Short-term prediction method of wind speed series based on fractal interpolation

Xiu, Chunbo; Wang, Tiantian; Tian, Meng; Li, Yanqing; Cheng, Yi

doi:10.1016/j.chaos.2014.07.013

Cited by 42 publications

(19 citation statements)

References 21 publications

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“…As shown from the analysis, the wind speeds of adjacent turbines in the same wind direction are remarkably correlated. Research results suggest that wind speed correlation based on the transmission of wind power between wind turbines can effectively enhance the accuracy of wind parameter prediction [32][33][34]. At present, the error of wind parameter prediction is usually between 25% and 40% [32].…”

Section: Mcspv Recombination-sensing Methods Concept and Its Feasibilitymentioning

confidence: 99%

“…There are two technical approaches for the typical research methods of wind parameter predictive perception: statistics [23][24][25][26][27] and the intelligent algorithm [28][29][30][31][32][33][34]. For the statistical approach, a statistical analysis of the historical data is made with all types of regression techniques and a mapping relation is established between the actual values and the predicted ones.…”

Section: Predictive Perception Methods Of Wind Parametermentioning

confidence: 99%

“…There are generally artificial neural network (ANN) [28][29][30][31], support vector machine (SVM) [31][32][33] and Kalman filtering method [34] approaches. In recent years, with the development of big data and data-mining technology, the use of spatial correlation between wind farms in predicting wind speed and wind direction has opened up a new route for wind parameter predictive perception [34][35][36][37][38]. The spatial correlation method selects multiple sets of historical data from wind farms at different locations [36], with all factors taken into consideration, including geographic space (wind turbine distribution and topological structure, etc.…”

Section: Predictive Perception Methods Of Wind Parametermentioning

confidence: 99%

“…), and physical effects (wake effect, wind farm terrain, roughness, etc.). It determines the wind speed correlation between wind turbines for different time delays, establishes a correlation prediction model based on spatial correlation and predicts the wind speed [34,35].…”

Section: Predictive Perception Methods Of Wind Parametermentioning

confidence: 99%

See 3 more Smart Citations

Overview of Wind Parameters Sensing Methods and Framework of a Novel MCSPV Recombination Sensing Method for Wind Turbines

Shen

Zhou

et al. 2018

Energies

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The paper presents an overview of the traditional methods to obtain wind parameters such as wind speed, wind direction and air density. After analyzing wind turbines' arrangements and communication characteristics and the correlation of operation data between wind turbines, the paper proposes a novel recombination-sensing method route of "measuring-correlating-sharingpredicting-verifying" (MCSPV) and explores its feasibility. The analysis undertaken in the paper shows that the wind speed and wind direction instrument fixed on the wind turbine nacelle is simple and economical. However, it performs in-process measurement, which restricts the control optimization of wind turbines. The light detection and ranging (LIDAR) technology which is accurate and fast, ensures an early and super short-time sensing of wind speed and wind direction but it is costly. The wind parameter predictive perception method can predict wind speed and wind power at multiple time scales statistically, but it has limited significance for the control of the action of wind turbines. None of the traditional wind parameter-sensing methods have ever succeeded in air density sensing. The MCSPV recombination sensing method is feasible, both theoretically and in engineering, for realizing the efficient and accurate sensing and obtaining of such parameters as wind speed, wind direction and air density aimed at the control of wind turbines.

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Section: Mcspv Recombination-sensing Methods Concept and Its Feasibilitymentioning

confidence: 99%

Section: Predictive Perception Methods Of Wind Parametermentioning

confidence: 99%

Section: Predictive Perception Methods Of Wind Parametermentioning

confidence: 99%

Section: Predictive Perception Methods Of Wind Parametermentioning

confidence: 99%

See 2 more Smart Citations

Overview of Wind Parameters Sensing Methods and Framework of a Novel MCSPV Recombination Sensing Method for Wind Turbines

Shen

Zhou

et al. 2018

Energies

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“…He showed that there exists a function interpolating a given data set such that the graph of this function is the attractor of an IFS. In recent decades it has been widely used in various fields such as approximation theory, image compression, modeling of signals and in many other scientific areas ( [4], [7], [9], [10], [12]).…”

Section: Introductionmentioning

confidence: 99%

Graph-directed fractal interpolation functions

Deniz¹,

Özdemir²

2017

Turk J Math

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It is known that there exists a function interpolating a given data set such that the graph of the function is the attractor of an iterated function system, which is called a fractal interpolation function. We generalize the notion of the fractal interpolation function to the graph-directed case and prove that for a finite number of data sets there exist interpolation functions each of which interpolates a corresponding data set in R 2 such that the graphs of the interpolation functions are attractors of a graph-directed iterated function system.

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Data-Driven Construction of Local Models for Short-Term Wind Speed Prediction

Salas

2015

Advances in Artificial Intelligence and Its Applications

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Short-term prediction method of wind speed series based on fractal interpolation

Cited by 42 publications

References 21 publications

Overview of Wind Parameters Sensing Methods and Framework of a Novel MCSPV Recombination Sensing Method for Wind Turbines

Overview of Wind Parameters Sensing Methods and Framework of a Novel MCSPV Recombination Sensing Method for Wind Turbines

Graph-directed fractal interpolation functions

Data-Driven Construction of Local Models for Short-Term Wind Speed Prediction

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