A novel hybrid approach based on variational heteroscedastic Gaussian process regression for multi-step ahead wind speed forecasting

2022 ◽  
Vol 136 ◽  
pp. 107717
Author(s):  
Chu Zhang ◽  
Tian Peng ◽  
Muhammad Shahzad Nazir
Keyword(s):  
Wind Speed ◽  
Gaussian Process ◽  
Hybrid Approach ◽  
Gaussian Process Regression ◽  
Wind Speed Forecasting

A Gaussian process regression based hybrid approach for short-term wind speed prediction

2016 ◽  
Vol 126 ◽  
pp. 1084-1092 ◽  
Author(s):  
Chi Zhang ◽  
Haikun Wei ◽  
Xin Zhao ◽  
Tianhong Liu ◽  
Kanjian Zhang
Keyword(s):  
Wind Speed ◽  
Gaussian Process ◽  
Hybrid Approach ◽  
Gaussian Process Regression ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Speed Prediction

scholarly journals A blackbox yield estimation workflow with Gaussian process regression applied to the design of electromagnetic devices

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mona Fuhrländer ◽  
Sebastian Schöps
Keyword(s):  
Gaussian Process ◽  
Surrogate Model ◽  
Hybrid Approach ◽  
Gaussian Process Regression ◽  
Benchmark Problems ◽  
Yield Estimation ◽  
Electromagnetic Devices ◽  
Main Challenge ◽  
Sample Points ◽  
Stochastic Yield

Abstract In this paper an efficient and reliable method for stochastic yield estimation is presented. Since one main challenge of uncertainty quantification is the computational feasibility, we propose a hybrid approach where most of the Monte Carlo sample points are evaluated with a surrogate model, and only a few sample points are reevaluated with the original high fidelity model. Gaussian process regression is a non-intrusive method which is used to build the surrogate model. Without many prerequisites, this gives us not only an approximation of the function value, but also an error indicator that we can use to decide whether a sample point should be reevaluated or not. For two benchmark problems, a dielectrical waveguide and a lowpass filter, the proposed methods outperform classic approaches.

A hybrid approach for multi-step wind speed forecasting based on two-layer decomposition, improved hybrid DE-HHO optimization and KELM

2021 ◽  
Vol 164 ◽  
pp. 211-229 ◽  
Author(s):  
Wenlong Fu ◽  
Kai Zhang ◽  
Kai Wang ◽  
Bin Wen ◽  
Ping Fang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Hybrid Approach ◽  
Wind Speed Forecasting

scholarly journals A Hybrid Wavelet Transform Based Short-Term Wind Speed Forecasting Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jujie Wang
Keyword(s):  
Wavelet Transform ◽  
Wind Speed ◽  
Network Architecture ◽  
Hybrid Approach ◽  
Experimental Simulation ◽  
Wind Speed Forecasting ◽  
Optimal Network ◽  
Partial Autocorrelation Function ◽  
Parks Management ◽  
Hidden Layer

It is important to improve the accuracy of wind speed forecasting for wind parks management and wind power utilization. In this paper, a novel hybrid approach known as WTT-TNN is proposed for wind speed forecasting. In the first step of the approach, a wavelet transform technique (WTT) is used to decompose wind speed into an approximate scale and several detailed scales. In the second step, a two-hidden-layer neural network (TNN) is used to predict both approximated scale and detailed scales, respectively. In order to find the optimal network architecture, the partial autocorrelation function is adopted to determine the number of neurons in the input layer, and an experimental simulation is made to determine the number of neurons within each hidden layer in the modeling process of TNN. Afterwards, the final prediction value can be obtained by the sum of these prediction results. In this study, a WTT is employed to extract these different patterns of the wind speed and make it easier for forecasting. To evaluate the performance of the proposed approach, it is applied to forecast Hexi Corridor of China’s wind speed. Simulation results in four different cases show that the proposed method increases wind speed forecasting accuracy.

scholarly journals Yield Optimization using Hybrid Gaussian Process Regression and a Genetic Multi-Objective Approach

2021 ◽  
Vol 19 ◽  
pp. 41-48
Author(s):  
Mona Fuhrländer ◽  
Sebastian Schöps
Keyword(s):  
Gaussian Process ◽  
Hybrid Approach ◽  
Gaussian Process Regression ◽  
Monte Carlo Analysis ◽  
Computational Effort ◽  
Benchmark Problem ◽  
Yield Optimization ◽  
Multi Objective ◽  
Electromagnetic Devices ◽  
The Impact

Abstract. Quantification and minimization of uncertainty is an important task in the design of electromagnetic devices, which comes with high computational effort. We propose a hybrid approach combining the reliability and accuracy of a Monte Carlo analysis with the efficiency of a surrogate model based on Gaussian Process Regression. We present two optimization approaches. An adaptive Newton-MC to reduce the impact of uncertainty and a genetic multi-objective approach to optimize performance and robustness at the same time. For a dielectrical waveguide, used as a benchmark problem, the proposed methods outperform classic approaches.

Gaussian Process Regression for numerical wind speed prediction enhancement

2020 ◽  
Vol 146 ◽  
pp. 2112-2123 ◽  
Author(s):  
Haoshu Cai ◽  
Xiaodong Jia ◽  
Jianshe Feng ◽  
Wenzhe Li ◽  
Yuan-Ming Hsu ◽  
...  
Keyword(s):  
Wind Speed ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Wind Speed Prediction ◽  
Speed Prediction
Sign in / Sign up

Export Citation Format

Share Document