Carbon efficiency evaluation method for urban energy system with multiple energy complementary

Urban energy systems(UESs)play a pivotal role in the consumption of clean energy and the promotion of energy cascade utilization.In the context of the construction and operation strategy of UESs with multiple complementary energy resources,a comprehensive assessment of the energy efficiency is of paramount importance.First,a multi-dimensional evaluation system with four primary indexes of energy utilization,environmental protection,system operation,and economic efficiency and 21 secondary indexes is constructed to comprehensively portray the UES.Considering that the evaluation system may contain a large number of indexes and that there is overlapping information among them,an energy efficiency evaluation method based on data processing,dimensionality reduction,integration of combined weights,and gray correlation analysis is proposed.This method can effectively reduce the number of calculations and improve the accuracy of energy efficiency assessments.Third,a demonstration project for a UES in China is presented.The energy efficiency of each scenario is assessed using six operational scenarios.The results show that Scenario 5,in which parks operate independently and investors build shared energy-storage equipment,has the best results and is best suited for green and low-carbon development.The results of the comparative assessment methods show that the proposed method provides a good energy efficiency assessment.This study provides a reference for the optimal planning,construction,and operation of UESs with multiple energy sources.

Research on Demand Response Potential of Adjustable Loads in Demand Response Scenarios

To address the issues of limited demand response data,low generalization of demand response potential evaluation,and poor demand response effect,the article proposes a demand response potential feature extraction and prediction model based on data mining and a demand response potential assessment model for adjustable loads in demand response scenarios based on subjective and objective weight analysis.Firstly,based on the demand response process and demand response behavior,obtain demand response characteristics that characterize the process and behavior.Secondly,establish a feature extraction and prediction model based on data mining,including similar day clustering,time series decomposition,redundancy processing,and data prediction.The predicted values of each demand response feature on the response day are obtained.Thirdly,the predicted data of various characteristics on the response day are used as demand response potential evaluation indicators to represent different demand response scenarios and adjustable loads,and a demand response potential evaluation model based on subjective and objective weight allocation is established to calculate the demand response potential of different adjustable loads in different demand response scenarios.Finally,the effectiveness of the method proposed in the article is verified through examples,providing a reference for load aggregators to formulate demand response schemes.

Structural failure analysis with CMS-based ground motion selection using innovative cost function and weight factors

The selection and scaling of ground motion records is considered a primary and essential task in performing structural analysis and design.Conventional methods involve using ground motion models and a conditional spectrum to select ground motion records based on the target spectrum.This research demonstrates the influence of adopting different weighted factors for various period ranges during matching selected ground motions with the target hazard spectrum.The event data from the Next Generation Attenuation West 2(NGA-West 2)database is used as the basis for ground motion selection,and hazard de-aggregation is conducted to estimate the event parameters of interest,which are then used to construct the target intensity measure(IM).The target IMs are then used to select ground motion records with different weighted vector-valued objective functions.The weights are altered to account for the relative importance of IM in accordance with the structural analysis application of steel moment resisting frame(SMRF)buildings.Instead of an ordinary objective function for the matching spectrum,a novel model is introduced and compared with the conventional cost function.The results indicate that when applying the new cost function for ground motion selection,it places higher demands on structures compared to the conventional cost function.Moreover,submitting more weights to the first-mode period of structures increases engineering demand parameters.Findings demonstrate that weight factors allocated to different period ranges can successfully account for period elongation and higher mode effects.

Study of Multistage Project Risk Identification-Assessment Process Based on Objective Orientation

Risk management of projects is about the real time ev aluation and making of decisions proactively in order to maximize the probabilit y of achieving or surpassing the targets set for project objectives. Project objective generally includes three elements: time, cost, quality. Risk occurrin g in the projects will affect these three factors to some various degrees in the end. There are different emphases in each stage and integrated balanced goals b etween the three factors. A large complex engineering project generally consists of several stages each of which has variable objective combinations leading to variable important risks. In order to achieve strategic goals on the schedule under the restriction of lim ited resources, the paper gives the analysis of the so-called risk identificati on-assessment process on the basis of objective orientation. In this paper the set of involved mostly hazards is presented in terms of given objective weight v ector, and so is the model of risk ranking .By reducing the range of risk factor s step by step, risk manager could pay more attention to important ventures and effectively control of them. According to different objective combination at different stages, primary risk f actor sets at different stages are given. With the probability and their various effects to project objectives, evaluation of these sets is made aiming to r educing of the scope of risks and providing decision maker with a better decisio ns support. Successful projects are those, which focus on the relevant business objectives t hroughout the whole process and seek to information integration across project l ife cycle. This paper also introduces the idea of real time process of risk iden tification-assessment and presents a flow chart as a demonstration.

The Impact of Climate Change on Country’s Fragility Assessment

The damage costs of climate change have the potential to cause the breakdown of governmental structures. The paper focuses on how climate change impacts the state’s fragility and how the government should intervene. The paper chooses temperature and rainfall as climate change indicators and establishes a comprehensive evaluating model. The weighting method of the model is determined by combining coefficient of variation method and improved entropy method. The model will output the objective comprehensive evaluation of state’s fragility. The state’s fragilities are quantitatively divided into three levels consisting of the fragile, the vulnerable and the stable in the model. For validation, this paper selects six representative countries and analyzes the degree and the approach of climate change’s impacts on state’s fragility. The fragile values by the model are consistent with the situation in these countries. The state’s fragilities are also predicted by back propagation (BP) neural network. This paper analyzes the impacts of human interventions on improving state’s fragility. The results indicate that the paper could provide reasonable suggestions for the government in the aspects of when and how to take what kind of interventions to improve state’s fragility.

Method for uncertain multi-attribute decisionmaking with preference information in the form of interval numbers complementary judgment matrix

The uncertain multi-attribute decision-making problems because of the information about attribute weights being known partly, and the decision maker＇s preference information on alternatives taking the form of interval numbers complementary to the judgment matrix, are investigated. First, the decision-making information, based on the subjective uncertain complementary preference matrix on alternatives is made uniform by using a translation function, and then an objective programming model is established. The attribute weights are obtained by solving the model, thus the overall values of the alternatives are gained by using the additive weighting method. Second, the alternatives are ranked, by using the continuous ordered weighted averaging （C-OWA） operator. A new approach to the uncertain multi-attribute decision-making problems, with uncertain preference information on alternatives is proposed. It is characterized by simple operations and can be easily implemented on a computer. Finally, a practical example is illustrated to show the feasibility and availability of the developed method.

On grey relation projection model based on projection pursuit

Multidimensional grey relation projection value can be synthesized as one-dimensional projection value by using projection pursuit model. The larger the projection value is,the better the model. Thus,according to the projection value,the best one can be chosen from the model aggregation. Because projection pursuit modeling based on accelerating genetic algorithm can simplify the implementation procedure of the projection pursuit technique and overcome its complex calculation as well as the difficulty in implementing its program,a new method can be obtained for choosing the best grey relation projection model based on the projection pursuit technique.

Research on the security of China's oil resources supply based on the objective weight method

With the rapid development of China's economy,external dependence on petroleum resources continues to increase,and their security has become an important part of national security.To evaluate the security of China's petroleum resource supply in a scientific and objective manner,this study establishes a corresponding petroleum life-cycle evaluation index system,based on the theory and method of the whole life-cycle security evaluation of mineral resources,and conducts further independence and grey correlation analysis on the indexes for the purpose of evaluating the petroleum risk situation in China,based on relevant public data from the past 10 years.The results show that the overall trend of China's oil risk has a“U”-shaped characteristic of first decreasing and then increasing.Furthermore,the analysis finds that China's mineral resources have been greatly influenced by the domestic production situation and international trade.These results suggest that the security of petroleum supply can be improved by safeguarding international trade in petroleum resources,strengthening the strategic reserves of domestic petroleum resources,and developing new alternative clean energy sources to improve the resilience of petroleum supply security.This study's research methodology is more logical and systematic than traditional methods,and the analysis of the factors is comprehensive and of high application value,providing implications for the establishment of a big data analysis and evaluation index system for oil resource security.

Evaluation of building energy demand forecast models using multi-attribute decision making approach

With the existence of several conventional and advanced building thermal energy demand forecast models to improve the energy efficiency of buildings,it is hard to find an appropriate,convenient,and efficient model.Evaluations based on statistical indexes(MAE,RMSE,MAPE,etc.)that characterize the accuracy of the forecasts do not help in the identification of the efficient building thermal energy demand forecast tool since they do not reflect the efforts entailed in implementation of the forecast model,i.e.,data collection to production/use phase.Hence,this work presents a Gini Index based Measurement of Alternatives and Ranking according to COmpromise Solution(GI-MARCOS),a hybrid Multi Attribute Decision Making(MADM)approach for the identification of the most efficient building energy demand forecast tool.GI-MARCOS employs(i)GI based objective weight method:assigns meaningful objective weights to the attributes in four phases(1:pre-processing,2:implementation,3:post-processing,and 4:use phase)thereby avoiding unnecessary biases in the expert’s opinion on weights and applicable to domains where there is a lack of domain expertise,and(ii)MARCOS:provides a robust and reliable ranking of alternatives in a dynamic environment.A case study with three alternatives evaluated over three to six attributes in four phases of implementation(pre-processing,implementation,post-processing and use)reveals that the use of GI-MARCOS improved the accuracy of alternatives MLR and BM by 6%and 13%,respectively.Moreover,additional validations state that(i)MLR performs best in Phase 1 and 2,while ANN performs best in Phase 3 and 4 with BM providing a mediocre performance in all four phases,(ii)sensitivity analysis:provides robust ranking with interchange of weights across phases and attributes,and(iii)rank correlation:ranks produce by GI-MARCOS has a high correlation with GRA(0.999),COPRAS(0.9786),and ARAS(0.9775).

Weighting IFT algorithm for off-axis quantized kinoforms of binary objects

The results of experiments on the synthesis of the off-axis quantized kinoforms of binary objects with the use of the weighting iterative Fourier transform （WIFT） algorithm are presented. Kinoforms are registered with a liquid-crystal spatial light modulator （SLM）. A simple procedure to introduce the carrier frequency into the structure of an axial kinoform is proposed. An image reconstructed by an off-axis kinoform is free from the noises with the zero and close frequencies caused by the imperfection of both the phase mode of operation of the SLM and the effects of quantization of the registered phase. Data on the diffraction efficiency are also given.

A simple data assimilation method for improving the MODIS LAI time-series data products based on the object analysis and gradient inverse weighted filter

A simple data assimilation method for improving estimation of moderate resolution imaging spectroradiometer （MODIS） leaf area index （LAI） time-series data products based on the gradient inverse weighted filter and object analysis is proposed. The properties and quality control （QC） of MODIS LAI data products are introduced. Also, the gradient inverse weighted filter and object analysis are analyzed. An experiment based on the simple data assimilation method is performed using MODIS LAI data sets from 2000 to 2005 of Guizhou Province in China.