scholarly journals Application of a new information priority accumulated grey model with time power to predict short-term wind turbine capacity

2020 ◽  
Vol 244 ◽  
pp. 118573 ◽  
Author(s):  
Jie Xia ◽  
Xin Ma ◽  
Wenqing Wu ◽  
Baolian Huang ◽  
Wanpeng Li
Keyword(s):  
Wind Turbine ◽  
Grey Model ◽  
Short Term ◽  
New Information ◽  
Turbine Capacity

A novel fractional discrete nonlinear grey Bernoulli model for forecasting the wind turbine capacity of China

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wen-ze Wu ◽  
Wanli Xie ◽  
Chong Liu ◽  
Tao Zhang
Keyword(s):  
Wind Turbine ◽  
Nonlinear Coefficient ◽  
Predictive Performance ◽  
Prediction Performance ◽  
New Method ◽  
Grey Model ◽  
Content Type ◽  
Proposed Model ◽  
Whale Optimization ◽  
Turbine Capacity

PurposeA new method for forecasting wind turbine capacity of China is proposed through grey modelling technique.Design/methodology/approachFirst of all, the concepts of discrete grey model are introduced into the NGBM(1,1) model to reduce the discretization error from the differential equation to its discrete forms. Then incorporating the conformable fractional accumulation into the discrete NGBM(1,1) model is carried out to further improve the predictive performance. Finally, in order to effectively seek the emerging coefficients, namely, fractional order and nonlinear coefficient, the whale optimization algorithm (WOA) is employed to determine the emerging coefficients.FindingsThe empirical results show that the newly proposed model has a better prediction performance compared to benchmark models; the wind turbine capacity from 2019 to 2021 is expected to reach 275954.42 Megawatts in 2021. According to the forecasts, policy suggestions are provided for policy-makers.Originality/valueBy combing the fractional accumulation and the concepts of discrete grey model, a new method to improve the prediction performance of the NGBM(1,1) model is proposed. The newly proposed model is firstly applied to predict wind turbine capacity of China.

scholarly journals Aerodynamic-Efficiency-Constrained Short-Term Frequency Support Control of Wind Turbine Generators

2020 ◽  
Vol 53 (2) ◽  
pp. 12115-12120
Author(s):  
Zhengyang Zhang ◽  
Zaiyu Chen ◽  
Guoqiang Yu ◽  
Tianhai Zhang ◽  
Minghui Yin ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Short Term ◽  
Aerodynamic Efficiency ◽  
Term Frequency ◽  
Turbine Generators ◽  
Wind Turbine Generators

scholarly journals Utilizing an Adaptive Grey Model for Short-Term Time Series Forecasting: A Case Study of Wafer-Level Packaging

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Che-Jung Chang ◽  
Der-Chiang Li ◽  
Wen-Li Dai ◽  
Chien-Chih Chen
Keyword(s):  
Semiconductor Manufacturing ◽  
Manufacturing System ◽  
Grey System Theory ◽  
Grey Model ◽  
Grey System ◽  
Wafer Level ◽  
Short Term ◽  
Packaging Process ◽  
Wafer Level Packaging

The wafer-level packaging process is an important technology used in semiconductor manufacturing, and how to effectively control this manufacturing system is thus an important issue for packaging firms. One way to aid in this process is to use a forecasting tool. However, the number of observations collected in the early stages of this process is usually too few to use with traditional forecasting techniques, and thus inaccurate results are obtained. One potential solution to this problem is the use of grey system theory, with its feature of small dataset modeling. This study thus uses the AGM(1,1) grey model to solve the problem of forecasting in the pilot run stage of the packaging process. The experimental results show that the grey approach is an appropriate and effective forecasting tool for use with small datasets and that it can be applied to improve the wafer-level packaging process.

scholarly journals Long-term changes in offshore wind power density and wind turbine capacity factor in the Iberian Peninsula (1900–2010)

Energy ◽  
2021 ◽  
Vol 226 ◽  
pp. 120364
Author(s):  
Sheila Carreno-Madinabeitia ◽  
Gabriel Ibarra-Berastegi ◽  
Jon Sáenz ◽  
Alain Ulazia
Keyword(s):  
Wind Turbine ◽  
Iberian Peninsula ◽  
Wind Power ◽  
Power Density ◽  
Capacity Factor ◽  
Offshore Wind ◽  
Offshore Wind Power ◽  
Long Term Changes ◽  
Turbine Capacity

Short-term prediction of UT1-UTC by combination of the grey model and neural networks

2017 ◽  
Vol 59 (2) ◽  
pp. 524-531 ◽  
Author(s):  
Yu Lei ◽  
Min Guo ◽  
Dan-dan Hu ◽  
Hong-bing Cai ◽  
Dan-ning Zhao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Grey Model ◽  
Short Term ◽  
Term Prediction ◽  
Short Term Prediction

Comparison of Spar and Semisubmersible Floater Concepts of Offshore Wind Turbines Using Long-Term Analysis

2015 ◽  
Vol 137 (6) ◽  
Author(s):  
Hasan Bagbanci ◽  
D. Karmakar ◽  
C. Guedes Soares
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Transfer Functions ◽  
Probability Distributions ◽  
Offshore Wind ◽  
Short Term ◽  
Offshore Wind Turbines ◽  
Floating Wind Turbine ◽  
Offshore Floating Wind Turbine

The long-term probability distributions of a spar-type and a semisubmersible-type offshore floating wind turbine response are calculated for surge, heave, and pitch motions along with the side-to-side, fore–aft, and yaw tower base bending moments. The transfer functions for surge, heave, and pitch motions for both spar-type and semisubmersible-type floaters are obtained using the fast code and the results are also compared with the results obtained in an experimental study. The long-term predictions of the most probable maximum values of motion amplitudes are used for design purposes, so as to guarantee the safety of the floating wind turbines against overturning in high waves and wind speed. The long-term distribution is carried out using North Atlantic wave data and the short-term floating wind turbine responses are represented using Rayleigh distributions. The transfer functions are used in the procedure to calculate the variances of the short-term responses. The results obtained for both spar-type and semisubmersible-type offshore floating wind turbine are compared, and the study will be helpful in the assessments of the long-term availability and economic performance of the spar-type and semisubmersible-type offshore floating wind turbine.

scholarly journals Short-Term Memory Comparison Of Students Of Faculty Of Medicine Pelita Harapan University Batch 2015 Between The Handwriting And Typing Method

Medicinus ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 108
Author(s):  
Caroline Widjaja ◽  
Stefanus Satria Sumali
Keyword(s):  
Experimental Study ◽  
Short Term Memory ◽  
P Value ◽  
Short Term ◽  
Typing Method ◽  
Term Memory ◽  
New Information ◽  
Study Population ◽  
The Right ◽  
Experimental Study Design

<p>Introduction : A lot of research has been done to determine if handwriting or typing note influenced short-term memory, however, the results obtained are still controversial. Therefore this study is structured to see the effect of note taking methods by handwriting and typing on short-term memory.</p><p>Aim : The aims of this study were to increase the performance of students in Faculty of Medicine Pelita Harapan University as well as providing the right and effective method of taking notes.<strong></strong></p><p>Method : Experimental study design was chosen in this study. Study population is students of faculty of medicine Pelita Harapan University batch 2015. 40 samples will be divide randomly into two, one group will take a note by handwriting and another by typing. Each group is required to watch a video about 15 minutes long.  The results were analyzed statistically using T-test.</p><p>Result : The average of  new information that can be remembered by group that take a note by handwriting significantly (p&lt;0,05) higher than group than take a note by typing with a p-value of 0,009.</p><p>Conclusion : Take a note by handwriting allows people to remember more new information than typing.</p>

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