In this paper, a data-mining approach was applied to build a multivariate time series model to predict power ramp rates of a wind farm over 10 min intervals. The boosting tree algorithm was used to reduce the dimensionality of the input and to enhance prediction accuracy.
The SVM algorithm outperformed the other four algorithms in both scenarios, i. In Table 6, -1 denotes the observation sampled 10 min earlier, 2 denotes the observation sampled 20 min earlier, and -3, -4, and -5 denote the observations sampled 30 min, 40 min, and 50 min in the past, respectively.
The models were built using historical data collected by the supervisory control and data acquisition SCADA system installed at a wind farm. Table 3 shows the importance index of 30 predictors computed by the boosting tree algorithm based on data set 2 of Table 2.
The SVM is a supervised learning algorithm used in classi. Review conducted by Spyros Voutsinas. In addition, power ramps decrease the lifetime of turbine and increase operation and maintenance expenses.
The univariate time series prediction model 15,16 is expressed as follows: The data used in this research originated at a wind farm of turbines.
Our second approach is dedicated to the very short-term up to a few hours ahead and use a propagation modelling of ramps to forecast ramps from the most up to date spatio-temporal information.
The mean, Std, max, min, and power are the. To test the accuracy of these algorithms, models trained from data set 2 of Table 2 were tested on data set 3 from Table 2. Five different data-mining algorithms were tested using data collected at a wind farm. Table 5 shows the prediction accuracy of the models generated by the.
For example, the boosting tree algorithm 17,18 and the wrapper approach 19,20utilizing the genetic or the. One possible mitigation strategy is to incorporate weather forecasting and additional off-site observation data, all at additional computational cost.
Figure 1 b shows the power ramp rate corresponding to the power presented in Fig. The SVM is a supervised learning algorithm used in classi. The mean, the standard deviation, and the maximum error all increase as the prediction horizon lengthens.
One disadvantage of the proposed approach is that the multivariate time series model used different parameters, and therefore updating the model with most current data is important.
The SVM algorithm outperformed the other four algorithms studied in this paper.
Based on the PRR constraints for the reserve activation and generation schedule, the capability of a thermal generation system for balancing a wind power was investigated. Though the power prediction research has a long tradition in the wind industry, the interest in prediction of power ramps is emerging.
In Table 6, -1 denotes the observation sampled 10 min earlier, 2 denotes the observation sampled 20 min earlier, and -3, -4, and -5 denote the observations sampled 30 min, 40 min, and 50 min in the past, respectively. Figure 5 a illustrates the absolute error of the.
Table 1 lists all the parameters used in this paper. Ignoring the power consumed by the wind farm, the power produced is always positive Fig.
There is no industry standard for PRR prediction. For the short-term up to 2 or 3 days aheadwe estimate the uncertainty in the timing of ramps with time-oriented prediction intervals. The support vector machine regression algorithm performed best out of the.
The support vector machine regression algorithm performed best out of the. It appears that for longer horizon predictions, weather forecasting data may be useful.
The boosting tree algorithm selects different parameters over different periods of the PRR prediction, i. Acknowledgment The research reported in the paper has been partially supported by funding from the Iowa Energy Center Grant No.
These algorithms include the multilayer perceptron algorithm MLP 23,24the support vector machine SVM regression 25,26the random forest 27,28the classi. It constructs a linear discriminant function that separates instances as widely as possible.
Time series prediction 15 focuses on determining future events based on known observations, measured typically at successive time intervals often uniform. Data mining is a promising approach for modeling wind farm performance.
Due to the high-dimensionality of the input vector of predictors of the multivariate time series model, the number of inputs was reduced.
For example, the boosting tree algorithm 17,18 and the wrapper approach 19,20utilizing the genetic or the?.
power output time series of a wind farm. For wind ramps forecasting, there are studies which showed improvements of predictions by using statistical. In Ref., the authors define a ramp event to be a power output change higher than 50 % of the wind farm nominal power, occurring over a period of 4 h or less.
They define two metrics: forecast accuracy and ramp capture—these metrics correspond to precision and recall defined in Sect. 5 below. It provided predictions of the power ramp rate for a time horizon of 10–60 min. The boosting tree algorithm selects parameters for enhancement of the prediction accuracy of the power ramp rate.
The data used in this research originated at a wind farm of turbines. The boosting tree algorithm selects parameters for enhancement of the prediction accuracy of the power ramp rate.
The data used in this research originated at a wind farm of turbines. The test results of multivariate time series models were presented in this paper.Prediction of wind farm power ramp