The probabilistic model and forecasting of power load based on JMAP-ML and Gaussian process

2018 ◽  
Vol 22 (S4) ◽  
pp. 8589-8596
Author(s):  
Wengen Gao ◽  
Qigong Chen ◽  
Yuan Ge ◽  
YiQing Huang
Keyword(s):  
Gaussian Process ◽  
Probabilistic Model ◽  
Power Load

scholarly journals Gaussian Process Autoregression for Joint Angle Prediction Based on sEMG Signals

2021 ◽  
Vol 9 ◽  
Author(s):  
Jie Liang ◽  
Zhengyi Shi ◽  
Feifei Zhu ◽  
Wenxin Chen ◽  
Xin Chen ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Probabilistic Model ◽  
Autoregressive Model ◽  
Process Model ◽  
Joint Angle ◽  
Gaussian Process Model ◽  
The Mean ◽  
Test Scenarios ◽  
Semg Signals ◽  
Non Parametric

There is uncertainty in the neuromusculoskeletal system, and deterministic models cannot describe this significant presence of uncertainty, affecting the accuracy of model predictions. In this paper, a knee joint angle prediction model based on surface electromyography (sEMG) signals is proposed. To address the instability of EMG signals and the uncertainty of the neuromusculoskeletal system, a non-parametric probabilistic model is developed using a Gaussian process model combined with the physiological properties of muscle activation. Since the neuromusculoskeletal system is a dynamic system, the Gaussian process model is further combined with a non-linear autoregressive with eXogenous inputs (NARX) model to create a Gaussian process autoregression model. In this paper, the normalized root mean square error (NRMSE) and the correlation coefficient (CC) are compared between the joint angle prediction results of the Gaussian process autoregressive model prediction and the actual joint angle under three test scenarios: speed-dependent, multi-speed and speed-independent. The mean of NRMSE and the mean of CC for all test scenarios in the healthy subjects dataset and the hemiplegic patients dataset outperform the results of the Gaussian process model, with significant differences (p < 0.05 and p < 0.05, p < 0.05 and p < 0.05). From the perspective of uncertainty, a non-parametric probabilistic model for joint angle prediction is established by using Gaussian process autoregressive model to achieve accurate prediction of human movement.

Power Load Forecasting based on Multi-task Gaussian Process

2014 ◽  
Vol 47 (3) ◽  
pp. 3651-3656 ◽  
Author(s):  
Yulai Zhang ◽  
Guiming Luo ◽  
Fuan Pu
Keyword(s):  
Gaussian Process ◽  
Load Forecasting ◽  
Power Load ◽  
Power Load Forecasting

scholarly journals Short-term power load forecasting based on combined kernel Gaussian process hybrid model

2021 ◽  
Vol 256 ◽  
pp. 01009
Author(s):  
Liang Lingyu ◽  
Wenqi Huang ◽  
Zhaojie Dong ◽  
Jiguang Zhao ◽  
Peng Li ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Kernel Function ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Complex Nature ◽  
Load Prediction ◽  
Machine Learning Model ◽  
Power Load ◽  
Electricity Load ◽  
Hybrid Kernel

As one of the countries with the most energy consumption in the world, electricity accounts for a large proportion of the energy supply in our country. According to the national basic policy of energy conservation and emission reduction, it is urgent to realize the intelligent distribution and management of electricity by prediction. Due to the complex nature of electricity load sequences, the traditional model predicts poor results. As a kernel-based machine learning model, Gaussian Process Mixing (GPM) has high predictive accuracy, can multi-modal prediction and output confidence intervals. However, the traditional GPM often uses a single kernel function, and the prediction effect is not optimal. Therefore, this paper will combine a variety of existing kernel to build a new kernel, and use it for load sequence prediction. In the electricity load prediction experiments, the prediction characteristics of the load sequences are first analyzed, and then the prediction is made based on the optimal hybrid kernel function constructed by GPM and compared with the traditional prediction model. The results show that the GPM based on the hybrid kernel is not only superior to the single kernel GPM but also superior to some traditional prediction models such as ridge regression, kernel regression and GP.

Power load probability density forecasting using Gaussian process quantile regression

2018 ◽  
Vol 213 ◽  
pp. 499-509 ◽  
Author(s):  
Yandong Yang ◽  
Shufang Li ◽  
Wenqi Li ◽  
Meijun Qu
Keyword(s):  
Quantile Regression ◽  
Gaussian Process ◽  
Probability Density ◽  
Power Load ◽  
Density Forecasting

Integrated Fractional white Noise as an Alternative to Multifractional Brownian Motion

2007 ◽  
Vol 44 (02) ◽  
pp. 393-408 ◽  
Author(s):  
Allan Sly
Keyword(s):  
Stochastic Process ◽  
Brownian Motion ◽  
White Noise ◽  
Gaussian Process ◽  
Discrete Data ◽  
Hölder Exponent ◽  
Scaling Properties ◽  
Multifractional Brownian Motion ◽  
Local Properties

Multifractional Brownian motion is a Gaussian process which has changing scaling properties generated by varying the local Hölder exponent. We show that multifractional Brownian motion is very sensitive to changes in the selected Hölder exponent and has extreme changes in magnitude. We suggest an alternative stochastic process, called integrated fractional white noise, which retains the important local properties but avoids the undesirable oscillations in magnitude. We also show how the Hölder exponent can be estimated locally from discrete data in this model.

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