Short-Term Wind Speed Prediction by Hybridizing Global and Mesoscale Forecasting Models with Artificial Neural Networks

Sanz, Sancho Salcedo; Pérez, Adrián Santamaría; Ortiz, Enrique; Portilla, Alberto; Prieto, Luís; Paredes, Daniel; Correoso, Juan Francisco

doi:10.1109/his.2008.36

Cited by 24 publications

(12 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, the hybridization of global and mesoscale weather forecasting models with neural networks was also employed for short-term wind speed forecasting. The results prove that the hybrid weather forecast model's neural network approach can achieve great forecasting results for short-term wind speeds under specific situations [41]. Hervas-Martinez et al proposed a hybrid model that combines the physical, statistical, and artificial neural networks, and achieves great forecasting accuracy [42].…”

Section: Review and Discussion For Previous Workmentioning

confidence: 86%

A Hybrid Wind Speed Forecasting System Based on a ‘Decomposition and Ensemble’ Strategy and Fuzzy Time Series

Yang

Jiang

2017

Energies

View full text Add to dashboard Cite

Accurate and stable wind speed forecasting is of critical importance in the wind power industry and has measurable influence on power-system management and the stability of market economics. However, most traditional wind speed forecasting models require a large amount of historical data and face restrictions due to assumptions, such as normality postulates. Additionally, any data volatility leads to increased forecasting instability. Therefore, in this paper, a hybrid forecasting system, which combines the 'decomposition and ensemble' strategy and fuzzy time series forecasting algorithm, is proposed that comprises two modules-data pre-processing and forecasting. Moreover, the statistical model, artificial neural network, and Support Vector Regression model are employed to compare with the proposed hybrid system, which is proven to be very effective in forecasting wind speed data affected by noise and instability. The results of these comparisons demonstrate that the hybrid forecasting system can improve the forecasting accuracy and stability significantly, and supervised discretization methods outperform the unsupervised methods for fuzzy time series in most cases.

show abstract

Section: Review and Discussion For Previous Workmentioning

confidence: 86%

A Hybrid Wind Speed Forecasting System Based on a ‘Decomposition and Ensemble’ Strategy and Fuzzy Time Series

Yang

Jiang

2017

Energies

View full text Add to dashboard Cite

show abstract

“…The main calculation contents of Kalman filter method are to deduce the right state and observation equations, and to value parameters of Kalman recursive equations [12].…”

Section: Kalman Recursive Equations Of Kalman Filter Methodsmentioning

confidence: 99%

“…Long-term forecasting is to build models and calculate forecasting values more than 24 h [12]. The forecasting algorithms usually include neural networks, time series analysis, Kalman filtering, genetic algorithm, wavelet analysis, etc [11−20].…”

Section: Introductionmentioning

confidence: 99%

Short-term forecasting optimization algorithms for wind speed along Qinghai-Tibet railway based on different intelligent modeling theories

Liu

Tian

2009

J. Cent. South Univ. Technol.

View full text Add to dashboard Cite

To protect trains against strong cross-wind along Qinghai-Tibet railway, a strong wind speed monitoring and warning system was developed. And to obtain high-precision wind speed short-term forecasting values for the system to make more accurate scheduling decision, two optimization algorithms were proposed. Using them to make calculative examples for actual wind speed time series from the 18th meteorological station, the results show that: the optimization algorithm based on wavelet analysis method and improved time series analysis method can attain high-precision multi-step forecasting values, the mean relative errors of one-step, three-step, five-step and ten-step forecasting are only 0.30%, 0.75%, 1.15% and 1.65%, respectively. The optimization algorithm based on wavelet analysis method and Kalman time series analysis method can obtain high-precision one-step forecasting values, the mean relative error of one-step forecasting is reduced by 61.67% to 0.115%. The two optimization algorithms both maintain the modeling simple character, and can attain prediction explicit equations after modeling calculation.

show abstract

“…As introduced in Section 3, the CPSO algorithm is employed as a parameter searching tool, optimizing the parameters α, β, and γ in Equations (15)- (17). This section aims to display how the parameters α, β, and γ are optimized during a CPSO-driven process.…”

Section: How the Cpso Work: An Ai-based Optimization Processmentioning

confidence: 99%

“…This set of parameters can always be obtained by the least square (LS) method during the model fitting process. The other category is the self-adaptive parameters, α, β, and γ, defined in Equations (15)- (17) when applying the SA strategy to an ARIMAX process. It can be easily found that parameters α, β, and γ represent a weighted average between the historical and the newly fitted model parameters.…”

Section: Parameters In the Sa-arimax Modelmentioning

confidence: 99%

Hybrid Wind Speed Prediction Based on a Self-Adaptive ARIMAX Model with an Exogenous WRF Simulation

Zhao

Liu

et al. 2015

Energies

View full text Add to dashboard Cite

Abstract:Wind speed forecasting is difficult not only because of the influence of atmospheric dynamics but also for the impossibility of providing an accurate prediction with traditional statistical forecasting models that work by discovering an inner relationship within historical records. This paper develops a self-adaptive (SA) auto-regressive integrated moving average with exogenous variables (ARIMAX) model that is optimized very-short-term by the chaotic particle swarm optimization (CPSO) algorithm, known as the SA-ARIMA-CPSO approach, for wind speed prediction. The ARIMAX model chooses the wind speed result from the Weather Research and Forecasting (WRF) simulation as an exogenous input variable. Further, an SA strategy is applied to the ARIMAX process. When new information is available, the model process can be updated adaptively with parameters optimized by the CPSO algorithm. The proposed SA-ARIMA-CPSO approach enables the forecasting process to update training information and model parameters intelligently and adaptively. As tested using the 15-min wind speed data collected from a wind farm in Northern China, the improved method has the best performance compared with several other models.

show abstract

Short-Term Wind Speed Prediction by Hybridizing Global and Mesoscale Forecasting Models with Artificial Neural Networks

Cited by 24 publications

References 17 publications

A Hybrid Wind Speed Forecasting System Based on a ‘Decomposition and Ensemble’ Strategy and Fuzzy Time Series

A Hybrid Wind Speed Forecasting System Based on a ‘Decomposition and Ensemble’ Strategy and Fuzzy Time Series

Short-term forecasting optimization algorithms for wind speed along Qinghai-Tibet railway based on different intelligent modeling theories

Hybrid Wind Speed Prediction Based on a Self-Adaptive ARIMAX Model with an Exogenous WRF Simulation

Contact Info

Product

Resources

About