Compare Relevance Vector Machine with Improved Support Vector Machine in 
 Short-term Power Load Forecasting

Integrated energy services will have multiple values and far-reaching significance in promoting energy transformation and serving “carbon peak and carbon neutralization”. In order to balance the supply and demand of power system in integrated energy, it is necessary to establish a scientific model for power load forecasting. Different algorithms for short-term electric load forecasting considering meteorological factors are presented in this paper. The correlation between electric load and meteorological factors is first analyzed. After the principal component analysis (PCA) of meteorological factors and autocorrelation analysis of the electric load, the daily load forecasting model is established by optimal support vector machine (OPT-SVM), Elman neural network (ENN), as well as their combinations through linear weighted average, geometric weighted average, and harmonic weighted average method, respectively. Based on the actual data of an industrial park of Nantong in China, the prediction performance in the four seasons with the different models is evaluated. The main contribution of this paper is to compare the effectiveness of different models for short-term electric load forecasting and to give a guideline to build the proper methods for load forecasting.

Download Full-text

Dual possibilistic regression models of support vector machines and application in power load forecasting

International Journal of Distributed Sensor Networks ◽

10.1177/1550147720921636 ◽

2020 ◽

Vol 16 (5) ◽

pp. 155014772092163

Author(s):

Xianfei Yang ◽

Xiang Yu ◽

Hui Lu

Keyword(s):

Support Vector Machine ◽

Support Vector Machines ◽

Quadratic Programming ◽

Regression Models ◽

Load Forecasting ◽

Interval Data ◽

Support Vector ◽

Power Load ◽

Vector Machines ◽

Power Load Forecasting

Power load forecasting is an important guarantee of safe, stable, and economic operation of power systems. It is appropriate to use interval data to represent fuzzy information in power load forecasting. The dual possibilistic regression models approximate the observed interval data from the outside and inside directions, respectively, which can estimate the inherent uncertainty existing in the given fuzzy phenomenon well. In this article, efficient dual possibilistic regression models of support vector machines based on solving a group of quadratic programming problems are proposed. And each quadratic programming problem containing fewer optimization variables makes the training speed of the proposed approach fast. Compared with other interval regression approaches based on support vector machines, such as quadratic loss support vector machine approach and two smaller quadratic programming problem support vector machine approach, the proposed approach is more efficient on several artificial datasets and power load dataset.

Download Full-text

Power load forecasting based on support vector machine and particle swarm optimization

2016 12th World Congress on Intelligent Control and Automation (WCICA) ◽

10.1109/wcica.2016.7578535 ◽

2016 ◽

Cited By ~ 2

Author(s):

Guanghua Ren ◽

Shiping Wen ◽

Zheng Yan ◽

Rui Hu ◽

Zhigang Zeng ◽

...

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Load Forecasting ◽

Particle Swarm ◽

Support Vector ◽

Swarm Optimization ◽

Power Load ◽

Power Load Forecasting

Download Full-text

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.127.569 ◽

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.

Download Full-text