Efficient bootstrap stacking ensemble learning model applied to wind power generation forecasting

Ribeiro, Matheus Henrique Dal Molin; Silva, Ramon Gomes da; Moreno, Sinvaldo Rodrigues; Mariani, Viviana Cocco; Coelho, Leandro dos Santos

doi:10.1016/j.ijepes.2021.107712

Cited by 103 publications

(28 citation statements)

References 43 publications

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“…The advantages and disadvantages were listed to help choose an appropriate model for future research. The estimation indicators include RMSE, MAE, MAPE, scatter index and correction efficiency to evaluate the performance of models [9,74]. The optimization of wind and wave application could extend to many fields of device optimization, layout optimization and energy management.…”

Section: Discussionmentioning

confidence: 99%

Review on Deep Learning Research and Applications in Wind and Wave Energy

2022

Energies

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Wind energy and wave energy are considered to have enormous potential as renewable energy sources in the energy system to make great contributions in transitioning from fossil fuel to renewable energy. However, the uncertain, erratic, and complicated scenarios, as well as the tremendous amount of information and corresponding parameters, associated with wind and wave energy harvesting are difficult to handle. In the field of big data handing and mining, artificial intelligence plays a critical and efficient role in energy system transition, harvesting and related applications. The derivative method of deep learning and its surrounding prolongation structures are expanding more maturely in many fields of applications in the last decade. Even though both wind and wave energy have the characteristics of instability, more and more applications have implemented using these two renewable energy sources with the support of deep learning methods. This paper systematically reviews and summarizes the different models, methods and applications where the deep learning method has been applied in wind and wave energy. The accuracy and effectiveness of different methods on a similar application were compared. This paper concludes that applications supported by deep learning have enormous potential in terms of energy optimization, harvesting, management, forecasting, behavior exploration and identification.

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Section: Discussionmentioning

confidence: 99%

Review on Deep Learning Research and Applications in Wind and Wave Energy

2022

Energies

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“…here the final meta-learner function is S(•) and each weaklearner regression is given by w l (•), where L is the number of weak learners [49]. Some strategies like boosting and stacking work similarly to aggregate the weak learners and obtain a model with better performance [55]. The boosting model consists of sequentially adjusting several weak learners in an adaptive way, so more importance is given to observations that were poorly handled by previous models in the sequence [56].…”

Section: Ensemble Learning Modelmentioning

confidence: 99%

Analysis of the Ultrasonic Signal in Polymeric Contaminated Insulators Through Ensemble Learning Methods

et al. 2022

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Outdoor insulators may experience stress due to severe environmental conditions, such as pollution and contamination. Through the identification of partial discharges by ultrasonic noise, it is possible to assess the possibility of a power grid failure occurring. In this paper, ensemble models are used to analyze an ultrasonic signal from an ultrasonic microphone Pettersson M500. As the insulators are susceptible to developing irreversible failures, it will be evaluated whether the ultrasonic signal will remain over time, so that it is possible to assess whether the discharges being captured can result in a failure in contaminated polymeric insulators, evaluated in a high voltage laboratory under controlled conditions. The ensemble models were used in this paper because they typically require less computational effort than techniques based on deep learning and have acceptable performance for the problem at hand. The bagging, boosting, random subspace, bagging plus random subspace, and stacked generalization ensemble models are evaluated, and the best result of each model is used to compare the differences between the models. The bagging ensemble learning model proved to be faster and have lower error than other ensemble models, long short-term memory (LSTM), and nonlinear autoregressive (NAR).

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“…Many variations of ensemble models for time series forecasting can be found such as efficient bootstrap stacking presented by Ribeiro et al [55], extreme gradient boosting proposed by Sauer et al [56]. Especially for power system failure prediction, the Wavelet transform combined with ensemble models becomes a superior approach to wellestablished models such as the adaptive neuro-fuzzy inference system [57].…”

Section: Related Workmentioning

confidence: 99%

Wavelet Long Short-Term Memory to Fault Forecasting in Electrical Power Grids

Branco¹,

Cavalca²,

Stefenon³

et al. 2022

Preprint

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The electric power distribution utility is responsible for providing energy to consumers in a continuous and stable way, failures in the electrical power system reduce the reliability indexes of the grid, directly harming its performance. For this reason, there is a need for failure prediction to reestablish power in the shortest possible time. Considering an evaluation of the number of failures over time, this paper proposes to perform a failure prediction during the first year of the pandemic in Brazil (2020) to verify the feasibility of using time series forecasting models for fault prediction. The Long Short-Term Memory (LSTM) model will be evaluated to obtain a forecast result that can be used by the electric power utility to organize the maintenance teams. The Wavelet transform shows to be promising in improving the predictive ability of the LSTM, making the Wavelet LSTM model suitable for the study at hand. The results show that the proposed approach has better results regarding the evaluation of the error in prediction and has robustness when a statistical analysis is performed.

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Efficient bootstrap stacking ensemble learning model applied to wind power generation forecasting

Cited by 103 publications

References 43 publications

Review on Deep Learning Research and Applications in Wind and Wave Energy

Review on Deep Learning Research and Applications in Wind and Wave Energy

Analysis of the Ultrasonic Signal in Polymeric Contaminated Insulators Through Ensemble Learning Methods

Wavelet Long Short-Term Memory to Fault Forecasting in Electrical Power Grids

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