A short-term wind speed prediction method utilizing novel hybrid deep learning algorithms to correct numerical weather forecasting

Yan, Huang; Lihua, Mi; Shen, Lian; Cai, C.S.; Liu, Yuchen; Li, Kai; Xu, Guoji

doi:10.1016/j.apenergy.2022.118777

Cited by 70 publications

(9 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, the commonly used hyperparametric optimization methods include grid search, random search, and Bayesian optimization. [50][51][52] In this study, the grid search algorithm 50 was used to optimize the hyper-parameters of CNN. The optimized learning rate was 0.003, and the learning rate decreased by epochs.…”

Section: Training and Validationmentioning

confidence: 99%

Data quality evaluation for bridge structural health monitoring based on deep learning and frequency-domain information

Deng

Zhong

et al. 2022

Structural Health Monitoring

View full text Add to dashboard Cite

Abnormal data recognition is of great importance in structural health monitoring. Most of the existing studies focused on detecting obvious abnormal data, which has obvious abnormal time-domain waveform. Pseudo normal data, which is normally looking in time domain but chaotic in frequency domain, did not receive enough attention and were likely to be misclassified as normal data. As a result, structural performance may be incorrectly evaluated because pseudo normal data are not recognized and eliminated from the monitoring data. This study developed a novel quality evaluation framework for monitoring data of bridge dynamic response. The main novelty of the proposed framework is that the frequency-domain information is used to characterize the quality of the monitoring data so that the normal data, obvious abnormal data, and pseudo normal data can be accurately distinguished. In the framework, the frequency-domain information was obtained by fast Fourier transform (FFT), and the Gramian angular field images converted from FFT results were used to train a designed convolutional neural network (CNN). The cable acceleration data of the Waitan cable-stayed bridge were taken as an example to verify the accuracy of the proposed framework. Compared with the CNN models based on time-domain images and time-frequency stacked images, this framework can better recognize pseudo normal data from the monitoring data. In large-scale testing, the classification accuracy of all channels was more than 96%. Finally, two cable acceleration sensors of another cable-stayed bridge were used to demonstrate the feasibility of the framework in cross-object application. The results show that the framework has good accuracy and robustness in large-scale monitoring data quality evaluation and cross-object application.

show abstract

Section: Training and Validationmentioning

confidence: 99%

Data quality evaluation for bridge structural health monitoring based on deep learning and frequency-domain information

Deng

Zhong

et al. 2022

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…It is difficult for general models to faithfully represent the development patterns buried in large and heavily sampled datasets. Physical model-based approaches and statistical modelling techniques are the two main groups into which wind speed prediction procedures can be classified [12]. A set of physical models with numerical parameters that characterize local meteorological and geographic characteristics such as temperature, atmospheric pressure, surface roughness, and barriers are the foundation of numerical weather prediction (NWP) systems [13,14].…”

Section: Introductionmentioning

confidence: 99%

Performance enhancement of short-term wind speed forecasting model using Realtime data

Ashraf,

Raza,

Arshad

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

The ever-increasing demand for electricity has presented a grave threat to traditional energy sources, which are finite, rapidly depleting, and have a detrimental environmental impact. These shortcomings of conventional energy resources have caused the globe to switch from traditional to renewable energy sources. Wind power significantly contributes to carbon-free energy because it is widely accessible, inexpensive, and produces no harmful emissions. Better and more efficient renewable wind power production relies on accurate wind speed predictions. Accurate short-term wind speed forecasting is essential for effectively handling unsteady wind power generation and ensuring that wind turbines operate safely. The significant stochastic nature of the wind speed and its dynamic unpredictability makes it difficult to forecast. This paper develops a hybrid model, L-LG-S, for precise short-term wind speed forecasting to address problems in wind speed forecasting. In this research, state-of-the-art machine learning and deep learning algorithms employed in wind speed forecasting are compared with the proposed approach. The effectiveness of the proposed hybrid model is tested using real-world wind speed data from a wind turbine located in the city of Karachi, Pakistan. Moreover, the mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) are used as accuracy evaluation indices. Experimental results show that the proposed model outperforms the state-of-the-art legacy models in terms of accuracy for short-term wind speed in training, validation and test predictions by 98% respectively.

show abstract

“…For the efficient use of wind energy, wind speed is considered one of the most important parameters to predict wind turbine power including a selection of a site and the optimal wind turbine size for a particular site. Wind speed can be predicted using traditional predicting methods [29], and more recently it has been increasingly observed using artificial intelligence methods [30]. Hybrid models become more deployed due to their design advancements and operating benefits leading to improve performance of stand-alone models.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Prediction of the Wind Speed Based on a Learning Process Control Algorithm in Isolated Power Systems

et al. 2023

View full text Add to dashboard Cite

Predicting the variability of wind energy resources at different time scales is extremely important for effective energy management. The need to obtain the most accurate forecast of wind speed due to its high degree of volatility is particularly acute since this can significantly improve the planning of wind energy production, reduce costs and improve the use of resources. In this study, a method for predicting the speed of wind flow in an isolated power system of the Gorno-Badakhshan Autonomous Oblast (GBAO), based on the use of a neural network with a learning process control algorithm, is proposed. Predicting is performed for four seasons of the year, based on hourly retrospective meteorological data of wind speed observations. The obtained wind speed average error forecasting ranged from 20–28% for a day ahead. The prediction results serve as a basis for optimizing the energy consumption of individual generating consumers to minimize their financial and technical costs. In addition, this study takes into account the possibility of exporting electricity to a neighboring country as an additional income line for the isolated GBAO power system during periods of excess energy from hydropower plants (March–September), which is a systematic vision of solving the problem of improving energy efficiency in the conditions of autonomous power supply.

show abstract

A short-term wind speed prediction method utilizing novel hybrid deep learning algorithms to correct numerical weather forecasting

Cited by 70 publications

References 33 publications

Data quality evaluation for bridge structural health monitoring based on deep learning and frequency-domain information

Data quality evaluation for bridge structural health monitoring based on deep learning and frequency-domain information

Performance enhancement of short-term wind speed forecasting model using Realtime data

Short-Term Prediction of the Wind Speed Based on a Learning Process Control Algorithm in Isolated Power Systems

Contact Info

Product

Resources

About