Study on the Forecast of the Economic Loss in Storm Surge Disaster Based on Fourier Series - A Case Study of China

2013 ◽  
Vol 734-737 ◽  
pp. 3280-3285
Author(s):  
Ling Di Zhao ◽  
Ya Ru Hao
Keyword(s):  
Fourier Series ◽  
Wind Speed ◽  
Wind Power ◽  
Storm Surge ◽  
Guangdong Province ◽  
Economic Loss ◽  
Forecasting Model ◽  
Storm Surge Disaster

The economic loss forecasting model is built up on the basis of the Fourier series to simulate economic loss and grades in storm surge disaster of Zhejiang, Fujian and Guangdong Provinces. The wind speed can be used to forecast the economic loss of Guangdong Province, and the accuracy of trend and grade forecasting is good (80%). The wind power data can be used in Zhejiang and Fujian Provinces, and the accuracy results are both inferior (60%). Therefore, in the economic warning of storm surge disaster, the Fourier series model can be applied to forecast economic loss and grades.

scholarly journals Short Term Wind Power Prediction Based on Data Regression and Enhanced Support Vector Machine

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6319
Author(s):  
Chia-Sheng Tu ◽  
Chih-Ming Hong ◽  
Hsi-Shan Huang ◽  
Chiung-Hsing Chen
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Regression Model ◽  
Wind Power ◽  
Support Vector ◽  
Forecasting Model ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Data Regression ◽  
Power Forecasting

This paper presents a short-term wind power forecasting model for the next day based on historical marine weather and corresponding wind power output data. Due the large amount of historical marine weather and wind power data, we divided the data into clusters using the data regression (DR) algorithm to get meaningful training data, so as to reduce the number of modeling data and improve the efficiency of computing. The regression model was constructed based on the principle of the least squares support vector machine (LSSVM). We carried out wind speed forecasting for one hour and one day and used the correlation between marine wind speed and the corresponding wind power regression model to realize an indirect wind power forecasting model. Proper parameter settings for LSSVM are important to ensure its efficiency and accuracy. In this paper, we used an enhanced bee swarm optimization (EBSO) to perform the parameter optimization for LSSVM, which not only improved the forecast model availability, but also improved the forecasting accuracy.

Regional Wind Power Forecasting Based on Cloud Model and Wind Rate Vector

2013 ◽  
Vol 860-863 ◽  
pp. 405-408
Author(s):  
Dun Nan Liu ◽  
Yu Hu ◽  
Qun Li ◽  
Guang Hui Shao ◽  
Hai Ming Zhou ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Turning Point ◽  
Cloud Model ◽  
Historical Change ◽  
Power Prediction ◽  
Wind Power Forecast ◽  
Wind Rate ◽  
Rate Vector

The accuracy of wind power forecast is important to the power system operation. A new prediction model is proposed based on cloud reasoning and wind rate vector , combining with the current and the historical change rule of wind speed, using the change rule of wind speed in a period of time to forecast the power gradient in a point-in-time, The wind turbine power prediction is discussed based on power gradient and power eigenvalue. Simulation results on the case study of historical wind speed and generated power data in some area in China demonstrate that the proposed methodology can improve the accuracy of wind speed forecast and has practical value, especially for the wind turning point.

scholarly journals Wind speed variability over Iran and its impact on wind power potential: a case study for Esfehan Province

2010 ◽  
Vol 18 (2) ◽  
pp. 198-210 ◽  
Author(s):  
Fatemeh Rahimzadeh ◽  
Ali Mohammad Noorian ◽  
Mojdeh Pedram ◽  
Michael C. Kruk
Keyword(s):  
Wind Speed ◽  
Wind Power

scholarly journals Understanding Hurricane Storm Surge Generation and Propagation Using a Forecasting Model, Forecast Advisories and Best Track in a Wind Model, and Observed Data—Case Study Hurricane Rita

2019 ◽  
Vol 7 (3) ◽  
pp. 77 ◽  
Author(s):  
Abram Musinguzi ◽  
Muhammad K. Akbar ◽  
Jason G. Fleming ◽  
Samuel K. Hargrove
Keyword(s):  
Storm Surge ◽  
Surface Wind ◽  
Storm Surges ◽  
Wind Pressure ◽  
Forecasting Model ◽  
Wind Fields ◽  
Wind Model ◽  
Meteorological Forcing ◽  
Hurricane Rita

Meteorological forcing is the primary driving force and primary source of errors for storm surge forecasting. The objective of this study was to learn how forecasted meteorological forcing influences storm surge generation and propagation during a hurricane so that storm surge models can be reliably used to forecast actual events. Hindcasts and forecasts of Hurricane Rita (2005) storm surge was used as a case study. Meteorological forcing or surface wind/pressure fields for Hurricane Rita were generated using both the Weather Research and Forecasting (WRF) full-scale forecasting model along with archived hurricane advisories ingested into a sophisticated parametric wind model, namely Generalized Asymmetric Holland Model (GAHM). These wind fields were used to forecast Rita storm surges. Observation based wind fields from the OceanWeather Inc. (OWI) Interactive Objective Kinematic Analysis (IOKA) model, and Best track wind data ingested into the GAHM model were used to generate wind fields for comparison purposes. These wind fields were all used to hindcast Rita storm surges with the ADvanced CIRCulation (ADCIRC) model coupled with the Simulating Waves Nearshore (SWAN) model in a tightly coupled storm surge-wave model referred to as ADCIRC+SWAN. The surge results were compared against a quality-controlled database of observed data to assess the performance of these wind fields on storm surge generation and propagation. The surge hindcast produced by the OWI wind field performed the best, although some high water mark (HWM) locations were overpredicted. Although somewhat underpredicted, the WRF wind fields forecasted wider surge extent and wetted most HWM locations. The hindcast using the Best track parameters in the GAHM and the forecast using forecast/advisories from the National Hurricane Center (NHC) in the GAHM produced strong and narrow wind fields causing localized high surges, which resulted in overprediction near landfall while many HWM locations away from wind bands remained dry.

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