Developing a hybrid probabilistic model for short-term wind speed forecasting

Zhang, Xiaobo

doi:10.1007/s10489-022-03644-8

Cited by 16 publications

(3 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They established a PSR-IWOA-QRGRU model for wind speed interval prediction by superimposing the predictions from different components. A hybrid generalized forecasting framework was developed by a study [ 30 ], which proposed a probabilistic wind speed prediction method in the form of point estimation and interval prediction. This approach combines empirical wavelet transform with neural network-based QR to enhance the generalization and stability of probabilistic forecasting.…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic Power Prediction Based on Hybrid Deep Learning Networks and Meteorological Data

Guo,

Xu,

Wang

et al. 2024

Sensors

View full text Add to dashboard Cite

Conventional point prediction methods encounter challenges in accurately capturing the inherent uncertainty associated with photovoltaic power due to its stochastic and volatile nature. To address this challenge, we developed a robust prediction model called QRKDDN (quantile regression and kernel density estimation deep learning network) by leveraging historical meteorological data in conjunction with photovoltaic power data. Our aim is to enhance the accuracy of deterministic predictions, interval predictions, and probabilistic predictions by incorporating quantile regression (QR) and kernel density estimation (KDE) techniques. The proposed method utilizes the Pearson correlation coefficient for selecting relevant meteorological factors, employs a Gaussian Mixture Model (GMM) for clustering similar days, and constructs a deep learning prediction model based on a convolutional neural network (CNN) combined with a bidirectional gated recurrent unit (BiGRU) and attention mechanism. The experimental results obtained using the dataset from the Australian DKASC Research Centre unequivocally demonstrate the exceptional performance of QRKDDN in deterministic, interval, and probabilistic predictions for photovoltaic (PV) power generation. The effectiveness of QRKDDN was further validated through ablation experiments and comparisons with classical machine learning models.

show abstract

Section: Introductionmentioning

confidence: 99%

Photovoltaic Power Prediction Based on Hybrid Deep Learning Networks and Meteorological Data

Guo,

Xu,

Wang

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…This method employs wavelet packet decomposition to compress and remove noise from the data, resulting in a faster and more precise model. Lastly, a versatile forecasting framework that combines empirical wavelet transform and neural network-based quantile regression to enhance the robustness and generalization of wind speed predictions has also been developed 35) .…”

Section: Introductionmentioning

confidence: 99%

Promoting Wind Energy by Robust Wind Speed Forecasting Using Machine Learning Algorithms Optimization

Aminuddin,

Nurry Widya Hesty,

Nina Konitat Supriatna

et al. 2024

Evergreen

View full text Add to dashboard Cite

Accurate, efficient, and stable wind prediction systems for wind turbines are critical to ensuring the operational safety and optimum design of power systems. This study deliberated hyperparameter fine-tuning of ten Machine Learning (ML) models to obtain the best short-term wind speed forecasting model by evaluating the Root-Mean-Square Error (RMSE), Mean Absolute Error (MAE), Correlation, and runtime. The Random Forest (RF) and gradient-boosted tree (GBT) had the best overall performance; however, RF has a much longer training time than GBT. This paper's findings can assist researchers and practitioners in developing the most effective data-driven methods for wind speed and power-generated forecasting.

show abstract

“…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

Developing a hybrid probabilistic model for short-term wind speed forecasting

Cited by 16 publications

References 43 publications

Photovoltaic Power Prediction Based on Hybrid Deep Learning Networks and Meteorological Data

Photovoltaic Power Prediction Based on Hybrid Deep Learning Networks and Meteorological Data

Promoting Wind Energy by Robust Wind Speed Forecasting Using Machine Learning Algorithms Optimization

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

Contact Info

Product

Resources

About