A New MCP Method of Wind Speed Temporal Interpolation and Extrapolation Considering Wind Speed Mixed Uncertainty

Liu, Xiao; Xu, Lihua; Zhou, Jin

doi:10.3390/en10081231

Cited by 6 publications

(1 citation statement)

References 20 publications

(42 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

Section: Mcp Methodsmentioning

confidence: 99%

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

2019

View full text Add to dashboard Cite

show abstract

A Monte Carlo simulation method for probabilistic evaluation of annual energy production of wind farm considering wind flow model and wake effect

Liu

Peng

Huang

et al. 2023

Energy Conversion and Management

View full text Add to dashboard Cite

A measure–correlate–predict model based on neural networks and frozen flow hypothesis for wind resource assessment

2022

View full text Add to dashboard Cite

In this paper, a measure–correlate–predict (MCP) model based on neural networks (NN) and frozen flow hypothesis, which is abbreviated as the MCPNN-frozen model, is proposed for wind resource assessment and tested using turbulent channel flows with three different surface roughness lengths, i.e., [Formula: see text], 0.01, and 0.1 m. The predictions from the MCPNN-frozen model are compared with the real data for different separations ( s) between the reference point and the target point. The results show that the correlation coefficients C.C. between the model predictions and real data are roughly higher than 0.5 for small separations [Formula: see text] (where δ is the boundary layer thickness), and the coefficients of determination ( R2) are approximately higher than 0.3 when [Formula: see text]. The generalization capacity of the MCPNN-frozen model is tested for different roughness lengths and different velocity components. Further analyses show that, even though C.C. and R2 decrease when increasing s, the large-scale variations of velocity fluctuations are well captured by the MCPNN-frozen model especially for the one trained using the data filtered in time. Furthermore, it is found that the model trained using the filtered data without a spanwise offset can well predict the large-scale variations at the target point when the spanwise offsets between the target point and the reference point are small (e.g., [Formula: see text] and [Formula: see text]). The proposed model leverages the power of neural networks and physical understanding. Further development of the model for complex scenarios will be carried out in the future work.

show abstract

A New MCP Method of Wind Speed Temporal Interpolation and Extrapolation Considering Wind Speed Mixed Uncertainty

Cited by 6 publications

References 20 publications

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

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

A Monte Carlo simulation method for probabilistic evaluation of annual energy production of wind farm considering wind flow model and wake effect

A measure–correlate–predict model based on neural networks and frozen flow hypothesis for wind resource assessment

Contact Info

Product

Resources

About