A hierarchical classification/regression algorithm for improving extreme wind speed events prediction

Peláez-Rodríguez, C.; Pérez-Aracil, Jorge; Fister, Dušan; Prieto-Godino, Luis; Deo, Ravinesh C.; Salcedo‐Sanz, Sancho

doi:10.1016/j.renene.2022.11.042

Cited by 14 publications

(3 citation statements)

References 43 publications

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“…Therefore, the AI and NN models fail to capture the complex dynamics of extreme events. Other approaches, like hierarchical algorithms (Peláez-Rodríguez et al, 2022), must be applied to improve the prediction skills of NN models on extreme events while preserving the prediction performance for regular conditions.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…To test our hypotheses, we employed a hierarchical regression analysis approach, as it helps to determine a unique contribution of a variable (Huang, 2023). The approach is suitable because it indicates whether the variables of interest can explain a significant variance to the dependent variable, especially after accounting for other variables (Peláez-Rodríguez et al, 2022). In this approach, predicting variables were entered into the regression in steps, and the framework worked as an approach for model comparison (Schriesheim, 1995).…”

Section: Hypotheses Testingmentioning

confidence: 99%

Parenting for Success: Exploring the Link Between Parenting Styles and Adolescents’ Academic Achievement Through Their Learning Engagement

Fute,

Oubibi,

Sun

et al. 2024

Sage Open

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Adolescents’ interaction with their parents at home is pivotal for their physical and mental health, which can further influence their academic achievement at school. However, every culture has distinctive parenting elements which are distinguishable from other cultures. This study explored the influence of parenting styles on adolescents’ academic achievement through the mediation role of learning engagement in a Chinese cultural context. A questionnaire was distributed among parents and students, and 1,557 valid responses were received and analyzed using SPSS software, PROCESS macro, Microsoft Excel, and JASP. The results indicated a positive and significant effect of parenting styles on students’ academic achievement, with students’ learning engagement partially mediating such relationship. Dimension-wise, parents’ emotional acceptance positively influenced students’ academic achievement, but rejecting and overprotecting them had negative repercussions. A comparison between fathers and mothers showed a higher effect of mothers’ parenting styles compared to that of fathers. Generally, students’ behaviors result from different forces which act in and outside them, making this study a wake-up call for cherishing collective efforts from parents, teachers, and students toward improving academic performance.

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“…In Peláez-Rodríguez et al (2022), a hierarchical classification-regression ML approach is proposed for a problem of extreme wind prediction. The approach starts with the application of clustering algorithms and different balancing techniques to increase the significance of clusters with poorly represented wind gusts data.…”

Section: Extreme Winds and Gustsmentioning

confidence: 99%

Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review

Salcedo-Sanz,

Pérez-Aracil,

Ascenso

et al. 2023

Theor Appl Climatol

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Atmospheric extreme events cause severe damage to human societies and ecosystems. The frequency and intensity of extremes and other associated events are continuously increasing due to climate change and global warming. The accurate prediction, characterization, and attribution of atmospheric extreme events is, therefore, a key research field in which many groups are currently working by applying different methodologies and computational tools. Machine learning and deep learning methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric extreme events. This paper reviews machine learning and deep learning approaches applied to the analysis, characterization, prediction, and attribution of the most important atmospheric extremes. A summary of the most used machine learning and deep learning techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. The critical literature review has been extended to extreme events related to rainfall and floods, heatwaves and extreme temperatures, droughts, severe weather events and fog, and low-visibility episodes. A case study focused on the analysis of extreme atmospheric temperature prediction with ML and DL techniques is also presented in the paper. Conclusions, perspectives, and outlooks on the field are finally drawn.

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A hierarchical classification/regression algorithm for improving extreme wind speed events prediction

Cited by 14 publications

References 43 publications

Using a hybrid approach for wind power forecasting in Northwestern Mexico

Using a hybrid approach for wind power forecasting in Northwestern Mexico

Parenting for Success: Exploring the Link Between Parenting Styles and Adolescents’ Academic Achievement Through Their Learning Engagement

Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review

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