scholarly journals Research on Prediction Method of Reasonable Cost Level of Transmission Line Project Based on PCA-LSSVM-KDE

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Zhao Xue-hua ◽  
Miao Xu-juan ◽  
Zhang Zhen-gang ◽  
Hao Zheng
Keyword(s):  
Prediction Model ◽  
Transmission Line ◽  
Prediction Interval ◽  
High Reliability ◽  
Prediction Method ◽  
Principal Component ◽  
Support Vector ◽  
Investment Planning ◽  
Project Investment ◽  
Project Cost

In order to reduce the investment risk, the evaluation standard of transmission line project investment planning becomes higher, which puts forward higher requirements for the reasonable level prediction of transmission line project cost. This paper combines principal component analysis (PCA) with the least squares support vector machine (LSSVM) model and establishes a point prediction model for transmission line project cost. Based on the analysis of the error of the point prediction model, the kernel density estimation (KDE) method is innovatively introduced to estimate the prediction error, and the probability density function of the error is obtained. Then, according to different confidence levels, the corresponding cost intervals are obtained, which means that the reasonable level of transmission line project cost is obtained. The results show that the coverage rate of the cost prediction interval under 85% confidence level is 88.57%. This conclusion shows that the model has high reliability and can provide a reliable basis for the evaluation of transmission line project investment planning.

scholarly journals Intelligent Prediction of Transmission Line Project Cost Based on Least Squares Support Vector Machine Optimized by Particle Swarm Optimization

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Tao Yi ◽  
Hao Zheng ◽  
Yu Tian ◽  
Jin-peng Liu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Prediction Model ◽  
Transmission Line ◽  
Least Squares ◽  
Principal Component ◽  
Support Vector ◽  
Swarm Optimization ◽  
Empirical Parameter ◽  
Parameter Prediction

In order to meet the demand of power supply, the construction of transmission line projects is constantly advancing, and the level of cost control is constantly improving, which puts forward higher requirements for the accuracy of cost prediction. This paper proposes an intelligent cost prediction model based on least squares support vector machine (LSSVM) optimized by particle swarm optimization (PSO). Originally extracting natural, technological, and economic indexes from the perspective of cost composition, principal component analysis (PCA) is used to reduce the dimension of indexes. And PSO is innovatively introduced to optimize the parameters of LSSVM model to obtain the optimal parameters. The obtained principal component data are imported into empirical parameter LSSVM prediction model and the optimized parameter PSO-LSSVM prediction model, respectively, for modeling and prediction, and then comparing the prediction results to analyze the effect of model optimization. The results show that the absolute deviation of the optimized parameter prediction model is less than 9%. And the prediction accuracy of the optimized parameter prediction model is better than that of the empirical parameter model, which can provide a reliable basis for investment decision-making of transmission line projects.

Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Input Variables ◽  
Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

Research on Combined Prediction Model for Busy Telephone Traffic

2014 ◽  
Vol 610 ◽  
pp. 789-796
Author(s):  
Jiang Bao Li ◽  
Zhen Hong Jia ◽  
Xi Zhong Qin ◽  
Lei Sheng ◽  
Li Chen
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Low Frequency ◽  
Prediction Method ◽  
Pso Algorithm ◽  
Frequency Component ◽  
Least Square ◽  
High Frequency Component ◽  
Support Vector ◽  
Telephone Traffic

In order to improve the prediction accuracy of busy telephone traffic, this study proposes a busy telephone traffic prediction method that combines wavelet transformation and least square support vector machine (lssvm) model which is optimized by particle swarm optimization (pso) algorithm. Firstly, decompose the pretreatment of busy telephone traffic data with mallat algorithm and get low frequency component and high frequency component. Secondly, reconfigure each component and use pso_lssvm model predict each reconfigured one. Then the busy telephone traffic can be achieved. The experimental results show that the prediction model has higher prediction accuracy and stability.

scholarly journals Online Monitoring Based on Temperature Field Features and Prediction Model for Selective Laser Sintering Process

2018 ◽  
Vol 8 (12) ◽  
pp. 2383 ◽  
Author(s):  
Zhehan Chen ◽  
Xianhui Zong ◽  
Jing Shi ◽  
Xiaohua Zhang
Keyword(s):  
Temperature Field ◽  
Prediction Model ◽  
Selective Laser Sintering ◽  
Principal Component ◽  
Laser Sintering ◽  
Support Vector ◽  
Sintering Process ◽  
Metal Materials ◽  
Key Features ◽  
The Mathematical Model

Selective laser sintering (SLS) is an additive manufacturing technology that can work with a variety of metal materials, and has been widely employed in many applications. The establishment of a data correlation model through the analysis of temperature field images is a recognized research method to realize the monitoring and quality control of the SLS process. In this paper, the key features of the temperature field in the process are extracted from three levels, and the mathematical model and data structure of the key features are constructed. Feature extraction, dimensional reduction, and parameter optimization are realized based on principal component analysis (PCA) and support vector machine (SVM), and the prediction model is built and optimized. Finally, the feasibility of the proposed algorithms and model is verified by experiments.

scholarly journals Short-Term Photovoltaic Power Interval Prediction Based on the Improved Generalized Error Mixture Distribution and Wavelet Packet -LSSVM

2021 ◽  
Vol 9 ◽  
Author(s):  
Mao Yang ◽  
Tian Peng ◽  
Xin Su ◽  
Miaomiao Ma
Keyword(s):  
Prediction Interval ◽  
Wavelet Packet ◽  
Mixture Distribution ◽  
Prediction Method ◽  
Support Vector ◽  
Average Width ◽  
Efficient Operation ◽  
Confidence Levels ◽  
Interval Prediction ◽  
Probability Prediction

The periodicity and non-stationary nature of photovoltaic (PV) output power make the point prediction result contain very little information, increase the difficulty of describing the prediction uncertainty, and it is difficult to ensure the most efficient operation of the power system. Effectively predicting the PV power range will greatly improve the economics and stability of the grid. Therefore, this paper proposes an improved generalized based on the combination of wavelet packet (WP) and least squares support vector machine (LSSVM) to obtain higher accuracy point prediction results. The error mixed distribution function is used to fit the probability distribution of the prediction error, and the probability prediction is performed to obtain the prediction interval. The coverage rate and average width of the prediction interval are used as indicators to evaluate the prediction results of the interval. By comparing with the results of conventional methods based on normal distribution, at 95 and 90% confidence levels, the method proposed in this paper achieves higher coverage while reducing the average bandwidth by 5.238 and 3.756%, which verifies the effectiveness of the proposed probability interval prediction method.

scholarly journals Stability Analysis of Rock Slope Based on Improved Principal Component Analysis Model: Taking Fuwushan Slope as an Example

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Lihua Huang ◽  
Liudan Mao ◽  
YiRong Zhu ◽  
YuLing Wang
Keyword(s):  
Principal Component Analysis ◽  
Slope Stability ◽  
Prediction Model ◽  
Relative Error ◽  
Rock Slope ◽  
High Reliability ◽  
Principal Component ◽  
Component Analysis ◽  
Rock Slope Stability ◽  
Maximum Relative Error

Aiming at the problems of low accuracy, low efficiency, and many parameters required in the current calculation of rock slope stability, a prediction model of rock slope stability is proposed, which combines principal component analysis (PCA) and relevance vector machine (RVM). In this model, PCA is used to reduce the dimension of several influencing factors, and four independent principal component variables are selected. With the help of RVM mapping the nonlinear relationship between the safety factor of slope stability and the principal component variables, the prediction model of rock slope stability based on PCA-RVM is established. The results show that under the same sample, the maximum relative error of the PCA-RVM model is only 1.26%, the average relative error is 0.95%, and the mean square error is 0.011, which is far lower than that of the RVM model and the GEP model. By comparing the results of traditional calculation method and PCA-RVM model, it can be concluded that the PCA-RVM model has the characteristics of high prediction accuracy, small discreteness, and high reliability, which provides reference value for accurately predicting the stability of rock slope.

scholarly journals Life prediction of slewing bearing based on isometric mapping and fuzzy support vector regression

2019 ◽  
Vol 42 (1) ◽  
pp. 94-103 ◽  
Author(s):  
Weigang Bao ◽  
Hua Wang ◽  
Jie Chen ◽  
Bo Zhang ◽  
Peng Ding ◽  
...  
Keyword(s):  
Prediction Model ◽  
Support Vector Regression ◽  
Life Prediction ◽  
Feature Fusion ◽  
Prediction Method ◽  
Support Vector ◽  
Isometric Mapping ◽  
Slewing Bearing ◽  
Time Frequency ◽  
Life Prediction Model

The monitoring data of slewing bearing is massive. In order to establish accurate life prediction model from complex vibration signal of slewing bearing, a life prediction method based on manifold learning and fuzzy support vector regression (SVR) is proposed. Firstly, the multiple features are extracted from time domain and time-frequency domain. Then isometric mapping (ISOMAP) is used to reduce high-dimensional features to low-dimensional features that can reflect degeneration of slewing bearing well. Finally, the fuzzy SVR is used to predict the life degradation trend of slewing bearing. The results show that: (1) Multi-feature fusion after ISOMAP can obtain more comprehensive degradation indicator. (2) The complexity of the life prediction model is simplified and the real-time life degradation trend of slewing bearing can be well predicted by fuzzy SVR, so it is very suitable to predict life degradation trend of slewing bearing based on massive data well. The time of prediction on average is reduced by 72.7%. The mean absolute error (MAE) and root mean square error (RMSE) of prediction are reduced by 73% and 59% respectively compared with traditional methods. The accuracy of prediction is greatly improved.

Prediction and Analysis of Strawberry Moisture Content based on BP Neural Network Model

2020 ◽  
Vol 13 (4) ◽  
pp. 657-671
Author(s):  
Wei Jiang ◽  
Hongmei Xu ◽  
Elnaz Akbari ◽  
Jiang Wen ◽  
Shuang Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Moisture Content ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Principal Component ◽  
Spectral Difference ◽  
Model Based ◽  
Neural Network Prediction ◽  
Network Prediction

Background: Moisture content is one of the most important indicators for the quality of fresh strawberries. Currently, several methods are usually employed to detect the moisture content in strawberry. However, these methods are relatively simple and can only be used to detect the moisture content of single samples but not batches of samples. Besides, the integrity of the samples may be destroyed. Therefore, it is important to develop a simple and efficient prediction method for strawberry moisture to facilitate the market circulation of strawberry. Objective: This study aims to establish a novel BP neural network prediction model to predict and analyze strawberry moisture. Methods: Toyonoka and Jingyao strawberries were taken as the research objects. The hyperspectral technology, spectral difference analysis, correlation coefficient method, principal component analysis and artificial neural network technology were combined to predict the moisture content of strawberry. Results: The characteristic wavelengths were highly correlated with the strawberry moisture content. The stability and prediction effect of the BP neural network prediction model based on characteristic wavelengths are superior to those of the prediction model based on principal components, and the correlation coefficients of the calibration set for Toyonaka and Jingyao respectively reached up to 0.9532 and 0.9846 with low levels of standard deviations (0.3204 and 0.3010, respectively). Conclusion: The BP neural network prediction model of strawberry moisture has certain practicability and can provide some reference for the on-line and non-destructive detection of fruits and vegetables.

Tension Prediction Model of Ice-Coated Transmission Line Based on YULE-WALK Auto-Regressive Model and Support Vector Machine

2013 ◽  
Vol 437 ◽  
pp. 331-334
Author(s):  
Lei Yang ◽  
Da Da Wang ◽  
Xin Wu ◽  
Lin Li ◽  
Xiao Ming Rui ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Power Systems ◽  
Prediction Model ◽  
Transmission Line ◽  
Break Line ◽  
Support Vector ◽  
Absolute Maximum ◽  
Regressive Model ◽  
Auto Regressive ◽  
Auto Regressive Model

Large tension of ice-coated transmission line will cause line overload and conductor galloping, accidents such as break line and tower collapse will be caused, it bring great threat to safety and stability of power systems. Therefore, there is an important physical meaning for preventing above accidents to in-depth study tension prediction model of ice-coated transmission line.In this paper,we establishes a tension prediction model of ice-coated transmission line based on the Yule-Wake auto-regressive model and support vector machine, the model contains the micrometeorological and tension historical data, etc. Through studying the tension prediction of Gan-Zhen 155# transmission line in Zhaotong area of Yunnan province,it shows the prediction obtained by this model in the next eight hours is in accord with the actual monitoring data pretty well, the absolute maximum error is less than 5.86%, and the maximum absolute mean error is less than 2.74%.So, the feasibility and accuracy of this model is verified.

Adaptive Kalman Filter Based Aircraft Ground Icing Thickness Prediction

2012 ◽  
Vol 562-564 ◽  
pp. 1660-1667
Author(s):  
Zhi Wei Xing ◽  
Hui Zhang ◽  
Zhun Ren
Keyword(s):  
Kalman Filter ◽  
Prediction Model ◽  
Prediction Method ◽  
Theory And Practice ◽  
Filter Method ◽  
Support Vector ◽  
Aircraft Icing ◽  
Adaptive Kalman Filter ◽  
Thickness Prediction ◽  
Kalman Filter Method

The nonlinear dynamics model is used to describe the change of aircraft icing thickness and icing deformation accelerations is viewed as dynamic noise in this paper. Then, a dynamic prediction model of aircraft icing thickness is established with the theory of adaptive kalman filter. And the adaptive kalman filter method based aircraft icing thickness prediction model is employed to forecast aircraft ground icing thickness and compared with support vector machine, BP neural network prediction method. The result of the instance simulation and analysis indicates that the adaptive kalman filter method based aircraft icing thickness prediction posed in this paper is reliable, simple and rapid, and the model has high prediction precision which can realize real-time tracking and prediction and has definite value of both theory and practice.

Sign in / Sign up

Export Citation Format

Share Document