scholarly journals A Novel Hybrid Model Based on Extreme Learning Machine, k-Nearest Neighbor Regression and Wavelet Denoising Applied to Short-Term Electric Load Forecasting

Energies ◽  
2017 ◽  
Vol 10 (5) ◽  
pp. 694 ◽  
Author(s):  
◽  
◽  
Keyword(s):  
Extreme Learning Machine ◽  
Hybrid Model ◽  
Nearest Neighbor ◽  
Load Forecasting ◽  
Wavelet Denoising ◽  
Electric Load ◽  
K Nearest Neighbor ◽  
Short Term ◽  
Model Based ◽  
Learning Machine

scholarly journals Probabilistic Forecasting of Short-Term Electric Load Demand: An Integration Scheme Based on Correlation Analysis and Improved Weighted Extreme Learning Machine

2019 ◽  
Vol 9 (20) ◽  
pp. 4215 ◽  
Author(s):  
Zhengmin Kong ◽  
Zhou Xia ◽  
Yande Cui ◽  
He Lv
Keyword(s):  
Correlation Analysis ◽  
Extreme Learning Machine ◽  
Load Forecasting ◽  
Integration Scheme ◽  
Electric Load ◽  
Probabilistic Forecasting ◽  
Short Term ◽  
Load Demand ◽  
Weighted Extreme Learning Machine ◽  
Learning Machine

Precise prediction of short-term electric load demand is the key for developing power market strategies. Due to the dynamic environment of short-term load forecasting, probabilistic forecasting has become the center of attention for its ability of representing uncertainty. In this paper, an integration scheme mainly composed of correlation analysis and improved weighted extreme learning machine is proposed for probabilistic load forecasting. In this scheme, a novel cooperation of wavelet packet transform and correlation analysis is developed to deal with the data noise. Meanwhile, an improved weighted extreme learning machine with a new switch algorithm is provided to effectively obtain stable forecasting results. The probabilistic forecasting task is then accomplished by generating the confidence intervals with the Gaussian process. The proposed integration scheme, tested by actual data from Global Energy Forecasting Competition, is proved to have a better performance in graphic and numerical results than the other available methods.

scholarly journals Short-term electric load forecasting based on improved Extreme Learning Machine Mode

2021 ◽  
Vol 7 ◽  
pp. 1563-1573
Author(s):  
Jie Yuan ◽  
Lihui Wang ◽  
Yajuan Qiu ◽  
Jing Wang ◽  
He Zhang ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Load Forecasting ◽  
Electric Load ◽  
Short Term ◽  
Learning Machine ◽  
Electric Load Forecasting

Mixed kernel based extreme learning machine for electric load forecasting

2018 ◽  
Vol 312 ◽  
pp. 90-106 ◽  
Author(s):  
Yanhua Chen ◽  
Marius Kloft ◽  
Yi Yang ◽  
Caihong Li ◽  
Lian Li
Keyword(s):  
Extreme Learning Machine ◽  
Load Forecasting ◽  
Electric Load ◽  
Learning Machine ◽  
Electric Load Forecasting

scholarly journals Optimized Kernel Extreme Learning Machine for Myoelectric Pattern Recognition

2018 ◽  
Vol 8 (1) ◽  
pp. 483
Author(s):  
Khairul Anam ◽  
Adel Al-Jumaily
Keyword(s):  
Pattern Recognition ◽  
Extreme Learning Machine ◽  
Healthy Subjects ◽  
Nearest Neighbor ◽  
Experimental Results ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

Myoelectric pattern recognition (MPR) is used to detect user’s intention to achieve a smooth interaction between human and machine. The performance of MPR is influenced by the features extracted and the classifier employed. A kernel extreme learning machine especially radial basis function extreme learning machine (RBF-ELM) has emerged as one of the potential classifiers for MPR. However, RBF-ELM should be optimized to work efficiently. This paper proposed an optimization of RBF-ELM parameters using hybridization of particle swarm optimization (PSO) and a wavelet function. These proposed systems are employed to classify finger movements on the amputees and able-bodied subjects using electromyography signals. The experimental results show that the accuracy of the optimized RBF-ELM is 95.71% and 94.27% in the healthy subjects and the amputees, respectively. Meanwhile, the optimization using PSO only attained the average accuracy of 95.53 %, and 92.55 %, on the healthy subjects and the amputees, respectively. The experimental results also show that SW-RBF-ELM achieved the accuracy that is better than other well-known classifiers such as support vector machine (SVM), linear discriminant analysis (LDA) and k-nearest neighbor (kNN).

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