An improved deep belief network based hybrid forecasting method for wind power

Hu, Shuai; Xiang, Yue; Huo, Da; Jawad, Shafqat; Liu, Junyong

doi:10.1016/j.energy.2021.120185

Cited by 55 publications

(19 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of the forecast of the output power of RES, it was performed in the literature [12][13][14][15]. Rodríguez et al [12] established an ANN model to forecast the short-term wind power density in the next 10 minutes to realize the optimization of microgrid control.…”

Section: Output Power Prediction Of Renewable Energy Systemsmentioning

confidence: 99%

Artificial intelligence for hydrogen-based hybrid renewable energy systems: A review with case study

Yan

Agbossou

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

In recent years, with the progress of computer technology, artificial intelligence has been rapidly developed and begun to be applied in industry, economy and other aspects. Besides, with the pursuit of green hydrogen, hydrogen-based hybrid renewable energy systems have become the focus of the development of the hydrogen industry. This paper compares different artificial intelligence applications in hydrogen-based hybrid renewable energy systems and carries out a case study in a typical area. Firstly, this paper summarizes important works in literature, which use artificial intelligence methods to predict the supply chain of the renewable energy system, including the prediction of renewable energy system resources, output power, load demand and terminal electricity price. Secondly, main articles about artificial intelligence optimization algorithms used in renewable energy systems are also summarized, including swarm and non-swarm biological heuristics, physical or chemical heuristics and hybrid optimization algorithms. Finally, a case study is carried out in Tikanlik, Xinjiang, China. Tikanlik’s weather and load data train the artificial neural network to predict system output power. It shows that 99.32% of the relative error of the test set is less than 3%, which proves that this model can achieve good prediction results.

show abstract

Section: Output Power Prediction Of Renewable Energy Systemsmentioning

confidence: 99%

Artificial intelligence for hydrogen-based hybrid renewable energy systems: A review with case study

Yan

Agbossou

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…Hu et al [104] introduced an enhanced hybrid wind forecasting method using DBN, SC, adaptive learning technique and sliding window strategy. Gaussian-Bernoulli restricted Boltzmann machine technique of DBN and adaptive learning technique were implemented to enhance the convergence speed.…”

Section: ) Deep Belief Network-based Hybrid Approachmentioning

confidence: 99%

Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects

et al. 2021

View full text Add to dashboard Cite

Globally, wind energy is growing rapidly and has received huge consideration to fulfill global energy requirements. An accurate wind power forecasting is crucial to achieve a stable and reliable operation of the power grid. However, the unpredictability and stochastic characteristics of wind power affect the grid planning and operation adversely. To address these concerns, a substantial amount of research has been carried out to introduce an efficient wind power forecasting approach. Artificial Intelligence (AI) approaches have demonstrated high precision, better generalization performance and improved learning capability, thus can be ideal to handle unstable, inflexible and intermittent wind power. Recently, AI-based hybrid approaches have become popular due to their high precision, strong adaptability and improved performance. Thus, the goal of this review paper is to present the recent progress of AI-enabled hybrid approaches for wind power forecasting emphasizing classification, structure, strength, weakness and performance analysis. Moreover, this review explores the various influential factors toward the implementations of AI-based hybrid wind power forecasting including data preprocessing, feature selection, hyperparameters adjustment, training algorithm, activation functions and evaluation process. Besides, various key issues, challenges and difficulties are discussed to identify the existing limitations and research gaps. Finally, the review delivers a few selective future proposals that would be valuable to the industrialists and researchers to develop an advanced AI-based hybrid approach for accurate wind power forecasting toward sustainable grid operation.INDEX TERMS Wind power forecasting, artificial intelligence, machine learning, deep learning, optimization, hybrid approaches.

show abstract

“…Zhang et al (2019) propose a bias-correction method using an average, variance trend to correct the simulated wind speed based on historical data. Hu et al (2021) propose a hybrid NWP wind speed correction model based on principal component analysis and improved deep belief network. propose a sequence transfer correction algorithm to correct the NWP wind speed and to obtain the correction results under different time steps, which is suitable for very-short-term WPF.…”

Section: Introductionmentioning

confidence: 99%

Numerical Weather Prediction Correction Strategy for Short-Term Wind Power Forecasting Based on Bidirectional Gated Recurrent Unit and XGBoost

et al. 2022

View full text Add to dashboard Cite

Accurate short-term wind power forecasting (WPF) plays a crucial role in grid scheduling and wind power accommodation. Numerical weather prediction (NWP) wind speed is the fundamental data for short-term WPF. At present, reducing NWP wind speed forecast errors contributes to improving the accuracy of WPF from the perspective of data quality. In this article, a variational mode decomposition combined with bidirectional gated recurrent unit (VMD-BGRU) method for NWP wind speed correction and XGBoost forecasting model are proposed. First, several NWP wind speed sub-series are divided by VMD to obtain more abundant multidimensional timing features. BGRU is applied to establish the potential relation between decomposed NWP wind speed sub-series and measured wind speed and get the proposed wind speed correction model. Then, a more clear regression forecasting model is trained based on XGBoost using historical measured wind speed and power. The corrected NWP wind speed is used to forecast wind power by XGBoost. Finally, the superiority of the proposed method is validated on a wind farm located in China. The results show that the proposed correction model and forecasting model outperform other compared models.

show abstract

An improved deep belief network based hybrid forecasting method for wind power

Cited by 55 publications

References 29 publications

Artificial intelligence for hydrogen-based hybrid renewable energy systems: A review with case study

Artificial intelligence for hydrogen-based hybrid renewable energy systems: A review with case study

Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects

Numerical Weather Prediction Correction Strategy for Short-Term Wind Power Forecasting Based on Bidirectional Gated Recurrent Unit and XGBoost

Contact Info

Product

Resources

About