Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism

Zhang, Shuai; Chen, Yong; Xiao, Jiuhong; Feng, Ruijun

doi:10.1016/j.renene.2021.04.091

Cited by 86 publications

(12 citation statements)

References 40 publications

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“…HMs in renewable energy forecasting are ML models that combine multiple techniques, including ANN, SVM, and statistical models, to improve the accuracy of predictions [202]. HMs offer several advantages over individual models by leveraging the strengths of each technique while compensating for their limitations [205]. In renewable energy forecasting, HMs can be employed to enhance prediction accuracy and overcome some of the limitations of classic ML models.…”

Section: Hybrid Model (Hm) For Forecasting Renewable Energymentioning

confidence: 99%

Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects

2023

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This article presents a review of current advances and prospects in the field of forecasting renewable energy generation using machine learning (ML) and deep learning (DL) techniques. With the increasing penetration of renewable energy sources (RES) into the electricity grid, accurate forecasting of their generation becomes crucial for efficient grid operation and energy management. Traditional forecasting methods have limitations, and thus ML and DL algorithms have gained popularity due to their ability to learn complex relationships from data and provide accurate predictions. This paper reviews the different approaches and models that have been used for renewable energy forecasting and discusses their strengths and limitations. It also highlights the challenges and future research directions in the field, such as dealing with uncertainty and variability in renewable energy generation, data availability, and model interpretability. Finally, this paper emphasizes the importance of developing robust and accurate renewable energy forecasting models to enable the integration of RES into the electricity grid and facilitate the transition towards a sustainable energy future.

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Section: Hybrid Model (Hm) For Forecasting Renewable Energymentioning

confidence: 99%

Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects

2023

View full text Add to dashboard Cite

show abstract

“…HMs in renewable energy forecasting are ML models that combine multiple techniques, like ANN, SVM, and statistical models, to improve the accuracy of predictions [192]. HMs offer several advantages over individual models by leveraging the strengths of each technique while compensating for their limitations [195]. In renewable energy forecasting, HMsls can be employed to enhance prediction accuracy and overcome some of the limitations of classic ML models.…”

Section: Hybrid Model (Hm) For Forecasting Renewable Energymentioning

confidence: 99%

“…For example, traditional models may struggle with capturing the complex and nonlinear relationships between RES and their influencing factors. HMs can address this challenge by combining multiple models and techniques to capture a wider range of features and enhance forecast accuracy [195].…”

Section: Hybrid Model (Hm) For Forecasting Renewable Energymentioning

confidence: 99%

Forecasting Renewable Energy Generation with Machine learning and Deep Learning: Current Advances and Future Prospects

Benti¹,

Chaka²,

Semie³

2023

Preprint

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This article presents a review of current advances and future prospects in the field of forecasting renewable energy generation using machine learning (ML) and deep learning (DL) techniques. With the increasing penetration of renewable energy sources (RES) into the electricity grid, accurate forecasting of their generation becomes crucial for efficient grid operation and energy management. Traditional forecasting methods have limitations, and thus ML and DL algorithms have gained popularity due to their ability to learn complex relationships from data and provide accurate predictions. This paper reviews the different approaches and models that have been used for renewable energy forecasting and discusses their strengths and limitations. It also highlights the challenges and future research directions in the field, such as dealing with uncertainty and variability in renewable energy generation, data availability, and model interpretability. Finally, this paper emphasizes the importance of developing robust and accurate renewable energy forecasting models to enable the integration of RES into the electricity grid and facilitate the transition towards a sustainable energy future.

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“…Furthermore, the probability distribution fitting is applied in order to fit a normal probability distribution to the wind speed time series regarding the repeated measurement of the variable wind data. The distribution fitting aims to predict the probability or the occurrence frequency of the wind speed magnitude in a certain interval [8]. It can be seen that the minimum and maximum average wind speed recorded is related to Terracina and Ventotene port at 4.175 and 5.065 (m/s), respectively.…”

Section: Statistical Analysis Of Wind Speedmentioning

confidence: 99%

Methodology Framework for Prioritisation of Renewable Energy Sources in Port Areas

Agostinelli

Neshat

Nezhad

et al. 2022

WIT Transactions on the Built Environment

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Ports play a crucial role in increasing the decarbonisation of urban environments to mitigate the environmental impacts of maritime transport and promote sustainable intermodal mobility. Various efforts have been made to increase energy self-sufficiency using renewable energy sources (RESs) in different ports worldwide. However, the ports played an essential role in the pollution process of the nearest cities due to the short distance and merging with urban areas. In this case, solar and wind were measured using the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) data of four Lazio province ports. Each RES was evaluated using 10 years of monthly data for mapping and 1 year of hourly data for potential assessment and energy converters installation. Furthermore, the time series method has been considered to design and develop better management of RESs for decision making monitoring the energy needs of ports. This time series method has been applied to the generated energy source based on various parameters of the RESs used in port.

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Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism

Cited by 86 publications

References 40 publications

Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects

Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects

Forecasting Renewable Energy Generation with Machine learning and Deep Learning: Current Advances and Future Prospects

Methodology Framework for Prioritisation of Renewable Energy Sources in Port Areas

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