scholarly journals Development of ANN Model for Wind Speed Prediction as a Support for Early Warning System

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Ivan Marović ◽  
Ivana Sušanj ◽  
Nevenka Ožanić
Keyword(s):  
Wind Speed ◽  
Prediction Model ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Ann Model ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
The University ◽  
The Impact

The impact of natural disasters increases every year with more casualties and damage to property and the environment. Therefore, it is important to prevent consequences by implementation of the early warning system (EWS) in order to announce the possibility of the harmful phenomena occurrence. In this paper, focus is placed on the implementation of the EWS on the micro location in order to announce possible harmful phenomena occurrence caused by wind. In order to predict such phenomena (wind speed), an artificial neural network (ANN) prediction model is developed. The model is developed on the basis of the input data obtained by local meteorological station on the University of Rijeka campus area in the Republic of Croatia. The prediction model is validated and evaluated by visual and common calculation approaches, after which it was found that it is possible to perform very good wind speed prediction for time steps Δt=1 h, Δt=3 h, and Δt=8 h. The developed model is implemented in the EWS as a decision support for improvement of the existing “procedure plan in a case of the emergency caused by stormy wind or hurricane, snow and occurrence of the ice on the University of Rijeka campus.”

scholarly journals Study on Fault Analysis and early warning system of wind-induced disasters on transmission lines

2020 ◽  
Vol 194 ◽  
pp. 03016
Author(s):  
LIN Fanqin ◽  
JIA Ran ◽  
Yang Jingjing ◽  
CAO Huaming ◽  
LIU Hui ◽  
...  
Keyword(s):  
Wind Speed ◽  
Real Time ◽  
Transmission Lines ◽  
Early Warning ◽  
Early Warning System ◽  
Short Circuit ◽  
Power Grid ◽  
Warning System ◽  
Strong Wind ◽  
The Impact

Wind-induced disaster has become one of the most important disasters affecting the safe operation of power grid. In order to improve the effect of prevention and treatment of wind-induced disasters, it is very important to give early warning and real-time warning. Therefore, based on the climate characteristics of Shandong power grid, this paper presents the wind speed statistical law and the distribution characteristics of the number of days with strong wind. By analysing the mechanism of wind-induced disasters and combining the fault data of five years, the characteristics and rules of wind deviation and foreign short-circuit fault are obtained. On this basis, an early warning model of wind deviation based on the calibration of minimum air gap and the determination of maximum wind speed is established. At the same time, there is a short circuit model based on visual image information. Based on GeoServer platform, we will develop an early warning system for wind-induced disasters of transmission lines, which can provide real-time warning and early warning for wind-induced disasters. The application of the system can reduce the impact of wind damage on the transmission line, and effectively improve the operation reliability of the line.

An application of backtracking search optimization–based least squares support vector machine for prediction of short-term wind speed

2019 ◽  
Vol 44 (3) ◽  
pp. 266-281 ◽  
Author(s):  
Zhongda Tian ◽  
Yi Ren ◽  
Gang Wang
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Prediction Model ◽  
Least Squares ◽  
Support Vector ◽  
Short Term ◽  
Backtracking Search ◽  
Wind Speed Prediction ◽  
Search Optimization ◽  
Speed Prediction

Wind speed prediction is an important technology in the wind power field; however, because of their chaotic nature, predicting wind speed accurately is difficult. Aims at this challenge, a backtracking search optimization–based least squares support vector machine model is proposed for short-term wind speed prediction. In this article, the least squares support vector machine is chosen as the short-term wind speed prediction model and backtracking search optimization algorithm is used to optimize the important parameters which influence the least squares support vector machine regression model. Furthermore, the optimal parameters of the model are obtained, and the short-term wind speed prediction model of least squares support vector machine is established through parameter optimization. For time-varying systems similar to short-term wind speed time series, a model updating method based on prediction error accuracy combined with sliding window strategy is proposed. When the prediction model does not match the actual short-term wind model, least squares support vector machine trains and re-establishes. This model updating method avoids the mismatch problem between prediction model and actual wind speed data. The actual collected short-term wind speed time series is used as the research object. Multi-step prediction simulation of short-term wind speed is carried out. The simulation results show that backtracking search optimization algorithm–based least squares support vector machine model has higher prediction accuracy and reliability for the short-term wind speed. At the same time, the prediction performance indicators are also improved. The prediction result is that root mean square error is 0.1248, mean absolute error is 0.1374, mean absolute percentile error is 0.1589% and R2 is 0.9648. When the short-term wind speed varies from 0 to 4 m/s, the average value of absolute prediction error is 0.1113 m/s, and average value of absolute relative prediction error is 8.7111%. The proposed prediction model in this article has high engineering application value.

scholarly journals A Novel Self-Adaptive Wind Speed Prediction Model Considering Atmospheric Motion and Fractal Feature

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 215892-215903
Author(s):  
Ji Jin ◽  
Bin Wang ◽  
Min Yu ◽  
Jiang Liu ◽  
Wenbo Wang
Keyword(s):  
Wind Speed ◽  
Prediction Model ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Atmospheric Motion ◽  
Self Adaptive

scholarly journals Methodology for Developing Hydrological Models Based on an Artificial Neural Network to Establish an Early Warning System in Small Catchments

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Ivana Sušanj ◽  
Nevenka Ožanić ◽  
Ivan Marović
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Small Scale ◽  
Ann Model ◽  
Data Value ◽  
The Mean ◽  
Artificial Neural

In some situations, there is no possibility of hazard mitigation, especially if the hazard is induced by water. Thus, it is important to prevent consequences via an early warning system (EWS) to announce the possible occurrence of a hazard. The aim and objective of this paper are to investigate the possibility of implementing an EWS in a small-scale catchment and to develop a methodology for developing a hydrological prediction model based on an artificial neural network (ANN) as an essential part of the EWS. The methodology is implemented in the case study of the Slani Potok catchment, which is historically recognized as a hazard-prone area, by establishing continuous monitoring of meteorological and hydrological parameters to collect data for the training, validation, and evaluation of the prediction capabilities of the ANN model. The model is validated and evaluated by visual and common calculation approaches and a new evaluation for the assessment. This new evaluation is proposed based on the separation of the observed data into classes based on the mean data value and the percentages of classes above or below the mean data value as well as on the performance of the mean absolute error.

scholarly journals Wind Speed Prediction Model Using LSTM and 1D-CNN

2018 ◽  
Vol 22 (4) ◽  
pp. 207-210 ◽  
Author(s):  
Rui Fukuoka ◽  
Hiroshi Suzuki ◽  
Takahiro Kitajima ◽  
Akinobu Kuwahara ◽  
Takashi Yasuno
Keyword(s):  
Wind Speed ◽  
Prediction Model ◽  
Wind Speed Prediction ◽  
Speed Prediction

Establishment of an early warning system for international trade tensions in the Heilongjiang province of China

2021 ◽  
Vol 95 (2) ◽  
pp. 17-28
Author(s):  
An Zhao Zhen ◽  
Keyword(s):  
International Trade ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Heilongjiang Province ◽  
Global Epidemic ◽  
General Importance ◽  
Strategic Issue ◽  
Trade Frictions ◽  
The Impact

In 2020, the global economic and trading environment has undergone major changes due to the impact of the global epidemic of the COVID-19. It is not only the world economy that has seriously suffered, protectionism in international trade is growing, and economic and trade frictions between countries with many factors have sharply worsened. Faced with a new situation and new challenges, accelerating the construction of an early warning system for international trade conflicts in Heilongjiang Province has become an important strategic issue of general importance.

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