The rest of this paper is organized as follows. The main reason for using nonparametric or data-driven techniques is that no previous assumptions are required to analyze and perform forecasts, such as the normality of residuals, the stationary of the time series, or a predefined model structure.
Briefly, the SSA method decomposes a time series into a number of components with simpler structures, such as a slowly varying trend, oscillations, and noise. Unlike physical models, statistical methods make forecasts by finding relationships using historical wind speed data and, sometimes, other variables e.
Many methods of forecasting wind speed have been proposed. The core of a smart grid is the integration of secure and high-speed data communication—based on advanced computers, electronic equipment, intelligent components, and more—to operate the mixture system intelligently and effectively [ 1415 ].
As fossil fuel prices increase and renewable technologies mature, renewable energies are becoming increasingly competitive; a global effort will be required to construct a low-carbon society [ 2 ]. These methods can be categorized into different groups. One of the main advantages of SSA compared to other Stock based on ssa and svm methods is that only two parameters are used to simulate the time series in many implementations [ 60 ], and no model is assumed before the SSA method is adopted; the subspace-based model is built adaptively.
After optimization, the SSA-based forecasting models are applied to forecasting the immediate short-term wind speed and are adopted at ten wind farms in China. Simulation results present the effectiveness of the proposed method in characterizing and predicting time series.
The methods contain the basic processing of physical methods. Neural networks can learn from past data and recognize hidden patterns or relationships in historical observations and use them to forecast future values.
Often, the results of the physical prediction merely represent the first step towards forecasting the wind; thus, the physically predicted wind speed can be regarded as an auxiliary input to other statistical models [ 27 ].
Chinese wind energy resource distribution and wind farms in Shandong province. The nondeterminacy seriously limits wind power penetration and threatens grid security. Generally, in these models, historical wind speed is used as an input at one of the points.
Embedding Embedding can be regarded as a mapping that converts a one-dimensional time series into the multidimensional series. A brief review of this paper and the future research are in Section 6.
Moreover, an ensemble classifier based on majority voting is implemented on top of these classifiers. The employed models meet two goals: The results indicate that prediction errors resulting from the Bayesian combination approach always become smaller, which is in contrast to artificial neural networks, whose performance is not consistent when the site or evaluation criterion changes [ 3 ].
Typically, prediction methods using NWP forecasts outperform statistical approaches after a 3—6 h look-ahead time, whereas statistical approaches turn out to be quite reliable for very short-term forecasts, that is, less than 6 h.
Wavelet analysis is a powerful tool for PCA and has been used for feature extraction and denoising wind speed for a long time. Some of these methods assume a time series model structure and then try to identify its parameters.
The primary reconstruction process of SSA is to decompose the original series into a set of subseries and then reconstruct the original series [ 5758 ]. To reduce energy-related toxic emissions in the current energy infrastructure, renewable energy should be utilized with the goal of maintaining sustainable development and creating a better ecological environment.
The aim of SSA is to obtain a decomposition of the original series into a set of independent and interpretable components, which include a slowly varying trend, oscillatory components, and random noise [ 57 ]. The success rates show that the results are satisfactory to claim the methods implemented in this study can be spread to future research with similar data sets from other countries.
In the research several feature extraction methodologies are implemented on both of the data sources, which are the stock market values and economic news. The forecasting approach can be determined based on the available information and the time scale in question, which will affect its application.
Combination models include the adaptive particle swarm optimization-based combined method [ 42 ], wavelet transform combination model based on a neural network and an evolutionary algorithm [ 43 ], wavelet transforms and adaptive models [ 44 ], and an adaptive fuzzy combination model based on the self-organizing map and support vector regression [ 45 ].Based on the structural risk minimization, support vector machine is a new method of data mining.
Since it has effectively solved complicated problems of classification and prediction, it has been widely used in many cross-disciplinary fields. SVM without these price features, the combination predictive methods——the EEMD-SVM and the SSA-SVM, which combine the price features into the SVMs perform better, with the best prediction to.
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FILTER X. List of computer science publications by Zhifang He.Download