Power Load Prediction Based on a Hybrid Forecast Algorithm

2013 ◽  
Vol 448-453 ◽  
pp. 2516-2519
Author(s):  
Min Zou ◽  
Huan Qi Tao
Keyword(s):  
Wavelet Transform ◽  
Support Vector Machines ◽  
Electrical Power ◽  
Least Square ◽  
Support Vector ◽  
Load Prediction ◽  
Electrical Power System ◽  
Power Load ◽  
Vector Machines ◽  
Standard Support

Power load prediction is an important task for the electrical power system. The nonstationary, nonlinear and volatile characteristics of power load data make more difficult for the accurate load prediction. This paper presents a hybrid forecast algorithm based on wavelet transform and support vector machines for power load prediction. The hybrid algorithm firstly decomposed the load series to several subseries with obvious tendency by wavelet transform. Then these subseries are forecasted with least square support vector machines (LS-SVM), an extension of standard support vector machines, respectively. Finally these forecast results were reconstructed as the prediction of original power load series. The effective simulation results of above algorithm were testified based on a sample load series.

scholarly journals Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257901
Author(s):  
Yanjing Bi ◽  
Chao Li ◽  
Yannick Benezeth ◽  
Fan Yang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Computer Assisted ◽  
Long Distance ◽  
Relationship Analysis ◽  
Vector Machines

Phoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate that machine learning is a promising method to build high-performance computer-assisted pronunciation training modalities. Many data-driven classifying models, such as support vector machines, back-propagation networks, deep neural networks and convolutional neural networks, are increasingly widely used for it. Yet, the acoustic waveforms of phoneme are essentially modulated from the base vibrations of vocal cords, and this fact somehow makes the predictors collinear, distorting the classifying models. A commonly-used solution to address this issue is to suppressing the collinearity of predictors via partial least square regressing algorithm. It allows to obtain high-quality predictor weighting results via predictor relationship analysis. However, as a linear regressor, the classifiers of this type possess very simple topology structures, constraining the universality of the regressors. For this issue, this paper presents an heterogeneous phoneme recognition framework which can further benefit the phoneme pronunciation diagnostic tasks by combining the partial least square with support vector machines. A French phoneme data set containing 4830 samples is established for the evaluation experiments. The experiments of this paper demonstrates that the new method improves the accuracy performance of the phoneme classifiers by 0.21 − 8.47% comparing to state-of-the-arts with different data training data density.

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