Deterministic and probabilistic interval prediction for wind farm based on VMD and weighted LS-SVM

Gendeel, Mohammed; Zhang, Yuxian; Xia, Qian; Xing, Zuoxia

doi:10.1080/15567036.2019.1632980

Cited by 23 publications

(9 citation statements)

References 42 publications

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“…PICP 256 and PINRW/PINAW 257 , 258 , 259 , 260 are two types of fundamental evaluation indexes, where PICP evaluates the total probability of the actual value falling within the interval, whereas PINAW/PINRW evaluates the width of the interval. When PINAW/PINRW reaches high levels, the PICP can be very large (perhaps even 100%), which reduces the reliability of the predictive results.…”

Section: State-of-the-art Probabilistic Forecasting Methodsmentioning

confidence: 99%

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An overview of deterministic and probabilistic forecasting methods of wind energy

Xie

et al. 2023

iScience

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Section: State-of-the-art Probabilistic Forecasting Methodsmentioning

confidence: 99%

“…In view of the contradiction between interval coverage and interval width, a comprehensive index named CWC 257 , 258 , 259 , 260 was developed and further improved to better assess the overall performance of prediction models. The original and improved CWC expressions are as follows.…”

Section: State-of-the-art Probabilistic Forecasting Methodsmentioning

confidence: 99%

An overview of deterministic and probabilistic forecasting methods of wind energy

Xie

et al. 2023

iScience

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“…Research shows that the physical model may act better in wind speed or wind power forecasting6 h to 1 day ahead, and it has usually been applied in power system management and trading systems [20,21]. The statistical method is the most common in applications forecasting less than 6 h ahead, which could benefit the wind turbine control and tracking [22,23].…”

Section: Forecasting Of Wind and Wave Energymentioning

confidence: 99%

Review on Deep Learning Research and Applications in Wind and Wave Energy

2022

Energies

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Wind energy and wave energy are considered to have enormous potential as renewable energy sources in the energy system to make great contributions in transitioning from fossil fuel to renewable energy. However, the uncertain, erratic, and complicated scenarios, as well as the tremendous amount of information and corresponding parameters, associated with wind and wave energy harvesting are difficult to handle. In the field of big data handing and mining, artificial intelligence plays a critical and efficient role in energy system transition, harvesting and related applications. The derivative method of deep learning and its surrounding prolongation structures are expanding more maturely in many fields of applications in the last decade. Even though both wind and wave energy have the characteristics of instability, more and more applications have implemented using these two renewable energy sources with the support of deep learning methods. This paper systematically reviews and summarizes the different models, methods and applications where the deep learning method has been applied in wind and wave energy. The accuracy and effectiveness of different methods on a similar application were compared. This paper concludes that applications supported by deep learning have enormous potential in terms of energy optimization, harvesting, management, forecasting, behavior exploration and identification.

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“…Reference (Bilgili and Sahin 2013) explained that the artificial neural network method can be used to measure data of surrounding stations to successfully predict the wind speed of any target station. The study in (Gendeel et al 2019) proposed a method based on Variational Mode Decomposition (VMD) and Least Square Support Vector Machine (LS-SVM) to optimize the power system planning and operation of wind farms. Compared with these existing studies, this paper applies machine learning algorithms to analyze the literature review research in the field of wind power.…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning algorithms in wind power: a review

Deng

Xue-hua

Zhou

et al. 2021

Energy Sources, Part A: Recovery, Utilization, and Environmenta

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Literature review is an overview of existing research, it can also be used to understand research trends and directions. In recent years, the literature associated with wind power has grown rapidly, and it seems inadequate to rely on human resources to study all papers. Very few studies have used machine learning algorithms and visualization approaches to analyze the trends and directions in wind power. To explore these, we collected 50,579 articles (2012-2019) from Web of Science Core Collection (WoSCC) and 785 papers (2012-2019) from China National Knowledge Infrastructure (CNKI). We applied machine learning algorithms including text mining, word segmentation, T-Distributed Stochastic Neighbor Embedding (T-SNE), Auto-Encoder (AE), visual imagery and other methods to analyze and visually display literature in the field of wind power via analysis of the trends with time-sequence, hotspots in abstracts and keywords, and spatial distribution. China, the United States, and Iran are the top three countries in the field of wind power. Through analyzing the trends between 2012-2019, we find that research hotspots have changed. The usage rate of terms such as Power Generation Control, Power Grids, Wind Power Plants, and Wind Turbines has significantly increased, and the corresponding growth rates are 10.91%, 7.06%, 6.28% and 4.33%, respectively. This study also provides information on the relationship of words of abstracts in papers, which shows that these words are mainly divided into four categories: forecasting, optimization, investment, energy and equipment. An implication of this study is that machine learning algorithms may play an important role in the analysis of wind power literature.

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Deterministic and probabilistic interval prediction for wind farm based on VMD and weighted LS-SVM

Cited by 23 publications

References 42 publications

An overview of deterministic and probabilistic forecasting methods of wind energy

An overview of deterministic and probabilistic forecasting methods of wind energy

Review on Deep Learning Research and Applications in Wind and Wave Energy

Application of machine learning algorithms in wind power: a review

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