Application of Radial Basis Function Networks for Wind Power Forecasting

Sideratos, G.; Hatziargyriou, Nikos D.

doi:10.1007/11840930_76

Cited by 6 publications

(9 citation statements)

References 3 publications

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Section: Methodsmentioning

confidence: 99%

“…There is also application of RBFNN to predict wind speeds for use in weather models, as reported in (Chunlin et al, 2017; Sideratos and Hatziargyriou, 2006; Silva et al, 2006). The centers of the hidden layers in the RBFNN are determined by K-means clustering with addition of Recursive Least Squares in Silva et al (2006) to obtain relatively acceptable errors. For sample rates that are not fixed (unlike what is presented in this paper), K-means clustering is preferable at finding the proper centers for the neural network (Sideratos and Hatziargyriou, 2006).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A radial basis function neural network approach to filtering stochastic wind speed data

Parmar,

Pieper

2023

Wind Engineering

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Various types of control methods are utilized in wind turbines to obtain the optimal amount of power from wind. The turbine dynamics are required in said methods, and the wind speed is a critical component of the analysis. However, the stochastic nature of wind means that wind speed sensor signals are noisy. This paper proposes the utilization of a radial basis function neural network (RBFNN) based filter to process the signal, by training the network with a simulated wind signal. The network is differentiated from a traditional filter in that the number of neurons and the “learning rate” of the network dictate the properties of the filtered signal. The information flow in the network consists of the signal to be processed as the input, the which is then used as an argument in a radial basis function (which determines the “distance” of each value in the input from a particular preset point), and then it multiplied by a weight. The learning rate is obtained from a novel equation that is proposed in the paper. The results showed that the proposed scheme has versatility in terms of noise removal and signal smoothing, and if required, can viably match performance with a Butterworth filter. Furthermore, live training and adaptability also serve as advantages over a classic filter. Three “modes” of processing the signal are determined based on choosing certain ranges of values for parameters which comprise the RBFNN (number of neurons used and learning rate), and the control designer can choose which one to implement based on performance requirements.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A radial basis function neural network approach to filtering stochastic wind speed data

Parmar,

Pieper

2023

Wind Engineering

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“…More solid reviews and analysis related to the wind power short-term forecasting/wind energy trading could be found in [12][13][14][15].…”

Section: Evaluation Of Wind Power Forecastingmentioning

confidence: 99%

Estimation of Wind Power Production Through Short-Term Forecast

Agabus¹,

Tammoja²

2009

Oil Shale

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Characteristics of wind power are different and therefore its integration leads to some important challenges concerning the electricity system. Due to weather dependence, the availability of the energy generated from wind power differs fundamentally from that generated conventionally from fossil fuels. In an electricity system with an important share of wind power, new methods for balancing supply and demand are needed. Wind power forecasting plays a key role in tackling this challenge. Good wind power predictions increase the value of the wind power making it more competitive.

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“…Wind energy is one of the economic renewable sources and a valuable supplement to conventional energy sources. One of the important problems in wide uses of wind power is difficulties of accurate wind power forecast [4,5,6]. Fluctuations in wind power production, also makes it difficult for owners of wind power plants to compete in electricity markets.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we have considered wind speed data of the given site, of a database with 5 years of historical data [5], [6]. a model is developed to estimate monthly mean wind speed for a given period using PROLOG [3] ( AI Tool) Weibull probability distribution of wind speed and forecast the wind speed using artificial neural network.…”

Section: Introductionmentioning

confidence: 99%

A Statistical Analysis and Datamining Approach for Wind Speed Predication

Mahesh

Vijayakumar

2014

IJCT

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The wind power industry has seen an unprecedented growth in last few years. The surge in orders for wind turbines has resulted in a producers market. This market imbalance, the relative immaturity of the wind industry, and rapid developments in data processing technology have created an opportunity to improve the performance of wind farms and change misconceptions surrounding their operations. This research offers a new paradigm for the wind power industry, data-driven modeling. Each wind Mast generates extensive data for many parameters, registered as frequently as every minute. As the predictive performance approach is novel to wind industry, it is essential to establish a viable research road map. This paper proposes a data-mining-based methodology for long term wind forecasting (ANN), which is suitable to deal with large real databases. The paper includes a case study based on a real database of five years of wind speed data for a site and discusses results of wind power density was determined by using the Weibull and Rayleigh probability density functions. Wind speed predicted using wind speed data with Datamining methodology using intelligent technology as Artificial Neural Networks (ANN) and a PROLOG program designed to calculate the monthly mean wind speed.

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Application of Radial Basis Function Networks for Wind Power Forecasting

Cited by 6 publications

References 3 publications

A radial basis function neural network approach to filtering stochastic wind speed data

A radial basis function neural network approach to filtering stochastic wind speed data

Estimation of Wind Power Production Through Short-Term Forecast

A Statistical Analysis and Datamining Approach for Wind Speed Predication

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