Time Series Analysis and Forecasting of Wind Speed Data

Elsaraiti, Meftah; Merabet, Adel; Al‐Durra, Ahmed

doi:10.1109/ias.2019.8912392

Cited by 7 publications

(7 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ANN and ARIMA models are still suitable for the short-term prediction of wind speed [11]. A trial was conducted to obtain the structure of the autoregressive integrated moving average (ARIMA) model, which will be the most efficient based on the least error, by comparing the real time series and the forecasting [12,13]. The ARIMA model was found to be effective for short-term forecasting [14].…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed

Elsaraiti

Merabet

2021

Energies

Self Cite

View full text Add to dashboard Cite

Forecasting wind speed has become one of the most attractive topics to researchers in the field of renewable energy due to its use in generating clean energy, and the capacity for integrating it into the electric grid. There are several methods and models for time series forecasting at the present time. Advancements in deep learning methods characterize the possibility of establishing a more developed multistep prediction model than shallow neural networks (SNNs). However, the accuracy and adequacy of long-term wind speed prediction is not yet well resolved. This study aims to find the most effective predictive model for time series, with less errors and higher accuracy in the predictions, using artificial neural networks (ANNs), recurrent neural networks (RNNs), and long short-term memory (LSTM), which is a special type of RNN model, compared to the common autoregressive integrated moving average (ARIMA). The results are measured by the root mean square error (RMSE) method. The comparison result shows that the LSTM method is more accurate than ARIMA.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed

Elsaraiti

Merabet

2021

Energies

Self Cite

View full text Add to dashboard Cite

show abstract

“…In contrast to RMSE, the MAE metric weights all values equally, and thus does not add additional weight to extreme forecasting events. This metric has been widely used in the renewable energy industry to assess the effectiveness of the forecast [53]. MAPE, on the other hand, represents the quantum of error relative to the actual data expressed as percentage, thereby directly indicating the accuracy of the model [54].…”

Section: Evaluation Metricsmentioning

confidence: 99%

Short-Term Forecasting of Wind Energy: A Comparison of Deep Learning Frameworks

2021

View full text Add to dashboard Cite

Wind energy has been recognized as the most promising and economical renewable energy source, attracting increasing attention in recent years. However, considering the variability and uncertainty of wind energy, accurate forecasting is crucial to propel high levels of wind energy penetration within electricity markets. In this paper, a comparative framework is proposed where a suite of long short-term memory (LSTM) recurrent neural networks (RNN) models, inclusive of standard, bidirectional, stacked, convolutional, and autoencoder architectures, are implemented to address the existing gaps and limitations of reported wind power forecasting methodologies. These integrated networks are implemented through an iterative process of varying hyperparameters to better assess their effect, and the overall performance of each architecture, when tackling one-hour to three-hours ahead wind power forecasting. The corresponding validation is carried out through hourly wind power data from the Spanish electricity market, collected between 2014 and 2020. The proposed comparative error analysis shows that, overall, the models tend to showcase low error variability and better performance when the networks are able to learn in weekly sequences. The model with the best performance in forecasting one-hour ahead wind power is the stacked LSTM, implemented with weekly learning input sequences, with an average MAPE improvement of roughly 6, 7, and 49%, when compared to standard, bidirectional, and convolutional LSTM models, respectively. In the case of two to three-hours ahead forecasting, the model with the best overall performance is the bidirectional LSTM implemented with weekly learning input sequences, showcasing an average improved MAPE performance from 2 to 23% when compared to the other LSTM architectures implemented.

show abstract

“…Maldonado-Correa et al (2021) [3] state that the mean absolute error (MAE) and root-mean-squared error (RMSE) are the most used ways of assessing error in wind power prediction models. Two measures were used to evaluate the accuracy of several wind power prediction models in this research.…”

Section: Handling Missing Valuesmentioning

confidence: 99%

A Comparative Study on Wind Power Forecasting Models Based on the Use of LSTM

Ali Abdulrhman Salihi, Merdin Danismaz

2023

tjjpt

View full text Add to dashboard Cite

In the context of wind power generation's growing significance, this research tackles the critical problem of improving power system stability by reducing peak load and frequency control pressures using sophisticated wind power forecasting methods. Using the rapidly developing area of artificial intelligence and neural networks in particular, the study explores the efficacy of Long Short-Term Memory (LSTM), a recurrent neural network designed for event forecasting in time series data with long intervals and temporal delays. The research presents a novel forecasting model, LSTM that enhances prediction accuracy. This is corroborated by the calculated Root Mean Squared Error (RMSE) of 0.6782 and Mean Absolute Error (MAE) of 0.4614. The sustainability and dependability of wind energy integration into power systems may be enhanced by these findings, which highlight the possibility for more efficient and rapid convergence processes in wind power forecasting.

show abstract

Time Series Analysis and Forecasting of Wind Speed Data

Cited by 7 publications

References 18 publications

A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed

A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed

Short-Term Forecasting of Wind Energy: A Comparison of Deep Learning Frameworks

A Comparative Study on Wind Power Forecasting Models Based on the Use of LSTM

Contact Info

Product

Resources

About