scholarly journals Performance Evaluation of Linear and Nonlinear Models for Short-Term Forecasting of Tropical-Storm Winds

2021 ◽  
Vol 11 (20) ◽  
pp. 9441
Author(s):  
Tianyou Tao ◽  
Peng Shi ◽  
Hao Wang ◽  
Lin Yuan ◽  
Sheng Wang
Keyword(s):  
Nonlinear Models ◽  
Arima Model ◽  
Tropical Storm ◽  
Tropical Storms ◽  
Short Term ◽  
Wind Speeds ◽  
One Step ◽  
Linear And Nonlinear ◽  
Short Term Forecasting ◽  
Forecasting Errors

Wind-sensitive structures usually suffer from violent vibrations or severe damages under the action of tropical storms. It is of great significance to forecast tropical-storm winds in advance for the sake of reducing or avoiding consequent losses. The model used for forecasting becomes a primary concern in engineering applications. This paper presents a performance evaluation of linear and nonlinear models for the short-term forecasting of tropical storms. Five extensively employed models are adopted to forecast wind speeds using measured samples from the tropical storm Rumbia, which facilitates a comparison of the predicting performances of different models. The analytical results indicate that the autoregressive integrated moving average (ARIMA) model outperforms the other models in the one-step ahead prediction and presents the least forecasting errors in both the mean and maximum wind speeds. However, the support vector regression (SVR) model has the worst performance on the selected dataset. When it comes to the multi-step ahead forecasting, the prediction error of each model increases as the number of steps expands. Although each model shows an insufficient ability to capture the variation of future wind speed, the ARIMA model still appears to have the least forecasting errors. Hence, the ARIMA model can offer effective short-term forecasting of tropical-storm winds in both one-step and multi-step scenarios.

scholarly journals Linear and nonlinear prediction techniques for short-term forecasting of HF fading signals

Radio Science ◽  
1997 ◽  
Vol 32 (3) ◽  
pp. 989-998 ◽  
Author(s):  
S. V. Fridman ◽  
K. C. Yeh ◽  
O. V. Fridman ◽  
S. J. Franke
Keyword(s):  
Nonlinear Prediction ◽  
Short Term ◽  
Linear And Nonlinear ◽  
Short Term Forecasting ◽  
Prediction Techniques

scholarly journals Very short-term forecast of near-coastal flow using scanning lidars

2017 ◽  
Author(s):  
Laura Valldecabres ◽  
Alfredo Peña ◽  
Michael Courtney ◽  
Lueder von Bremen ◽  
Martin Kühn
Keyword(s):  
Arima Model ◽  
Atmospheric Stability ◽  
Lead Times ◽  
Atmospheric Conditions ◽  
Short Term ◽  
Wind Speeds ◽  
Lidar Measurements ◽  
Wind Measurements ◽  
Scanning Lidar ◽  
Coastal Flow

Abstract. Wind measurements can reduce the uncertainty in the prediction of wind energy production. Nowadays, commercially available scanning lidars can scan the atmosphere up to several kilometres. Here, we use lidar measurements to forecast near-coastal winds with lead times of five minutes. Using Taylor's frozen turbulence hypothesis together with local topographic corrections, we demonstrate that wind speeds at a downstream position can be forecast by using measurements from a scanning lidar performed upstream in a very short-term horizon. The study covers ten periods characterized by neutral and stable atmospheric conditions. Our methodology shows smaller forecasting errors than those of the persistence method and the ARIMA model. We discuss the applicability of this forecasting technique with regards to the characteristics of the lidar trajectories, the site-specific conditions and the atmospheric stability.

The short-term forecasting of evaporation duct height (EDH) based on ARIMA model

2016 ◽  
Vol 76 (23) ◽  
pp. 24903-24916 ◽  
Author(s):  
Shaobo Yang ◽  
Sihui Liu ◽  
Xingfei Li ◽  
Ying Zhong ◽  
Xin He ◽  
...  
Keyword(s):  
Arima Model ◽  
Short Term ◽  
Evaporation Duct ◽  
Short Term Forecasting ◽  
Evaporation Duct Height

scholarly journals The Blundered Strategy of Euro Adoption in the Czech Republic: A ”Pre-crisis“ Retrospective

2013 ◽  
Vol 13 (3) ◽  
pp. 132-145
Author(s):  
Vladislav Flek
Keyword(s):  
Czech Republic ◽  
Economic Policies ◽  
Convergence Criteria ◽  
Eurozone Crisis ◽  
Short Term ◽  
The Czech Republic ◽  
Euro Adoption ◽  
Short Term Forecasting ◽  
Forecasting Errors

Abstract The commitment to join the eurozone in 2009-2010 was rejected by Czech Republic in 2006 at a time when signs of the eurozone crisis were not yet apparent. Nor did the prospects of failure to fulfil any of the Maastricht Convergence Criteria have any realistic basis. Real or alleged difficulties in meeting the Maastricht Convergence Criteria and attaining economic alignment with the eurozone did not serve as a mobilization factor for economic policies. Instead, these issues were perceived to offer an objective reason against euro adoption within the declared timeframe. The official stance against the euro was partly based on serious analytical and short-term forecasting errors, if not on deliberate manipulations, including an overemphasised misalignment of the Czech economy with the eurozone.

scholarly journals Deterministic prediction of surface wind speed variations

2014 ◽  
Vol 32 (11) ◽  
pp. 1415-1425 ◽  
Author(s):  
G. V. Drisya ◽  
D. C. Kiplangat ◽  
K. Asokan ◽  
K. Satheesh Kumar
Keyword(s):  
Wind Speed ◽  
Arima Model ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Temporal Variations ◽  
Short Term ◽  
Deterministic Models ◽  
Wind Speed Data ◽  
Wind Speeds ◽  
Past Data

Abstract. Accurate prediction of wind speed is an important aspect of various tasks related to wind energy management such as wind turbine predictive control and wind power scheduling. The most typical characteristic of wind speed data is its persistent temporal variations. Most of the techniques reported in the literature for prediction of wind speed and power are based on statistical methods or probabilistic distribution of wind speed data. In this paper we demonstrate that deterministic forecasting methods can make accurate short-term predictions of wind speed using past data, at locations where the wind dynamics exhibit chaotic behaviour. The predictions are remarkably accurate up to 1 h with a normalised RMSE (root mean square error) of less than 0.02 and reasonably accurate up to 3 h with an error of less than 0.06. Repeated application of these methods at 234 different geographical locations for predicting wind speeds at 30-day intervals for 3 years reveals that the accuracy of prediction is more or less the same across all locations and time periods. Comparison of the results with f-ARIMA model predictions shows that the deterministic models with suitable parameters are capable of returning improved prediction accuracy and capturing the dynamical variations of the actual time series more faithfully. These methods are simple and computationally efficient and require only records of past data for making short-term wind speed forecasts within practically tolerable margin of errors.

scholarly journals Short-Term Forecasting of Loads and Wind Power for Latvian Power System: Accuracy and Capacity of the Developed Tools

2016 ◽  
Vol 53 (2) ◽  
pp. 3-13 ◽  
Author(s):  
V. Radziukynas ◽  
A. Klementavičius
Keyword(s):  
Power System ◽  
Wind Power ◽  
Arima Models ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Additional Input ◽  
Input Variables ◽  
Short Term Forecasting ◽  
Performance Results ◽  
Forecasting Errors

Abstract The paper analyses the performance results of the recently developed short-term forecasting suit for the Latvian power system. The system load and wind power are forecasted using ANN and ARIMA models, respectively, and the forecasting accuracy is evaluated in terms of errors, mean absolute errors and mean absolute percentage errors. The investigation of influence of additional input variables on load forecasting errors is performed. The interplay of hourly loads and wind power forecasting errors is also evaluated for the Latvian power system with historical loads (the year 2011) and planned wind power capacities (the year 2023).

scholarly journals Assessing and Comparing Short Term Load Forecasting Performance

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 2054 ◽  
Author(s):  
Pekka Koponen ◽  
Jussi Ikäheimo ◽  
Juha Koskela ◽  
Christina Brester ◽  
Harri Niska
Keyword(s):  
Electricity Market ◽  
Load Forecasting ◽  
Performance Criteria ◽  
Short Term ◽  
Comparison Results ◽  
Forecasting Performance ◽  
Network Costs ◽  
And Performance ◽  
Short Term Forecasting ◽  
Forecasting Errors

When identifying and comparing forecasting models, there may be a risk that poorly selected criteria could lead to wrong conclusions. Thus, it is important to know how sensitive the results are to the selection of criteria. This contribution aims to study the sensitivity of the identification and comparison results to the choice of criteria. It compares typically applied criteria for tuning and performance assessment of load forecasting methods with estimated costs caused by the forecasting errors. The focus is on short-term forecasting of the loads of energy systems. The estimated costs comprise electricity market costs and network costs. We estimate the electricity market costs by assuming that the forecasting errors cause balancing errors and consequently balancing costs to the market actors. The forecasting errors cause network costs by overloading network components thus increasing losses and reducing the component lifetime or alternatively increase operational margins to avoid those overloads. The lifetime loss of insulators, and thus also the components, is caused by heating according to the law of Arrhenius. We also study consumer costs. The results support the assumption that there is a need to develop and use additional and case-specific performance criteria for electricity load forecasting.

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