Retracted: Forecasting model for wind power integrating least squares support vector machine, singular spectrum analysis, deep belief network, and locality‐sensitive hashing

Abstract

Accurate wind power prediction can alleviate the negative influence on power system caused by the integration of wind farms into grid. In this paper, a novel combination model is proposed with the purpose of enhancing short-term wind power prediction precision. Singular spectrum analysis is utilized to decompose the original wind power series into the trend component and the fluctuation component. Then least squares support vector machine (LSSVM) is applied to forecast the trend component while deep belief network (DBN) is utilized to predict the fluctuation component. By this means, the performance advantages of LSSVM and DBN can be brought into full play. Moreover, the locality-sensitive hashing search algorithm is introduced to cluster the nearest training samples to further improve forecasting accuracy. Besides, the effect of LSSVM based on different kernel functions and the number of the nearest samples is investigated. The simulation results show that the normalized root mean square errors of the proposed model based on linear kernel function from1-step to 3-step forecasting are 2.13%, 5.03%, and 7.29%, respectively, which outperforms all the other comparison models. Therefore, it can be concluded that the proposed combination model provides a promising and effective alternative for short-term wind power prediction. KEYWORDS combination forecasting model, deep belief network, least squares support vector machine, locality-sensitive hashing, singular spectrum analysis, wind power

Using enhanced crow search algorithm optimization-extreme learning machine model to forecast short-term wind power

“…The support vector machine (SVM) is applied to the field of short-term wind power forecast due to its strong mapping ability (Habib et al, 2020). SVM also has the problem of random parameters (Fu et al, 2019).…”

Short‐term wind speed multistep combined forecasting model based on two‐stage decomposition and LSTM

“…At the same time, the neural network model is widely used to predict wind power and PV power. Habib et al 17 presented combined model based on least squares support vector machine, singular spectrum analysis, deep belief network, and local sensitive hash model. Wang et al 18 used LSTM‐RNN cyclic neural network model to predict PV power and adopted BPNN network model for minute prediction of solar radiation 19 .…”

Approach for short-term wind power prediction via kernel principal component analysis and echo state network optimized by improved particle swarm optimization algorithm

“…(8) In recent years, the deep learning model has been developed deeply, and it is also widely used in the prediction of wind power. These deep learning models include recurrent neural network (Liu et al, 2021;Wang et al, 2021), long short-term memory (Dinler, 2021;Ko et al, 2021), deep belief network (Habib et al, 2020;Wang et al, 2018b), and so forth.…”

“…In recent years, the deep learning model has been developed deeply, and it is also widely used in the prediction of wind power. These deep learning models include recurrent neural network (Liu et al, 2021; Wang et al, 2021), long short-term memory (Dinler, 2021; Ko et al, 2021), deep belief network (Habib et al, 2020; Wang et al, 2018b), and so forth. Compared with traditional machine learning algorithms, deep learning model emphasizes learning from massive data, which can solve the problems of high dimension, clutter, and high noise in massive data that traditional machine learning algorithms are difficult to deal with.…”