scholarly journals Construction of Power Transmission and Transformation Project Cost Information Platform Based on Big Data Analysis

2022 ◽  
Vol 2146 (1) ◽  
pp. 012004
Author(s):  
Chen Chen ◽  
Kankan Chen ◽  
Xiaoli Chen ◽  
Xuemei Zhu ◽  
Ye Ke
Keyword(s):  
Large Scale ◽  
Power Transmission ◽  
Information Value ◽  
Cost Information ◽  
Cost Index ◽  
Project Cost ◽  
Information Platform ◽  
Data Value ◽  
The Cost ◽  
Information Database

Abstract With the reform of China’s power system, power transmission and transformation project (hereinafter referred to as PTATP) are gradually developing in the direction of integration, informatization, large-scale and systematization. Therefore, the traditional project cost can no longer meet the needs of the society, which requires the project cost based on BD (hereinafter referred to as BD) technology. Through the information platform (hereinafter referred to as IPF), we can collect a lot of information, including policies and regulations database, talent and machine price information database, project cost index database, industry information database, etc., which will provide important support for project cost. Project cost informatization will solve the problems of low information sharing rate, low information value and high information cost, which will more scientifically complete the cost of PTATP. Based on BD technology, we can collect, sort out and analyze the cost information data of PTATP, which will fully explore the data value. Firstly, this paper analyzes the main algorithms needed for project cost. Finally, this paper constructs a PTATP cost IPF based on BD analysis, which will provide accurate countermeasures.

Hybrid Spectral Clustering Algorithm and its Application in Cost Index Estimation

2012 ◽  
Vol 461 ◽  
pp. 695-701
Author(s):  
Bing Chen ◽  
Xue Qin Hu ◽  
Bei Zhan Wang ◽  
Yin Huan Zheng
Keyword(s):  
Cost Estimation ◽  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Estimation Method ◽  
Cost Index ◽  
Actual Case ◽  
Project Cost ◽  
Index Estimation ◽  
The Cost ◽  
Spectral Clustering Algorithm

This paper proposed a new hybrid spectral clustering algorithm in which Mean Impact Value (MIV) was used in the cost dimension reduction. The processing of system implementation is as follows: first, we used BP neural network to determine the principal items materials, and then applied the spectral clustering algorithm to calculate the principal items price according to principal items materials; finally, principal items ratio estimation method has been used to do cost estimation. This paper took the Xiamen project cost station as the actual case and experimental results shown that this algorithm could meet the cost requirements of project cost station both in time efficiency and accuracy through parameters self-adjusting.

Construction Project Cost Information Management Research

2014 ◽  
Vol 919-921 ◽  
pp. 1433-1436
Author(s):  
Zhen Wen
Keyword(s):  
Information Management ◽  
System Integration ◽  
Construction Projects ◽  
Large Scale ◽  
Rapid Development ◽  
Information Service ◽  
Information Management System ◽  
Cost Information ◽  
Project Cost ◽  
Construction Project Cost

The project cost industry got rapid development with large scale development construction projects in china. The government's management departments of project cost, in order to provide the capacity and level of management, the transformation of the mode of management, strengthening the project cost information service function, implement the informatization strategy comprehensively. But in the country, because of the lack of unified project cost information technology planning, and construction standards, resulted in the formation of many "islands of information". Therefore, the article proposes information management system integration of project cost for the management department of project cost to meet the urgent needs of project cost sharing and using.

An Optimization and Planning Method Research to Determine Power Transmission Capacity of Clustering Wind Farms Accounting Risk

2014 ◽  
Vol 678 ◽  
pp. 89-93
Author(s):  
Bai Xiao ◽  
Tian Li Cui ◽  
Gang Mu ◽  
Xiao Jing Dong ◽  
Dong Sheng Dang
Keyword(s):  
Large Scale ◽  
Power Transmission ◽  
Wind Farms ◽  
Transmission Capacity ◽  
Mixed Integer ◽  
Transmission Network ◽  
Expansion Planning ◽  
Risk Cost ◽  
The Stability ◽  
The Cost

Large-scale wind farms connected into main grid is accompanied by the reducing of the stability, reliability and security of the power system operation. So the risk cost becomes one of the important factors influencing the profit of transmission network expansion planning. This paper adopted the method of clustering wind power transmission. The objective function established in this paper reflecting the comprehensive benefits of transmission project defined the Pline (transmission capacity) as variable. In order to maximize the comprehensive benefits of the transmission project, the influence of transmission constructing cost, the congestion loss probably caused by a low transmission capacity as well as the cost of risk were taken into account. Calculation of risk is a very complex mixed integer nonlinear problem, so we used Monte-Carlo Simulation to solve this question. Example shoes that the proposed method can achieve optimal transmission capacity, transmission project benefit and planning.

Research on Neural Network Prediction of Power Transmission and Transformation Project Cost Based on GA-RBF and PSO-RBF

2014 ◽  
Vol 644-650 ◽  
pp. 2526-2531 ◽  
Author(s):  
Dong Xiao Niu ◽  
Fu Yu Hua ◽  
Bing Jie Li ◽  
Ming Fu ◽  
Xiao Yan Yu ◽  
...  
Keyword(s):  
Neural Network ◽  
Construction Projects ◽  
Power Transmission ◽  
Rbf Neural Network ◽  
Unit Length ◽  
Project Cost ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Line Construction ◽  
The Cost

This paper’s primary mission is to predict the cost of power transmission and transformation projects of a certain China’s province based on GA-RBF and PSO-RBF neural network. The projects’ data is divided into two main categories-power transformation projects and power line construction projects, with the cost per capacity (RMB/kVA) and cost per unit length (RMB/km) as the indicators of each category. After filtering out main influencing factors and initialization processing for the data, the obtained normalized data can be put into GA-RBF and PSO-RBF predicting model. The empirical analysis is carried on by Matlab. The prediction accuracy can be compared intuitively based on the output of neural network, and from the results we can conclude that GA-RBF is more precise than PSO-RBF when applied to project cost prediction.

scholarly journals Prediction Technology of Power Transmission and Transformation Project Cost Based on the Decomposition-Integration

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yan Lu ◽  
Dongxiao Niu ◽  
Jinpeng Qiu ◽  
Weidong Liu
Keyword(s):  
Power Transmission ◽  
Prediction Algorithm ◽  
Good Effect ◽  
Project Cost ◽  
Total Cost ◽  
Purchase Cost ◽  
Prediction Module ◽  
The Cost ◽  
Cost Characteristics ◽  
Installation Cost

Through the analysis of power transmission and transformation project cost, total cost can be decomposed into construction cost, equipment purchase cost, installation cost, and other costs. This paper proposes a decomposition-integration cost prediction model taking a substation project as an example by fully considering the cost characteristics. In decomposition module, the total cost is decomposed into four expenses. In prediction module, different forecasting models are selected to forecast different expense. In integrated module, choose different integration methods to get the predicting results of total cost. The empirical results show that decomposition-integration prediction algorithm has good effect which can effectively predict the cost of power transmission and transformation project and has practical application and popularization value.

Cost- und Monitoring-Projekt: Zwei neue forstliche Meinungsumfragen im Vergleich | The Cost- and Monitoring-Projects: A Comparison of Two New Forestry Opinion Polls

2000 ◽  
Vol 151 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Stephan Wild-Eck ◽  
Willi Zimmermann
Keyword(s):  
Large Scale ◽  
Forest Policy ◽  
Forest Economics ◽  
Positive Attitudes ◽  
Federal Institute ◽  
Swiss Confederation ◽  
Institute Of Technology ◽  
The Cost ◽  
Near Future ◽  
Opinion Polls

Two large-scale surveys looking at attitudes towards forests, forestry and forest policy in the second half ofthe nineties have been carried out. This work was done on behalf of the Swiss Confederation by the Chair of Forest Policy and Forest Economics of the Federal Institute of Technology (ETH) in Zurich. Not only did the two studies use very different methods, but the results also varied greatly as far as infrastructure and basic conditions were concerned. One of the main differences between the two studies was the fact that the first dealt only with mountainous areas, whereas the second was carried out on the whole Swiss population. The results of the studies reflect these differences:each produced its own specific findings. Where the same (or similar) questions were asked, the answers highlight not only how the attitudes of those questioned differ, but also views that they hold in common. Both surveys showed positive attitudes towards forests in general, as well as a deep-seated appreciation ofthe forest as a recreational area, and a positive approach to tending. Detailed results of the two surveys will be available in the near future.

Waste minimization program in Shuaiba Industrial Area

1999 ◽  
Vol 39 (10-11) ◽  
pp. 289-295
Author(s):  
Saleh Al-Muzaini
Keyword(s):  
Solid Waste ◽  
Power Plants ◽  
Large Scale ◽  
Industrial Area ◽  
Waste Minimization ◽  
Regulatory Body ◽  
Eastern Sector ◽  
Western Sector ◽  
Petroleum Companies ◽  
The Cost

The Shuaiba Industrial Area (SIA) is located about 50 km south of Kuwait City. It accommodates most of the large-scale industries in Kuwait. The total area of the SIA (both eastern and western sectors) is about 22.98 million m2. Fifteen plants are located in the eastern sector and 23 in the western sector, including two petrochemical companies, three refineries, two power plants, a melamine company, an industrial gas corporation, a paper products company and, two steam electricity generating stations, in addition to several other industries. Therefore, only 30 percent of the land in the SIA's eastern sector and 70 percent of land in the SIA's western sector is available for future expansion. Presently, industries in the SIA generate approximately 204,000 t of solid waste. With future development in the industries in the SIA, the estimated quantities will reach 240,000 t. The Shuaiba Area Authority (SAA), a governmental regulatory body responsible for planning and development in the SIA, has recognized the problem of solid waste and has developed an industrial waste minimization program. This program would help to reduce the quantity of waste generated within the SIA and thereby reduce the cost of waste management. This paper presents a description of the waste minimization program and how it is to be implemented by major petroleum companies. The protocols employed in the waste minimization program are detailed.

An Out-of-Core Dataflow Middleware to Reduce the Cost of Large Scale Iterative Solvers

2012 ◽  
Author(s):  
Zheng Zhou ◽  
Erik Saule ◽  
Hasan Metin Aktulga ◽  
Chao Yang ◽  
Esmond G. Ng ◽  
...  
Keyword(s):  
Large Scale ◽  
Iterative Solvers ◽  
The Cost

scholarly journals A Survey on Contrastive Self-Supervised Learning

Technologies ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 2
Author(s):  
Ashish Jaiswal ◽  
Ashwin Ramesh Babu ◽  
Mohammad Zaki Zadeh ◽  
Debapriya Banerjee ◽  
Fillia Makedon
Keyword(s):  
Computer Vision ◽  
Supervised Learning ◽  
Language Processing ◽  
Large Scale ◽  
Performance Comparison ◽  
Extensive Review ◽  
Future Directions ◽  
Dominant Component ◽  
Supervised Methods ◽  
The Cost

Self-supervised learning has gained popularity because of its ability to avoid the cost of annotating large-scale datasets. It is capable of adopting self-defined pseudolabels as supervision and use the learned representations for several downstream tasks. Specifically, contrastive learning has recently become a dominant component in self-supervised learning for computer vision, natural language processing (NLP), and other domains. It aims at embedding augmented versions of the same sample close to each other while trying to push away embeddings from different samples. This paper provides an extensive review of self-supervised methods that follow the contrastive approach. The work explains commonly used pretext tasks in a contrastive learning setup, followed by different architectures that have been proposed so far. Next, we present a performance comparison of different methods for multiple downstream tasks such as image classification, object detection, and action recognition. Finally, we conclude with the limitations of the current methods and the need for further techniques and future directions to make meaningful progress.

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