Wind speed forecasting using a hybrid model considering the turbulence of the airflow

Méndez-Gordillo, Alma Rosa; Campos–Amezcua, Rafael; Cadenas, Erasmo

doi:10.1016/j.renene.2022.06.139

Cited by 7 publications

(4 citation statements)

References 48 publications

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“…Short-term prediction is often utilized to optimize the generation plan of conventional units, whereas medium-or long-term projection is associated with the power grid's repair plan. Methods for predicting wind speed have been widely proposed and applied (L opez & Arboleya, 2022;Méndez-Gordillo et al, 2022;Roungkvist & Enevoldsen, 2020). Physical, statistical, artificial intelligence (AI)-based, and hybrid models are the most common types of wind speed forecasting systems (Jiang et al, 2021;Xian & Che, 2022).…”

Section: Literature Review On Wind Speed Forecastingmentioning

confidence: 99%

“…Methods for predicting wind speed have been widely proposed and applied (López & Arboleya, 2022; Méndez‐Gordillo et al, 2022; Roungkvist & Enevoldsen, 2020). Physical, statistical, artificial intelligence (AI)‐based, and hybrid models are the most common types of wind speed forecasting systems (Jiang et al, 2021; Xian & Che, 2022).…”

Section: Literature Review On Wind Speed Forecastingmentioning

confidence: 99%

See 1 more Smart Citation

Explainable machine learning techniques based on attention gate recurrent unit and local interpretable model‐agnostic explanations for multivariate wind speed forecasting

Peng,

Lv,

Wang

2024

Journal of Forecasting

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Wind power has emerged as a successful component within power systems. The ability to reliably and accurately forecast wind speed is of great importance in maintaining the security and stability of the power grid. However, the significance of explaining prediction models has often been overlooked by researchers. To address this gap, this study introduces a novel approach to wind speed forecasting that incorporates a significant decomposition method, attention‐based machine learning, and local explanation techniques. The proposed model utilizes grid search variational mode decomposition to decompose the wind speed sequence into different modes while employing gate recurrent unit with an attention mechanism to achieve superior forecasting performance. Experimental evaluations conducted on eight real‐world wind speed datasets demonstrate that the proposed approach outperforms other popular models across multiple performance criteria. In two specific experiments, the proposed approach achieved a minimal mean absolute percentage error of 2.74% and 1.70%, respectively. Furthermore, local interpretable model‐agnostic explanations (LIME) were employed to assess the influence of factors, highlighting whether they positively or negatively affected the predicted values.

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Section: Literature Review On Wind Speed Forecastingmentioning

confidence: 99%

Section: Literature Review On Wind Speed Forecastingmentioning

confidence: 99%

Explainable machine learning techniques based on attention gate recurrent unit and local interpretable model‐agnostic explanations for multivariate wind speed forecasting

Peng,

Lv,

Wang

2024

Journal of Forecasting

View full text Add to dashboard Cite

show abstract

“…Combined approaches are needed to obtain an advanced forecasting method with higher precision levels and longer forecast horizons, particularly for sites with complex terrain. In fact, hybrid methods generally outperform individual models (Tascikaraoglu and Uzunoglu, 2014;Okumus and Dinler, 2016;Méndez-Gordillo et al, 2022). In particular, hybrid methods that use NWP tend to outperform statistical approaches after a lead time of 3-6 h, therefore, they are present in most operational and commercial uses (Giebel and Kariniotakis, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…employed a single exponential smoothing method to forecast wind speeds in Chetumal, in eastern Mexico. Ibargüengoytia et al (2014) developed a dynamic Bayesian network model based on historical time series for wind velocity prediction at a wind farm in Oaxaca, Mexico, for a short-term forecast horizon of 5 h. Méndez-Gordillo et al (2022) proposed a hybrid statistical technique that includes a separation of turbulent and non-turbulent flows to forecast wind speed one step ahead at two sites in the northern Gulf of Mexico. Also, Santamaría-Bonfil et al (2016) employed a hybrid statistical approach based on support vector regression, obtaining accurate results for medium to short-term wind speed and power forecasts in southwestern Mexico.…”

Section: Introductionmentioning

confidence: 99%

Using a hybrid approach for wind power forecasting in Northwestern Mexico

Diaz-Esteban,

López-Villalobos,

Ochoa Moya

et al. 2023

Atm.

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Wind energy is an important renewable source that has been considerably developed recently. In order to obtain successful 24-h lead-time wind power forecasts for operational and commercial uses, a combination of physical and statistical models is desirable. In this paper, a hybrid methodology that employs a numerical weather prediction model (Weather Research and Forecasting) and a neural network (NN) algorithm is proposed and assessed. The methodology is applied to a wind farm in northwestern Mexico, a region with high wind potential where complex geography adds large uncertainty to wind energy forecasts. The energy forecasts are then evaluated against actual on-site power generation over one year and compared with two reference models: decision trees (DT) and support vector regression (SVR). The proposed method exhibits a better performance with respect to the reference methods, showing an hourly normalized mean absolute percentage error of 6.97%, which represents 6 and 13 percentage points less error in wind power forecasts than with DT and SVR methods, respectively. Under strong synoptic forcing, the NN wind power forecast is not very accurate, and novel approaches such as hierarchical algorithms should be employed instead. Overall, the proposed model is capable of producing high-quality wind power forecasts for most weather conditions prevailing in this region and demonstrates a good performance with respect to similar models for medium-term wind power forecasts.

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A novel hybrid forecasting model with feature selection and deep learning for wind speed research

Chen,

Wang,

Zhang

et al. 2024

Journal of Forecasting

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Accurate wind speed prediction is of great importance for the operation of wind farms, and extensive efforts have been made to develop effective forecasting methods in this regard. However, the feature selection of data input as well as optimization of deep learning models have received comparatively less attention, leading to unreliable forecasting results. This research proposes a novel hybrid model that integrates data preprocessing, feature selection, and optimized forecasting for improved wind speed prediction. Specifically, a powerful preprocessing technique is utilized to reduce data noise disturbances, while an innovative two‐stage feature selection is designed to achieve the optimal input data format for forecasting purposes. Moreover, a hybrid forecasting module based on long‐short term memory, which is optimized by the Bayesian optimization algorithm, has been developed to enhance the efficiency and reliability of the model. The empirical study used 10‐min interval wind speed data of four seasons for presentation and evaluation results demonstrated its superior performance in effectively learning the volatility and irregularity features of wind speed series, which established a solid foundation for practical applications in wind power systems.

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Wind speed forecasting using a hybrid model considering the turbulence of the airflow

Cited by 7 publications

References 48 publications

Explainable machine learning techniques based on attention gate recurrent unit and local interpretable model‐agnostic explanations for multivariate wind speed forecasting

Explainable machine learning techniques based on attention gate recurrent unit and local interpretable model‐agnostic explanations for multivariate wind speed forecasting

Using a hybrid approach for wind power forecasting in Northwestern Mexico

A novel hybrid forecasting model with feature selection and deep learning for wind speed research

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