Intelligent Neural Learning Models for Multi-step Wind Speed Forecasting in Renewable Energy Applications

Deepa, S. N.; Banerjee, Abhik

doi:10.1007/s40313-021-00862-2

Cited by 5 publications

(2 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CNN and LSTM-based wind power forecasting models supported by some more effective machine learning methods have been proposed although it is known that such forecasting studies are based on DNN [17]. [18] and similar studies are based on making the prediction of wind speed, which is the most basic variable affecting that power, using deep learning techniques, rather than forecasting the wind power. In [19], an approach based on CNN and LSTM is proposed for short-time energy generation prediction of renewable energy sources.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Assessment and Comparative Analysis of Deep Learning Models for Large-Scale Renewable Energy Power Generation Prediction: A National Perspective

Aksoy,

Genc

2024

Preprint

View full text Add to dashboard Cite

In forecasting the future energy consumption and generation at the national level, strategic planning for both the medium and long term becomes imperative. The trajectory of renewable energy contribution to the smart grid, whether in the short or long term, significantly influences the grid's operational dynamics. This study is dedicated to the development of deep learning-based power prediction models tailored for a nation characterized by extensive reliance on renewable energy sources. Specifically, four distinct deep learning methodologies—namely,Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU)—have been formulated and rigorously examined for their efficacy within this domain. These models have been individually tailored for the prediction of power generation from solar photovoltaic plants and wind turbines, leveraging the inherent advantages of architectures featuring memory cells. The outcomes of these predictive models, which encompass the entire spectrum of renewable energy sources, exhibit remarkable precision. Furthermore, an exhaustive analysis of the performance metrics derived from these models has been conducted, affording a comprehensive and nuanced comparison. The findings contribute valuable insights into the suitability and effectiveness of the aforementioned deep learning methodologies in forecasting power generation from renewable sources at a national scale.

show abstract

Section: Introductionmentioning

confidence: 99%

Comprehensive Assessment and Comparative Analysis of Deep Learning Models for Large-Scale Renewable Energy Power Generation Prediction: A National Perspective

Aksoy,

Genc

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…He et al [16] developed a combined model for wind power forecasting, which was validated with data obtained from a wind farm in Northwest China. Jalali et al [17], Deepa and Banerjee [18] implemented convolutional neural networks (CNN) for short-term wind power prediction, while in [19] wavelet neural networks were also implemented for short-term wind power prediction, demonstrating the usefulness of neural networks in this type of study. Research has also been conducted to predict short-term wind speed, using wind speed measurements from neighboring locations to improve the prediction results [20].…”

Section: Introductionmentioning

confidence: 99%

Wind power prediction using a nonlinear autoregressive exogenous model network: the case of Santa Marta, Colombia

Guillot

Leal

Robles-Algarín

et al. 2023

IJECE

View full text Add to dashboard Cite

<span lang="EN-US">The monitoring of wind installations is key for predicting their future behavior, due to the strong dependence on weather conditions and the stochastic nature of the wind. However, in some places, in situ measurements are not always available. In this paper, active power predictions for the city of Santa Marta-Colombia using a nonlinear autoregressive exogenous model (NARX) network were performed. The network was trained with a reliable dataset from a wind farm located in Turkey, because the meteorological data from the city of Santa Marta are unavailable or unreliable on certain dates. Three training and testing cases were designed, with different input variables and varying the network target between active power and wind speed. The dataset was obtained from the Kaggle platform, and is made up of five variables: date, active power, wind speed, theoretical power, and wind direction; each with 50,530 samples, which were preprocessed and, in some cases, normalized, to facilitate the neural network learning. For the training, testing and validation processes, a correlation coefficient of 0.9589 was obtained for the best scenario with the data from Turkey, while the best correlation coefficient for the data from Santa Marta was 0.8537.</span>

show abstract

ANFIS Wind Speed Estimator-Based Output Feedback Near-Optimal MPPT Control for PMSG Wind Turbine

Phan

Tung

Mai³

et al. 2022

J Control Autom Electr Syst

View full text Add to dashboard Cite

Intelligent Neural Learning Models for Multi-step Wind Speed Forecasting in Renewable Energy Applications

Cited by 5 publications

References 61 publications

Comprehensive Assessment and Comparative Analysis of Deep Learning Models for Large-Scale Renewable Energy Power Generation Prediction: A National Perspective

Comprehensive Assessment and Comparative Analysis of Deep Learning Models for Large-Scale Renewable Energy Power Generation Prediction: A National Perspective

Wind power prediction using a nonlinear autoregressive exogenous model network: the case of Santa Marta, Colombia

ANFIS Wind Speed Estimator-Based Output Feedback Near-Optimal MPPT Control for PMSG Wind Turbine

Contact Info

Product

Resources

About