 1981 to 2008. According to the theory of combination forecasting, the variable weight combination forecasting model was formulated to forecast coal demand in China for the next 12 years.

A novel fractional grey forecasting model with variable weighted buffer operator and its application in forecasting China's crude oil consumption

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.

PCA weight and Johnson transformation based alarm threshold optimization in chemical processes

To alleviate the heavy load of massive alarm on operators, alarm threshold in chemical processes was optimized with principal component analysis(PCA) weight and Johnson transformation in this paper. First, few variables that have high PCA weight factors are chosen as key variables. Given a total alarm frequency to these variables initially, the allowed alarm number for each variable is determined according to their sampling time and weight factors. Their alarm threshold and then control limit percentage are determined successively. The control limit percentage of non-key variables is determined with 3σ method alternatively. Second, raw data are transformed into normal distribution data with Johnson function for all variables before updating their alarm thresholds via inverse transformation of obtained control limit percentage. Alarm thresholds are optimized by iterating this process until the calculated alarm frequency reaches standard level(normally one alarm per minute). Finally,variables and their alarm thresholds are visualized in parallel coordinate to depict their variation trends concisely and clearly. Case studies on a simulated industrial atmospheric-vacuum crude distillation demonstrate that the proposed alarm threshold optimization strategy can effectively reduce false alarm rate in chemical processes.

Application of fuzzy optimization model in the evaluationof urban flood-waterloggedvulnerabilitybased on the synthetic weight ofgame theory

The evaluation of urban flood-waterlogged vulnerability is very important to the safety of urban flood control. In this paper, the evaluation of consolidated index is used. Respectively, AHP and entropy method calculate the subjective and objective weight of the evaluation indicators, and combine them by game theory. So we can obtain synthetic weight based on objective and subjective weights. The evaluation of urban flood-waterlogged vulnerability as target layer, a single variable multi-objective fuzzy optimization model is established. We use the model to evaluate flood-waterlogged vulnerability of 13 prefecture-level city in Hunan, and compare it with other evaluation method. The results show that the evaluation method has certain adaptability and reliability, and it＇ s helpfid to the construction planning of urban flood control.

Icing tolerance envelope protection based on variable-weighted multiple-model predictive control

Multiple-model predictive control(MMPC) is a fundamental icing tolerance envelope protection(ITEP) design method that can systematically handle nonlinear and time-varying constraints. However, few studies have addressed the envelope protection failure that results from the inaccurate prediction of multiple linear predictive models when actual conditions deviate from design conditions. In this study, weights that vary with icing conditions and flight parameters are considered to develop an effective and reliable envelope protection control strategy. First, an ITEP structure based on variable-weighted MMPC was implemented to improve the protection performance with condition departure information. Then, a variable-weighted rule was proposed to guarantee the stability of variable-weighted MMPC. A design approach involving a variable-weighted function that uses icing conditions and flight parameters as arguments was also developed with the proposed rules. Finally, a systematic ITEP design method on variable-weighted MMPC was constructed with additional design criteria for other normal control parameters.Simulations were conducted, and the results show that the proposed method can effectively enhance ITEP performance.

Investigation on the Long Term Operational Stability of Underground Energy Storage in Salt Rock

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.

METHOD FOR BRIDGE BEARING CAPACITY ASSESSMENT BASED ON ANALYTIC HIERARCHY PROCESS

By analyzing the existing methods for the bridge bearing capacity assessment, an analytic hierarchy pro cess estimation model with a variable weight and fuzzy description is proposed based on the nondestructive infor mation. Considering the actual strength, the bearing capacity is first calculated from its design state, and then modified based on the detection information. The modification includes the section reduction and the structure deterioration. The section reduction involves the concrete section and the steel cross-section reduction. The structure deterioration is decided by six factors, i.e. , the concrete surface damage, the actual concrete strength, the steel corrosion electric potential, the chloride ion content, the carbonization depth, and the protective layer depth. The initial weight of each factor is calculated by the expert judgment matrix using an analytic hierarchy process. The consistency approximation and the error transfer theory are used. Then, the variable weight is in- troduced to expand the influences of factors in the worse state. Finally, an actual bridge is taken as an example to verify the proposed method. Results show that the estimated capacity agrees well with that of the load test, thus the method is objective and credible

Risk Analysis Technique on Inconsistent Interview Big Data Based on Rough Set Approach

Rough set theory is relativly new to area of soft computing to handle the uncertain big data efficiently. It also provides a powerful way to calculate the importance degree of vague and uncertain big data to help in decision making. Risk assessment is very important for safe and reliable investment. Risk management involves assessing the risk sources and designing strategies and procedures to mitigate those risks to an acceptable level. In this paper, we emphasize on classification of different types of risk factors and find a simple and effective way to calculate the risk exposure.. The study uses rough set method to classify and judge the safety attributes related to investment policy. The method which based on intelligent knowledge accusation provides an innovative way for risk analysis. From this approach, we are able to calculate the significance of each factor and relative risk exposure based on the original data without assigning the weight subjectively.

Weighted Estimates of Variable Kernel Fractional Integral and Its Commutators on Vanishing Generalized Morrey Spaces with Variable Exponent

In this paper,the authors obtain the boundedness of the fractional integral operators with variable kernels on the variable exponent generalized weighted Morrey spaces and the variable exponent vanishing generalized weighted Morrey spaces.And the corresponding commutators generated by BMO function are also considered.

Existence of <i>T</i>-<i>ν</i>-<i>p</i>(<i>x</i>)-Solution of a Nonhomogeneous Elliptic Problem with Right Hand Side Measure

Using the theory of weighted Sobolev spaces with variable exponent and the L1-version on Minty’s lemma, we investigate the existence of solutions for some nonhomogeneous Dirichlet problems generated by the Leray-Lions operator of divergence form, with right-hand side measure. Among the interest of this article is the given of a very important approach to ensure the existence of a weak solution of this type of problem and of generalization to a system with the minimum of conditions.