Ensemble forecaster based on the combination of time-frequency analysis and machine learning strategies for very short-term wind speed prediction

Rodríguez, Fermín; Alonso-Pérez, Sandra; Sánchez-Guardamino, Ignacio; Galarza, Ainhoa

doi:10.1016/j.epsr.2022.108863

Cited by 14 publications

(5 citation statements)

References 39 publications

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“…141,142 The optimization of renewable energy production, particularly from solar and wind sources, stands as a striking illustration of this trend. 143,144 Real-time data are analyzed to predict energy output, allowing for more efficient grid integration and energy distribution. 145 This analysis primarily utilizes data such as weather forecasts, historical energy usage patterns, and real-time sensor readings from energy generation equipment.…”

Section: In Energy Systemsmentioning

confidence: 99%

“…In recent years, a marked advancement in ML application in energy systems has been observed. , Significant enhancements in the efficiency and sustainability of these systems have been realized by harnessing the predictive and analytical capabilities of ML algorithms. , The optimization of renewable energy production, particularly from solar and wind sources, stands as a striking illustration of this trend. , Real-time data are analyzed to predict energy output, allowing for more efficient grid integration and energy distribution . This analysis primarily utilizes data such as weather forecasts, historical energy usage patterns, and real-time sensor readings from energy generation equipment.…”

Section: Energy and Fuelsmentioning

confidence: 99%

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Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future

Bin Abu Sofian,

Sun,

Gupta

et al. 2024

Energy Fuels

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This review illuminates the pivotal synergy between machine learning (ML) and biopolymers, spotlighting their combined potential to reshape sustainable energy, fuels, and biochemicals. Biobased polymers, derived from renewable sources, have garnered attention for their roles in sustainable energy and fuel sectors. These polymers, when integrated with ML techniques, exhibit enhanced functionalities, optimizing renewable energy systems, storage, and conversion. Detailed case studies reveal the potential of biobased polymers in energy applications and the fuel industry, further showcasing how ML bolsters fuel efficiency and innovation. The intersection of biobased polymers and ML also marks advancements in biochemical production, emphasizing innovations in drug delivery and medical device development. This review underscores the imperative of harnessing the convergence of ML and biobased polymers for future global sustainability endeavors in energy, fuels, and biochemicals. The collective evidence presented asserts the immense promise this union holds for steering a sustainable and innovative trajectory.

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Section: In Energy Systemsmentioning

confidence: 99%

Section: Energy and Fuelsmentioning

confidence: 99%

Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future

Bin Abu Sofian,

Sun,

Gupta

et al. 2024

Energy Fuels

View full text Add to dashboard Cite

show abstract

“…In this work, RMSE, MAE, and MAPE are the assessment criteria, and Equations ( 18)-( 20) provide the formulas. RMSE, MAE, and MAPE are often used in time series forecasting studies (Rodríguez et al, 2023).…”

Section: Evaluation Metricmentioning

confidence: 99%

“…In this work, RMSE, MAE, and MAPE are the assessment criteria, and Equations (18)–(20) provide the formulas. RMSE, MAE, and MAPE are often used in time series forecasting studies (Rodríguez et al, 2023).

RMSE goodbreak= \sqrt{\frac{1}{N_{t}} \sum_{n = 1}^{N_{t}} {({italicyp}_{n} - y_{n})}^{2}},

MAE goodbreak= \frac{1}{N_{t}} \sum_{n = 1}^{N_{t}} (||, {yp}_{n} goodbreak- y_{n}),

MAPE goodbreak= \frac{1}{N_{t}} \sum_{n = 1}^{N_{t}} \frac{(||, {yp}_{n} goodbreak- y_{n})}{y_{n}},

where N t is the number of test sets, y n denotes the actual point, and

{yp}_{n}

is the forecasting value.…”

Section: Evaluation Metricmentioning

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

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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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“…After the forecasters have been trained, their true accuracy can be measured against a validation dataset. (Rodríguez, F et al,2023).…”

Section: Literature Reviewmentioning

confidence: 99%

Temporal Forecasting by Converting Stochastic Behaviour into a Stable Pattern in Electric Grid.

Qashou,

Yousef

2023

Preprint

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The malfunction variables of power stations are related to the areas of weather, physical structure, control, and load behavior. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behavior into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms are used to perform the Short-term estimation. The environment, the operation, and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a dataset. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, to address the gap in knowledge for any future power grid estimated failures, the achieved results in this paper form good basis for a testbed to estimate any grid future failures.

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Ensemble forecaster based on the combination of time-frequency analysis and machine learning strategies for very short-term wind speed prediction

Cited by 14 publications

References 39 publications

Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future

Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future

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

Temporal Forecasting by Converting Stochastic Behaviour into a Stable Pattern in Electric Grid.

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