Research of Least Square Support Vector Machine Based on Chaotic Time Series in Power Load Forecasting Model

2006 ◽  
pp. 984-993 ◽  
Author(s):  
Wei Sun ◽  
Chenguang Yang
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Load Forecasting ◽  
Chaotic Time Series ◽  
Least Square ◽  
Support Vector ◽  
Forecasting Model ◽  
Power Load ◽  
Power Load Forecasting

scholarly journals Research on Regional Short-Term Power Load Forecasting Model and Case Analysis

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1617
Author(s):  
Kang Qian ◽  
Xinyi Wang ◽  
Yue Yuan
Keyword(s):  
Meteorological Factors ◽  
Load Forecasting ◽  
Weighted Average ◽  
Support Vector ◽  
Forecasting Model ◽  
Electric Load ◽  
Short Term ◽  
Power Load ◽  
Power Load Forecasting ◽  
Electric 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.

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