{"title":"Electricity Consumption Forecasting Based on Improved BP Neural Network","authors":"Z. Xing-ping, Yuan Jia-hai","doi":"10.1109/ICRMEM.2008.104","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.104","url":null,"abstract":"An improved BP Neural Network with additional momentum and adaptive learning is proposed in the paper to predict the growth rate of electricity consumption in China. Matlab7 is used as modeling tool to design the model. Current year GDP growth, electric power consumption growth and growth rate of secondary industry are taken as input variables while next year electric power consumption growth is predicted. The simulation results are compared with that of traditional BP Neural Network model, which show the feasibility of the model proposed in the paper.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131398443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Performance-Based Regulation Transition Model of Electricity Transmission and Distribution Price in China","authors":"Zeng Ming, Qu Shengming, T. Kuo, Li Na","doi":"10.1109/ICRMEM.2008.80","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.80","url":null,"abstract":"Transmission and distribution networkpsilas natural monopoly characteristic decides it must receive regulation. Based on the analysis of price-cap regulation model, combining with the situation and status of power network in China, this paper put forward a reformative price regulation model, which takes network expansion and service quality into account at the same time. To carry out the transition of regulation method from traditional mode to incentive regulation mode, we studied the setup of initial price in price regulation model. In the end, the problem of quality factor computation was discussed.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131475477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Optimization of Maintenance Strategy for Repair Systems with Different Safe and Reliability Degrees","authors":"Xu-jie Jia, L. Cui","doi":"10.1109/ICRMEM.2008.92","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.92","url":null,"abstract":"In safety-critical systems, components must be highly reliable. Once a component's reliability is lower than the criterion, it will be replaced and transferred to be used in another system which does not need such high reliability. The safety virtual age criterion not only relates with the cost of repair actions, but also with the repair degrees and the systempsilas wearing degrees. In this paper, we considered different repair degrees and studied the best safety virtual age criterion to minimize the cost. Finally some numerical examples are presented to illustrate the results obtained in this paper.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"270 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123114572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Research and Prediction of Coal Price in China","authors":"Shuo Liu, Wen Huang, Guanghong Zhang","doi":"10.1109/ICRMEM.2008.19","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.19","url":null,"abstract":"Since the second half of 2007, the coal price in China has witnessed dramatic growth, the generation cost has gone up and power generation companies have got huge benefit losses. This essay analyzes the major reasons of coal price increase in China, predicts the future tendency of steam coal price and proposes policy suggestion to deal with rapid growth of coal price.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129971316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Externality Identification and Quantification of Transmission Construction Projects","authors":"Jun Dong, Jing Zhang","doi":"10.1109/ICRMEM.2008.58","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.58","url":null,"abstract":"This paper provides the evaluation method and model of the externality of the transmission projects, and identifies both negative and positive externalities, as well as makes quantitative and qualitative analysis of externality. In order to quantify the impact of transmission construction projects on the power flow distribution, power supply and demand, as well as the power price of the neighboring power grid, this paper operates the optimal power flow calculation on the Matlab platform through using the IEEE 30-Node Prototype System, and figures out the construction project's influence on the economic benefits of different power market participants. Based on the existing environmental evaluation study, this paper analyzes how the transmission construction project affects the natural resource and the environment and the daily life of the inhabitants. Taking into account the externality and quantifying the externality when evaluating the investment project can help the government and the investors to make the investment decision which is beneficial to the whole society.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134576226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Oil Consumption and Economic Growth in China: A Multivariate Cointegration Analysis","authors":"Changhong Zhao, Jiahai Yuan, Jian-gang Kang","doi":"10.1109/ICRMEM.2008.65","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.65","url":null,"abstract":"Using an aggregate production model where capital, labor and energy are treated as separate inputs, this paper tests for the existence and direction of causality between output growth and oil consumption in China. Using the Johansen cointegration technique, the empirical findings indicate that there exists long-run cointegration among output, labor, capital and oil consumption in China. Then using a VEC specification, the short-run dynamics of the interested variables are tested; indicating that there exists bilateral Granger-causality running between oil consumption and GDP. We thus further analyze the policy implications.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130961240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Research on Optimization of Generation Companies' Profits Risk Management","authors":"Nansheng Pang, Yingling Shi, Xian Ping","doi":"10.1109/ICRMEM.2008.111","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.111","url":null,"abstract":"Under a market environments, there are a lot of uncertainties, such as fluctuation of power prices, shortage of fuel supply and rising of fuel prices, which make generators encounter serious profits loss risk. For stabilizing profits, it is necessary for generators to establish an efficient and optimal risk management technique mix. By adopting the real option, this paper establishes a real option model in which contract electricity is taken into consideration. The optimal output of generator unit in contract market and spot market is obtained after the simulation, and then generators' profits may be maximized. Besides, by using profits loss insurance included in property insurance for reference, this paper proposes an independent profits loss insurance which is suitable for generating enterprises, and then it is combined with the real option to transfer generators' profits loss risk.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114141720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Error Correcting Markov Chains Study on Medium/Long Term Load Rolling Forecasting of SVM Based on Grey Relational Grade","authors":"D. Niu, Yuan Zhang, Jia-liang Lv, Jian-rong Jia","doi":"10.1109/ICRMEM.2008.31","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.31","url":null,"abstract":"According to the low sample and multifactor impact for long-medium term power load forecasting, the grey relational grade was used in screening factors, the combined model in BP neural network and SVM was established, and the multivariate variables and history load variables were used to roll prediction. The combined predictive values are obviously better than single method. Empirical study showed that the method in this paper is superior to conventional method, so it is worth to be extended and applied.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"191 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116454510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Research on the Effect of Debt Financing in Electric Power Listed Company","authors":"L. Xiao-yan, L. Tao","doi":"10.1109/ICRMEM.2008.46","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.46","url":null,"abstract":"This paper investigates the effect of debt financing in electric power listed company through an empirical research with the regression analysis method. According to the capital structure theory, the effect of debt financing lies in financial leverage effect, the tax shield effect and the company governance effect. Considering the characteristics of the electric power industry, we makes a detailed study relying on the data of electric power listed companies from 2004-2006, which belongs to the security markets of Shanghai and Shenzhen. The empirical results show that, for the companies with strong profitability, raising the debt ratio can contribute to the improvement of the effect of debt fiancing; for the companies with poor profitability, it will cause worse effect. These researches help to make suggestions for improving the effect of financing.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116739875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Model of Power Plant Selection Based on Improved Fuzzy Neural Network","authors":"Yanmei Li, Wei Sun","doi":"10.1109/ICRMEM.2008.43","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.43","url":null,"abstract":"The paper adopts rough set reduction algorithm to reduce the influence factors of power plant selection and eliminate the uncorrelated attribution, through which we can obtain typical samples. After this, adopting fuzzy method to calculate the membership degree of the typical samples, which are looked on as the input of BP Neural Network and the expert values are as the expected output to train the network. Through this way the training speed and accuracy will be improved. In this way, we will obtain the network output namely the evaluation result of the case when we calculate using the trained network. According to the result, we can evaluate and make a decision for power plant selection.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114506909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}