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Shi-Jie Ye

Bio: Shi-Jie Ye is an academic researcher from Chongqing University. The author has contributed to research in topics: Rough set & Soft computing. The author has an hindex of 2, co-authored 3 publications receiving 257 citations.

Papers
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Journal ArticleDOI
TL;DR: Through attribute reduction based on variable precision with rough set, the influence of noise data and weak interdependency data to BP is avoided so the time taken for training is decreased.
Abstract: Precise Short term load forecasting (STLF) plays a significant role in the management of power system of countries and regions on the grounds of insufficient electric energy for increased need. This paper presents an approach of back propagation neural network with rough set (RSBP) for complicated STLF with dynamic and non-linear factors to develop the accuracy of predictions. Through attribute reduction based on variable precision with rough set, the influence of noise data and weak interdependency data to BP is avoided so the time taken for training is decreased. Using load time series from a practical power system, we tested the performance of RSBP by comparing its predictions with that of BP network.

231 citations

Proceedings ArticleDOI
13 Jun 2005
TL;DR: The method is designed that recognizing soft information patterns by establishing the information table based on soft sets theory; at the same time the solutions are proposed corresponding to the different recognition vectors.
Abstract: In this paper, an appropriate definition of soft information is put forward. In order to discover soft sets application in recognizing soft information patterns, firstly analyze the basic definition of soft sets, make use of table to describe soft sets and give the conception of soft sets reduction according to the characters of soft sets. Then, the method is designed that recognizing soft information patterns by establishing the information table based on soft sets theory; at the same time the solutions are proposed corresponding to the different recognition vectors. This method with good maneuverability can operate collaterally and by batch so as to release the difficulty and complexity in information analysis by some extents.

63 citations

Book ChapterDOI
28 May 2006
TL;DR: Through attribution reduction based on variable precision with rough set, the influence of noise data and weak interdependency data to BP is avoided so the time taken for training is decreased.
Abstract: Accurate Short term load forecasting (STLF) plays a significant role in the management of power system of countries and regions on the grounds of insufficient electric energy for increased need. This paper presents an approach of back propagation neural network with rough set (RSBP) for complicated STLF with dynamic and non-linear factors in order to develop the accuracy of predictions. Through attribution reduction based on variable precision with rough set, the influence of noise data and weak interdependency data to BP is avoided so the time taken for training is decreased. Using load time series from a practical system, we tested the accuracy of forecasting in specific days with comparison.

2 citations


Cited by
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Journal ArticleDOI
TL;DR: In this paper an attempt is made to review the various energy demand forecasting models to accurately predict the future energy needs.
Abstract: Energy is vital for sustainable development of any nation – be it social, economic or environment. In the past decade energy consumption has increased exponentially globally. Energy management is crucial for the future economic prosperity and environmental security. Energy is linked to industrial production, agricultural output, health, access to water, population, education, quality of life, etc. Energy demand management is required for proper allocation of the available resources. During the last decade several new techniques are being used for energy demand management to accurately predict the future energy needs. In this paper an attempt is made to review the various energy demand forecasting models. Traditional methods such as time series, regression, econometric, ARIMA as well as soft computing techniques such as fuzzy logic, genetic algorithm, and neural networks are being extensively used for demand side management. Support vector regression, ant colony and particle swarm optimization are new techniques being adopted for energy demand forecasting. Bottom up models such as MARKAL and LEAP are also being used at the national and regional level for energy demand management.

1,002 citations

Journal ArticleDOI
TL;DR: The properties of fuzzy soft sets as defined and studied in the work of Maji et al. (2001), Roy and Maji (2007), and Yang and Yang (2007) are supported.
Abstract: We further contribute to the properties of fuzzy soft sets as defined and studied in the work of Maji et al. (2001), Roy and Maji (2007), and Yang et al. (2007) and support them with examples and counterexamples. We improve Proposition 3.3 by Maji et al., (2001). Finally we define arbitrary fuzzy soft union and fuzzy soft intersection and prove DeMorgan Inclusions and DeMorgan Laws in Fuzzy Soft Set Theory.

745 citations

Journal ArticleDOI
TL;DR: An uni-int decision making method which selects a set of optimum elements from the alternatives is constructed which shows that the method can be successfully applied to many problems that contain uncertainties.

622 citations

Journal ArticleDOI
TL;DR: This work defines soft matrices and their operations which are more functional to make theoretical studies in the soft set theory and constructs a soft max-min decision making method which can be successfully applied to the problems that contain uncertainties.
Abstract: In this work, we define soft matrices and their operations which are more functional to make theoretical studies in the soft set theory We then define products of soft matrices and their properties We finally construct a soft max-min decision making method which can be successfully applied to the problems that contain uncertainties

433 citations

Journal ArticleDOI
TL;DR: A Back Propagation neural network based on Particle Swam Optimization that combines PSO-BP with comprehensive parameter selection is introduced that achieves much better forecast performance than the basic back propagation neural network and ARIMA model.
Abstract: As a clean and renewable energy source, wind energy has been increasingly gaining global attention. Wind speed forecast is of great significance for wind energy domain: planning and design of wind farms, wind farm operation control, wind power prediction, power grid operation scheduling, and more. Many wind speed forecasting algorithms have been proposed to improve prediction accuracy. Few of them, however, have studied how to select input parameters carefully to achieve desired results. After introducing a Back Propagation neural network based on Particle Swam Optimization (PSO-BP), this paper details a method called IS-PSO-BP that combines PSO-BP with comprehensive parameter selection. The IS-PSO-BP is short for Input parameter Selection (IS)-PSO-BP, where IS stands for Input parameter Selection. To evaluate the forecast performance of proposed approach, this paper uses daily average wind speed data of Jiuquan and 6-hourly wind speed data of Yumen, Gansu of China from 2001 to 2006 as a case study. The experiment results clearly show that for these two particular datasets, the proposed method achieves much better forecast performance than the basic back propagation neural network and ARIMA model.

419 citations