Hybrid load forecasting method based on fuzzy support vector machine and linear extrapolation

2012 ◽  
Author(s):  
Xin Jiang ◽  
Xiao-Hua Liu ◽  
Rong Gao
Keyword(s):  
Support Vector Machine ◽  
Load Forecasting ◽  
Support Vector ◽  
Linear Extrapolation ◽  
Fuzzy Support Vector Machine ◽  
Forecasting Method

scholarly journals Uncertain Machine Load Forecasting Based on Least Squares Support Vector Machine

2021 ◽  
Author(s):  
Qiaofeng Meng
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Strong Correlation ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Load Forecasting ◽  
Support Vector ◽  
Shop Scheduling ◽  
Machine Load ◽  
Forecasting Method

Machine state is a very important constraint for job shop scheduling. For the uncertainty machine state, the paper proposes a machine load forecasting method based on support vector machine. The method reduces complexity and improves efficiency by eliminating a large number of unrelated input factors and selecting a small number of input parameters with strong correlation. The efficiency of the algorithm is verified by the production workshop instance.

A Short-Term Load Forecasting Method of Warship Based on PSO-SVM Method

2011 ◽  
Vol 127 ◽  
pp. 569-574
Author(s):  
Dong Liang Li ◽  
Xiao Feng Zhang ◽  
Ming Zhong Qiao ◽  
Gang Cheng
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Load Forecasting ◽  
Support Vector ◽  
Short Term ◽  
Swarm Optimization ◽  
Power Load ◽  
Forecasting Method ◽  
Short Term Load Forecasting ◽  
Adaptive Pso

The power load characteristics of warship on a specific task was analyzed,and a task-based forecasting method for warship short-term load forecasting was presented. the new influencing factors of warship power load were used in modeling which is different with the land grid and civilian vessels grid. Theory of particle swarm optimization and Support vector machine was disscused first, and the method of particle swarm optimization was improved to have the ability of adaptive parameter optimization. and the method of support vector machine was improved by the adaptive PSO optimizational method. then a new adaptive short-term load forecasting model was established by the adaptive PSO-SVM method. finally Through simulation results show that the adaptive PSO-SVM method is highly feasible to predict with high accuracy and high generalization capability.

An Improved Short-Term Load Forecasting Method of Warship

2011 ◽  
Vol 127 ◽  
pp. 575-581
Author(s):  
Dong Liang Li ◽  
Xiao Feng Zhang ◽  
Ming Zhong Qiao ◽  
Gang Cheng
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Load Forecasting ◽  
Support Vector ◽  
Short Term ◽  
Swarm Optimization ◽  
Power Load ◽  
Forecasting Method ◽  
Short Term Load Forecasting ◽  
Adaptive Pso

The power load characteristics of warship on a specific task was analyzed,and a task-based forecasting method for warship short-term load forecasting was presented. the new influencing factors of warship power load were used in modeling which is different with the land grid and civilian vessels grid. Theory of particle swarm optimization and Support vector machine was disscused first, and the method of particle swarm optimization was improved to have the ability of adaptive parameter optimization. and the method of support vector machine was improved by the adaptive PSO optimizational method. then a new adaptive short-term load forecasting model was established by the adaptive PSO-SVM method. finally Through simulation results show that the adaptive PSO-SVM method is highly feasible to predict with high accuracy and high generalization capability.

APPLICATION OF A HYBRID OF LEAST SQUARE SUPPORT VECTOR MACHINE AND ARTIFICIAL BEE COLONY FOR BUILDING LOAD FORECASTING

2016 ◽  
Vol 78 (6-2) ◽  
Author(s):  
Mohammad Azhar Mat Daut ◽  
Mohammad Yusri Hassan ◽  
Hayati Abdullah ◽  
Hasimah Abdul Rahman ◽  
Md Pauzi Abdullah ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Hybrid Method ◽  
Artificial Bee Colony ◽  
Load Forecasting ◽  
Least Square ◽  
Electricity Production ◽  
Support Vector ◽  
Bee Colony ◽  
Forecasting Method ◽  
Building Load

Accurate load forecasting is an important element for proper planning and management of electricity production. Although load forecasting has been an important area of research, methods for accurate load forecasting is still scarce in the literature. This paper presents a study on a hybrid load forecasting method that combines the Least Square Support Vector Machine (LSSVM) and Artificial Bee Colony (ABC) methods for building load forecasting. The performance of the LSSVM-ABC hybrid method was compared to the LSSVM method in building load forecasting problems and the results has shown that the hybrid method is able to substantially improve the load forecasting ability of the LSSVM method.

Spatial Electric Load Forecasting Based on Least Squares Support Vector

2014 ◽  
Vol 986-987 ◽  
pp. 542-545 ◽  
Author(s):  
Yan Bin Li ◽  
Yun Li ◽  
Le Cao ◽  
Wei Guo Li
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Artificial Neural Network Model ◽  
Linear Equations ◽  
Load Forecasting ◽  
Regression Coefficients ◽  
Support Vector ◽  
Electric Load ◽  
Forecasting Method ◽  
Quadratic Programming Problems

This paper proposes a new spatial load forecasting method for distribution network based on least squares support vector machine. The method adopt data, the characteristic of which is similar with forecast sample, to training in order to obtain the regression coefficients and bias, which we need to do the forecasting.Atthe same time,compare with artificial neural network model,The least squares support vector machine transforms quadratic programming problems into linear equations, thus avoiding the insensitive loss function, greatly reducing the computational complexity and further improving the accuracy of the prediction model. Finally, the effectiveness and practicality are verified by examples.

scholarly journals Clifford Fuzzy Support Vector Machine for Regression and Its Application in Electric Load Forecasting of Energy System

2021 ◽  
Vol 9 ◽  
Author(s):  
Rui Wang ◽  
Xiaoyi Xia ◽  
Yanping Li ◽  
Wenming Cao
Keyword(s):  
Support Vector Machine ◽  
Support Vector Regression ◽  
Geometric Algebra ◽  
Load Forecasting ◽  
Energy System ◽  
Support Vector ◽  
Electric Load ◽  
Fuzzy Support Vector Machine ◽  
Data Set ◽  
Electric Load Forecasting

Electric load forecasting is a prominent topic in energy research. Support vector regression (SVR) has extensively and successfully achieved good performance in electric load forecasting. Clifford support vector regression (CSVR) realizes multiple outputs by the Clifford geometric algebra which can be used in multistep forecasting of electric load. However, the effect of input is different from the forecasting value. Since the load forecasting value affects the energy reserve and distribution in the energy system, the accuracy is important in electric load forecasting. In this study, a fuzzy support vector machine is proposed based on geometric algebra named Clifford fuzzy support vector machine for regression (CFSVR). Through fuzzy membership, different input points have different contributions to deciding the optimal regression hyperplane. We evaluate the performance of the proposed CFSVR in fitting tasks on numerical simulation, UCI data set and signal data set, and forecasting tasks on electric load data set and NN3 data set. The result of the experiment indicates that Clifford fuzzy support vector machine for regression has better performance than CSVR and SVR of other algorithms which can improve the accuracy of electric load forecasting and achieve multistep forecasting.

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