The attention-assisted ordinary differential equation networks for short-term probabilistic wind power predictions

Liu, Xin; Yang, Luoxiao

doi:10.1016/j.apenergy.2022.119794

Cited by 18 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, most research focuses only on deterministic prediction, which generates a single point prediction of future wind speeds, while ignoring the adverse effects of wind speed uncertainty on the power system. Different from point forecasting, probabilistic forecasting also provides uncertainty, that is, accurate estimation of the fluctuation range of the predicted wind speed, which provides more valuable reference information for the decision of the dispatcher [26][27][28]. The quantile regression neural network, which combines the advantages of a neural network and quantile regression, is often used in wind speed probabilistic forecasting [29,30].…”

Section: Introductionmentioning

confidence: 99%

A Point-Interval Forecasting Method for Wind Speed Using Improved Wild Horse Optimization Algorithm and Ensemble Learning

Guo,

Zhu,

Hao

et al. 2023

Sustainability

View full text Add to dashboard Cite

With the implementation of the green development strategy and the “double carbon goal”, as an important energy for sustainable development, wind power has been widely researched and vigorously developed across the world. Wind speed prediction has a major impact on the grid dispatching of wind power connection. Most current studies only focus on the deterministic prediction of wind speed. However, the traditional deterministic forecast only provides the single wind speed prediction results and cannot meet the diverse demands of dispatchers. To bridge the gap, a wind speed point-interval forecasting method is proposed that utilizes empirical wavelet transform, an improved wild horse optimization algorithm, a multi-predictor, and improved kernel density estimation. This method decomposes the wind speed sequence into stationary subsequences through empirical wavelet transform, and then optimizes three basic learners with completely different learning mechanisms to form an ensemble model using the modified wild horse optimization algorithm. Finally, the uncertainty is analysed using an improved kernel density estimation. The datasets of three sites from America’s national renewable energy laboratory are used for comparison experiments with other models, and the predictions are discussed from different angles. The simulation results demonstrate that the model can produce high-precision deterministic results and high-quality probabilistic results. The reference information the model provides can be extremely valuable for scheduling operators.

show abstract

Section: Introductionmentioning

confidence: 99%

A Point-Interval Forecasting Method for Wind Speed Using Improved Wild Horse Optimization Algorithm and Ensemble Learning

Guo,

Zhu,

Hao

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…These techniques generate short-term forecasts by analysing statistical features found in historical wind power data. However, wind power fluctuates a lot, and conventional models have a hard time explaining these intricate patterns (Liu et al, 2022). AI technologies, with deep learning as a prominent example, possess strong pattern recognition and data processing capabilities.…”

mentioning

confidence: 99%

Short-term wind power forecasting based on dual attention mechanism and gated recurrent unit neural network

Xu,

Liu,

Fan

et al. 2024

Front. Energy Res.

View full text Add to dashboard Cite

Accurate wind power forecasting is essential for both optimal grid scheduling and the massive absorption of wind power into the grid. However, the continuous changes in the contribution of various meteorological features to the forecasting of wind power output under different time or weather conditions, and the overlapping of wind power sequence cycles, make forecasting challenging. To address these problems, a short-term wind power forecasting model is established that integrates a gated recurrent unit (GRU) network with a dual attention mechanism (DAM). To compute the contributions of different features in real time, historical wind power data and meteorological information are first extracted using a feature attention mechanism (FAM). The feature sequences collected by the FAM are then used by the GRU network for preliminary forecasting. Subsequently, one-dimensional convolution employing several distinct convolution kernels is used to filter the GRU outputs. In addition, a multi-head time attention mechanism (MHTAM) is proposed and a Gaussian bias is introduced to assign different weights to different time steps of each modality. The final forecast results are produced by combining the outputs of the MHTAM. The results of the simulation experiment show that for 5-h, 10-h, and 20-h short-term wind power forecasting, the establishedÂ DAM-GRU model performs better than comparative models on the basis of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared (R2), Square sum error (SSE), Mean absolute percentile error (MAPE), and Relative root mean square error (RRMSE) index.

show abstract

Development and trending of deep learning methods for wind power predictions

Liu,

Zhang

2024

Artif Intell Rev

View full text Add to dashboard Cite

With the increasing data availability in wind power production processes due to advanced sensing technologies, data-driven models have become prevalent in studying wind power prediction (WPP) methods. Deep learning models have gained popularity in recent years due to their ability of handling high-dimensional input, automating data feature engineering, and providing high flexibility in modeling. However, with a large volume of deep learning based WPP studies developed in recent literature, it is important to survey the existing developments and their contributions in solving the issue of wind power uncertainty. This paper revisits deep learning-based wind power prediction studies from two perspectives, deep learning-enabled WPP formulations and developed deep learning methods. The advancement of WPP formulations is summarized from the following perspectives, the considered input and output designs as well as the performance evaluation metrics. The technical aspect review of deep learning leveraged in WPPs focuses on its advancement in feature processing and prediction model development. To derive a more insightful conclusion on the so-far development, over 140 recent deep learning-based WPP studies have been covered. Meanwhile, we have also conducted a comparative study on a set of deep models widely used in WPP studies and recently developed in the machine learning community. Results show that DLinear obtains more than 2% improvements by benchmarking a set of strong deep learning models. Potential research directions for WPPs, which can bring profound impacts, are also highlighted.

show abstract

The attention-assisted ordinary differential equation networks for short-term probabilistic wind power predictions

Cited by 18 publications

References 32 publications

A Point-Interval Forecasting Method for Wind Speed Using Improved Wild Horse Optimization Algorithm and Ensemble Learning

A Point-Interval Forecasting Method for Wind Speed Using Improved Wild Horse Optimization Algorithm and Ensemble Learning

Short-term wind power forecasting based on dual attention mechanism and gated recurrent unit neural network

Development and trending of deep learning methods for wind power predictions

Contact Info

Product

Resources

About