Wind Power Ramp Forecasting Based on Least-Square Support Vector Machine

Forecasting large and fast variations of wind power (the so-called ramps) helps achieve the integration of large amounts of wind energy. This paper presents a survey on wind power ramp forecasting, reflecting the increasing interest on this topic observed since 2007. Three main aspects were identified from the literature: wind power ramp definition, ramp underlying meteorological causes and experiences in predicting ramps. In this framework, we additionally outline a number of recommendations and potential lines of research.

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“…Table 1 References of the works considered in Fig. 1 [57][58][59][60] - [61,62] [ [63][64][65][66] *Denotes data up to August 2014.…”

Section: Introductionmentioning

confidence: 99%

A review on the recent history of wind power ramp forecasting

Gallego-Castillo

Cuerva-Tejero

López-García

2015

Renewable and Sustainable Energy Reviews

146

113

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“…For example, Gan and Ke [34] used least square support vector machine (LSSVM) to predict wind power ramps. The most widely used method is the multilayer-perceptron (MLP) neural network [18,19,[35][36][37][38].…”

Section: State-of-the-art Methods (Soa)mentioning

confidence: 99%

Improving the prediction of wind power ramps using texture extraction techniques applied to atmospheric pressure fields

Musilek

Lozowski

2017

Int J Data Sci Anal

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Much recent research in wind power forecasting has been focused on predicting large, sudden changes in wind power output, called wind ramps. State-of-the-art wind ramp prediction methods estimate future wind ramps from forecast power time series. We suggest that, by analyzing the weather associated with wind ramps, their forecasting can be improved. In particular, we propose a new method for wind ramp forecasting based on the analysis of forecast atmospheric pressure fields. Feature vectors relating to the pressure gradient are extracted from the pressure fields using an image texture extraction technique, called Gabor filtering. Numerical experiments show that these Gabor feature vectors are well correlated with power generation. They are used as inputs to a new wind power forecasting model. Compared with a basic state-of-the-art wind power forecasting model that does not use Gabor features as input, the proposed model exhibits better performance for power prediction, for two of the three wind farms chosen for this study. However, the ability of the model to forecast actual wind ramps is worse than that of the basic model, as measured by ramp capture rate and forecast accuracy. We also describe a second method to pre- Earth and Atmospheric Sciences, University of Alberta, Edmonton, Canada dict the magnitude of a sudden power change (wind ramp), using several input variables, in addition to Gabor features. Numerical experiments show that this second approach has better performance than the basic model, with respect to ramp capture rate and forecast accuracy. We suggest that it could be used operationally to supplement a current state-of-the-art ramp prediction model. We present an example of using this approach to provide a warning of a potential ramp.

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“…In order to test the prediction performance of the proposed prediction model, this paper selects seven prediction models including the persistence model (Weber et al, 2019), ARIMA (Chen et al, 2010), genetic algorithm optimized SVM (GA-SVM) (Zhang et al, 2019b), particle swarm algorithm optimized SVM (PSO-SVM) (Lu and Liu, 2015), LSSVM (Gan and Ke, 2014), hybrid kernel function SVM (H-SVM) (Tian et al, 2018), and extreme learning machine (ELM) (Wan et al, 2013) as the comparison model. The specific parameters of these comparison models are shown in Table 1 below (the persistence model does not need parameter setting.…”

Section: Comparison Modelsmentioning

confidence: 99%

“…Because the calculation process and structure of the statistical model are relatively simple and stable in the short-term scale, it is also used as a benchmark model in the research to evaluate the prediction effect of other models. d. In addition to the prediction models mentioned above, intelligent learning model, such as artificial neural network (Abedinia et al, 2020;Dorado-Moreno et al, 2017;Wan et al, 2013;Wang et al, 2020), support vector machine (Li et al, 2019), least squares support vector machine (Ding et al, 2021;Gan and Ke, 2014), etc., are developing most rapidly in the field of wind power prediction. The intelligent learning model establishes the relationship between input variables and output variables through the learning and training of a large number of historical operation measured data.…”

Section: Introductionmentioning

confidence: 99%

Wind power prediction based on wavelet denoising and improved slime mold algorithm optimized support vector machine

Lian

2021

Wind Engineering

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The accuracy of wind power prediction directly affects the operation cost of power grid and is the result of power grid supply and demand balance. Therefore, how to improve the prediction accuracy of wind power is very important. In order to improve the prediction accuracy of wind power, a prediction model based on wavelet denoising and improved slime mold algorithm optimized support vector machine is proposed. The wavelet denoising algorithm is used to denoise the wind power data, and then the support vector machine is used as the prediction model. Because the prediction results of support vector machine are greatly affected by model parameters, an improved slime mold optimization algorithm with random inertia weight mechanism is used to determine the best penalty factor and kernel function parameters in support vector machine model. The effectiveness of the proposed prediction model is verified by using two groups actually collected wind power data. Seven prediction models are selected as the comparison model. Through the comparison between the predicted value and the actual value, the prediction error and its histogram distribution, the performance indicators, the Pearson’s correlation coefficient, the DM test, box-plot distribution, the results show that the proposed prediction model has high prediction accuracy.

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Wind Power Ramp Forecasting Based on Least-Square Support Vector Machine

Cited by 10 publications

References 8 publications

A review on the recent history of wind power ramp forecasting

A review on the recent history of wind power ramp forecasting

Improving the prediction of wind power ramps using texture extraction techniques applied to atmospheric pressure fields

Wind power prediction based on wavelet denoising and improved slime mold algorithm optimized support vector machine

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