scholarly journals Uncertain Interval Forecasting for Combined Electricity-Heat-Cooling-Gas Loads in the Integrated Energy System Based on Multi-Task Learning and Multi-Kernel Extreme Learning Machine

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1645
Author(s):  
Haoran Zhao ◽  
Sen Guo
Keyword(s):  
Prediction Model ◽  
Extreme Learning Machine ◽  
Kernel Function ◽  
Energy System ◽  
Superior Performance ◽  
Interval Prediction ◽  
Task Learning ◽  
Integrated Energy System ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

The accurate prediction of electricity-heat-cooling-gas loads on the demand side in the integrated energy system (IES) can provide significant reference for multiple energy planning and stable operation of the IES. This paper combines the multi-task learning (MTL) method, the Bootstrap method, the improved Salp Swarm Algorithm (ISSA) and the multi-kernel extreme learning machine (MKELM) method to establish the uncertain interval prediction model of electricity-heat-cooling-gas loads. The ISSA introduces the dynamic inertia weight and chaotic local searching mechanism into the basic SSA to improve the searching speed and avoid falling into local optimum. The MKELM model is established by combining the RBF kernel function and the Poly kernel function to integrate the superior learning ability and generalization ability of the two functions. Based on the established model, weather, calendar information, social–economic factors, and historical load are selected as the input variables. Through empirical analysis and comparison discussion, we can obtain: (1) the prediction results of workday are better than those on holiday. (2) The Bootstrap-ISSA-MKELM based on the MTL method has superior performance than that based on the STL method. (3) Through comparing discussion, we discover the established uncertain interval prediction model has the superior performance in combined electricity-heat-cooling-gas loads prediction.

Remaining Useful Life Prediction of Lithium-ion Batteries Using Multiple Kernel Extreme Learning Machine

2020 ◽  
Vol 13 ◽  
Author(s):  
Renxiong Liu
Keyword(s):  
Lithium Ion Batteries ◽  
Extreme Learning Machine ◽  
Kernel Function ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Prediction Methods ◽  
Multiple Kernel ◽  
Kernel Extreme Learning Machine ◽  
Useful Life ◽  
Learning Machine

Objective: Lithium-ion batteries are important components used in electric automobiles (EVs), fuel cell EVs and other hybrid EVs. Therefore, it is greatly important to discover its remaining useful life (RUL). Methods: In this paper, a battery RUL prediction approach using multiple kernel extreme learning machine (MKELM) is presented. The MKELM’s kernel keeps diversified by consisting multiple kernel functions including Gaussian kernel function, Polynomial kernel function and Sigmoid kernel function, and every kernel function’s weight and parameter are optimized through differential evolution (DE) algorithm. Results : Battery capacity data measured from NASA Ames Prognostics Center are used to demonstrate the prediction procedure of the proposed approach, and the MKELM is compared with other commonly used prediction methods in terms of absolute error, relative accuracy and mean square error. Conclusion: The prediction results prove that the MKELM approach can accurately predict the battery RUL. Furthermore, a compare experiment is executed to validate that the MKELM method is better than other prediction methods in terms of prediction accuracy.

scholarly journals Pressure Prediction of Coal Slurry Transportation Pipeline Based on Particle Swarm Optimization Kernel Function Extreme Learning Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Xue-cun Yang ◽  
Xiao-ru Yan ◽  
Chun-feng Song
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Extreme Learning Machine ◽  
Kernel Function ◽  
Particle Swarm ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
Coal Slurry ◽  
Swarm Optimization ◽  
Learning Machine

For coal slurry pipeline blockage prediction problem, through the analysis of actual scene, it is determined that the pressure prediction from each measuring point is the premise of pipeline blockage prediction. Kernel function of support vector machine is introduced into extreme learning machine, the parameters are optimized by particle swarm algorithm, and blockage prediction method based on particle swarm optimization kernel function extreme learning machine (PSOKELM) is put forward. The actual test data from HuangLing coal gangue power plant are used for simulation experiments and compared with support vector machine prediction model optimized by particle swarm algorithm (PSOSVM) and kernel function extreme learning machine prediction model (KELM). The results prove that mean square error (MSE) for the prediction model based on PSOKELM is 0.0038 and the correlation coefficient is 0.9955, which is superior to prediction model based on PSOSVM in speed and accuracy and superior to KELM prediction model in accuracy.

scholarly journals Underwater Acoustic Signal Prediction Based on MVMD and Optimized Kernel Extreme Learning Machine

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Hong Yang ◽  
Lipeng Gao ◽  
Guohui Li
Keyword(s):  
Prediction Model ◽  
Extreme Learning Machine ◽  
Acoustic Signal ◽  
Grey Wolf ◽  
Underwater Acoustic ◽  
Signal Prediction ◽  
Mode Decomposition ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Underwater Acoustic Signal

Aiming at the chaotic characteristics of underwater acoustic signal, a prediction model of grey wolf-optimized kernel extreme learning machine (OKELM) based on MVMD is proposed in this paper for short-term prediction of underwater acoustic signals. To solve the problem of K value selection in variational mode decomposition, a new K value selection method MVMD is proposed from the perspective of mutual information, which avoids the blindness of variational mode decomposition (VMD) in the preset modal number. Based on the prediction model of kernel extreme learning machine (KELM), this paper uses grey wolf optimization (GWO) algorithm to optimize and select its regularization parameters and kernel parameters and proposes an optimized kernel extreme learning machine OKELM. To further improve the prediction performance of the model, combined with MVMD, an underwater acoustic signal prediction model based on MVMD-OKELM is established. MVMD-OKELM prediction model is applied to Mackey–Glass chaotic time series prediction and underwater acoustic signal prediction and is compared with ARIMA, EMD-OKELM, and other prediction models. The experimental results show that the proposed MVMD-OKELM prediction model has a higher prediction accuracy and can be effectively applied to the prediction of underwater acoustic signal series.

scholarly journals Progressive Kernel Extreme Learning Machine for Food Image Analysis via Optimal Features from Quality Resilient CNN

2021 ◽  
Vol 11 (20) ◽  
pp. 9562
Author(s):  
Ghalib Ahmed Tahir ◽  
Chu Kiong Loo
Keyword(s):  
Extreme Learning Machine ◽  
Novelty Detection ◽  
Fine Tuning ◽  
Superior Performance ◽  
Selection Strategy ◽  
Research Attention ◽  
Food Ingredients ◽  
Kernel Extreme Learning Machine ◽  
Food Diaries ◽  
Learning Machine

Recently, food recognition has received more research attention for mHealth applications that use automated visual-based methods to assess dietary intake. The goal is to improve the food diaries by addressing the challenges faced by existing methodologies. In addition to the classical challenge of the absence of rigid food structure and intra-class variations, food diaries employing deep networks trained with pristine images are susceptible to quality variations in real-world conditions of image acquisition and transmission. Similarly, existing progressive classifiers that use visual features via a convolutional neural network (CNN) classify food categories and cannot detect food ingredients. We aim to provide a system that selects the optimal subset of features from quality resilient CNNs and subsequently incorporates the parallel type of classification to tackle such challenges. The first progressive classifier recognizes food categories, and its multilabel extension detects food ingredients. Following this idea, after extracting features from the quality resilient category and ingredient CNN models by fine-tuning it on synthetic images generated using the novel online data augmentation method random iterative mixup. Our feature selection strategy uses the Shapley additive explanation (SHAP) values from the gradient explainer to select the best features. Then, novel progressive kernel extreme learning machine (PKELM) is exploited to cater to domain variations due to quality distortions, intra-class variations, and so forth, by remodeling the network structure based on activity value with the nodes. PKELM extension for multilabel classification detects ingredients by employing a bipolar step function to process test output and then selecting the column labels of the resulting matrix with a value of one. Moreover, during online learning, the PKELM novelty detection mechanism can label unlabeled instances and detect noisy samples. Experimental results showed superior performance on an integrated set of measures for seven publicly available food datasets.

scholarly journals Cholesky Factorization Based Online Sequential Multiple Kernel Extreme Learning Machine Algorithm for a Cement Clinker Free Lime Content Prediction Model

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1540
Author(s):  
Pengcheng Zhao ◽  
Ying Chen ◽  
Zhibiao Zhao
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Extreme Learning Machine ◽  
Cholesky Factorization ◽  
Cement Clinker ◽  
Free Lime ◽  
Multiple Kernel ◽  
Kernel Extreme Learning Machine ◽  
Online Prediction ◽  
Learning Machine

Aiming at the difficulty in real-time measuring and the long offline measurement cycle for the content of cement clinker free lime (fCaO), it is very important to build an online prediction model for fCaO content. In this work, on the basis of Cholesky factorization, the online sequential multiple kernel extreme learning machine algorithm (COS-MKELM) is proposed. The LDLT form Cholesky factorization of the matrix is introduced to avoid the large operation amount of inverse matrix calculation. In addition, the stored initial information is utilized to realize online model identification. Then, three regression datasets are used to test the performance of the COS-MKELM algorithm. Finally, an online prediction model for fCaO content is built based on COS-MKELM. Experimental results demonstrate that the fCaO content model improves the performance in terms of learning efficiency, regression accuracy, and generalization ability. In addition, the online prediction model can be corrected in real-time when the production conditions of cement clinker change.

scholarly journals A novel framework for prediction of dam deformation based on extreme learning machine and Lévy flight bat algorithm

2021 ◽  
Author(s):  
Youliang Chen ◽  
Xiangjun Zhang ◽  
Hamed Karimian ◽  
Gang Xiao ◽  
Jinsong Huang
Keyword(s):  
Extreme Learning Machine ◽  
Kernel Function ◽  
Bat Algorithm ◽  
Deformation Monitoring ◽  
Gaussian Kernel ◽  
Lévy Flight ◽  
Levy Flight ◽  
Kernel Extreme Learning Machine ◽  
Dam Deformation ◽  
Learning Machine

Abstract Dam deformation monitoring and prediction are crucial for evaluating the safety of reservoirs. There are several elements that influence dam deformation. However, the mixed effects of these elements are not always linear. Oppose to a single-kernel extreme learning machine, which suffers from poor generalization performance and instability, in this study, we proposed an improved bat algorithm for dam deformation prediction based on a hybrid-kernel extreme learning machine. To improve the learning ability of the global kernel and the generalization ability of the local kernel, we combined the global kernel function (polynomial kernel function) and local kernel function (Gaussian kernel function). Moreover, a Lévy flight bat optimization algorithm (LBA) was proposed to overcome the shortages of bat algorithms. The results showed that our model outperformed other models. This proves that our proposed algorithm and methods can be used in dam deformation monitoring and prediction in different projects and regions.

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