Performance assessment of five MCP models proposed for the estimation of long-term wind turbine power outputs at a target site using three machine learning techniques

Díaz, Santiago; Carta, José A.; Matías, J. M.

doi:10.1016/j.apenergy.2017.11.007

Cited by 44 publications

(14 citation statements)

References 35 publications

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“…For the wind speeds tested [4 m/s to 16 m/s], the Reynolds number goes from 1.015 × 10 5 to 4.063 × 10 5 . Although turbulence and density variations influence in-field measurements [3,22], since this work presents a calibration procedure, we only considered wind tunnel measures at low turbulence levels.…”

Section: Methodsmentioning

confidence: 99%

Alternative Calibration of Cup Anemometers: A Way to Reduce the Uncertainty of Wind Power Density Estimation

Guerrero-Villar¹,

Dorado-Vicente²,

Medina-Sánchez³

et al. 2019

Sensors

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This study presents a procedure to reduce the uncertainty of wind power density estimations, which is useful to improve the energy production predictions of wind farms. Power density is usually determined from the wind speed measured by a cup anemometer and the air density value (conventional procedure). An alternative procedure based on wind speed and dynamic pressure estimations provided by a cup anemometer is proposed. The dynamic pressure is obtained by means of a calibration curve that relates the anemometer rotation frequency and the dynamic pressure measured by a Pitot tube. The quadratic regression, used to define the calibration curve, and its uncertainty are both detailed. A comparison between the alternative procedure and the conventional one points out the advantage of the proposed alternative since results show a high reduction of the indirect measurement uncertainty of wind power density.

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Section: Methodsmentioning

confidence: 99%

Alternative Calibration of Cup Anemometers: A Way to Reduce the Uncertainty of Wind Power Density Estimation

Guerrero-Villar¹,

Dorado-Vicente²,

Medina-Sánchez³

et al. 2019

Sensors

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“…Hence, comparing distinct methods is necessary before electing a single one to proceed with the WRA. Several studies worked on this matter of comparing different MCP methods according to metrics such as mean absolute error, mean absolute relative error or mean squared error [51][52][53][54][55][56][57][58][59][60][61]. Nonetheless, there is no consensus about one single best MCP model, reinforcing the necessity of testing several of them for every set of wind data.…”

Section: Mcp Methodsmentioning

confidence: 99%

The Influence of the Wind Measurement Campaign Duration on a Measure-Correlate-Predict (MCP)-Based Wind Resource Assessment

2019

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Driven by the energy auctions system, wind power in Brazil is undergoing a phase of expansion within its electric energy mix. Due to wind’s stochastic nature and variability, the wind measurement campaign duration of a wind farm project is required to last for a minimum of 36 months in order for it to partake in energy auctions. In this respect, the influence of such duration on a measure-correlate-predict (MCP) based wind resource assessment was studied to assess the accuracy of generation forecasts. For this purpose, three databases containing time series of wind speed belonging to a site were considered. Campaigns with durations varying from 2 to 6 years were simulated to evaluate the behavior of the uncertainty in the long-term wind resource and to analyze how it impacts a wind farm power output estimation. As the wind measurement campaign length is increased, the uncertainty in the long-term wind resource diminished, thereby reducing the overall uncertainty that pervades the wind power harnessing. Larger monitoring campaigns implied larger quantities of data, thus enabling a better assessment of wind speed variability within that target location. Consequently, the energy production estimation decreased, allowing an improvement in the accuracy of the energy generation prediction by not overestimating it, which could benefit the reliability of the Brazilian electric system.

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“…where: MARE is the mean absolute relative error for the forecast horizon; T is the number of data in the test stage (see Figure 1); r = T-m-n; MARE j is the mean absolute relative error for the forecasting period j; P j and  j P are the actual and estimated wind farm power output in the forecasting period j, respectively; R is the mean value of Pearson's coefficient of correlation between the estimated and actual wind farm power output for the forecast horizon; and R j is the mean Pearson correlation coefficient between the estimated and actual wind farm power output values for the forecasting period j. The combined use of the two previous metrics is considered sufficient for the evaluation of the performance of the models and they have been widely used [38][39][40][41]. Alternatively, for the evaluation of future models, combinations of other metrics could be used [42].…”

Section: Metrics Used To Compare the Different Modelsmentioning

confidence: 99%

Optimization of the ANNs Models Performance in the Short-Term Forecasting of the Wind Power of Wind Farms

Velázquez-Medina¹,

Portero-Ajenjo²

2021

Theory of Complexity - Definitions, Models, and Applications

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Due to the low dispatchability of wind power, the massive integration of this energy source in electrical systems requires short-term and very short-term wind farm power output forecasting models to be as efficient and stable as possible. A study is conducted in the present paper of potential improvements to the performance of artificial neural network (ANN) models in terms of efficiency and stability. Generally, current ANN models have been developed by considering exclusively the meteorological information of the wind farm reference station, in addition to selecting a fixed number of time periods prior to the forecasting. In this respect, new ANN models are proposed in this paper, which are developed by: varying the number of prior 1-h periods (periods prior to the prediction hour) chosen for the input layer parameters; and/or incorporating in the input layers data from a second weather station in addition to the wind farm reference station. It has been found that the model performance is always improved when data from a second weather station are incorporated. The mean absolute relative error (MARE) of the new models is reduced by up to 7.5%. Furthermore, the longer the forecast horizon, the greater the degree of improvement.

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Performance assessment of five MCP models proposed for the estimation of long-term wind turbine power outputs at a target site using three machine learning techniques

Cited by 44 publications

References 35 publications

Alternative Calibration of Cup Anemometers: A Way to Reduce the Uncertainty of Wind Power Density Estimation

Alternative Calibration of Cup Anemometers: A Way to Reduce the Uncertainty of Wind Power Density Estimation

The Influence of the Wind Measurement Campaign Duration on a Measure-Correlate-Predict (MCP)-Based Wind Resource Assessment

Optimization of the ANNs Models Performance in the Short-Term Forecasting of the Wind Power of Wind Farms

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