Data efficient measure-correlate-predict approaches to wind resource assessment for small-scale wind energy

Weekes, S.M.; Tomlin, Alison S.

doi:10.1016/j.renene.2013.08.033

Cited by 29 publications

(30 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The subscripts and refer to the reference and target sites respectively. The baseline MCP approaches described above were applied previously to the meteorological monitoring sites used in this study for a fixed short-term measurement period of three months [9]. They are included here to serve as a comparison for assessing the success of the BW approaches using multiple measurement periods.…”

Section: Variance Ratio Methodsmentioning

confidence: 99%

“…Previous work [9] demonstrated that the success of the LR technique can be significantly improved by accounting for the residual errors. Hence, here is modelled using random samples from a zero-mean Gaussian distribution of the form:…”

Section: Linear Regressionmentioning

confidence: 99%

“…To obtain robust error statistics, multiple test periods were used by implementing a sliding window approach [9] across the entire 11 year data record as follows: (I) A 12 month training window was shifted in steps of one month across the entire data record using a total of 120 steps. At each step, data not covered by the window had a combined length of 10 years and was designated as the test data such …”

Section: Meteorological Measurementsmentioning

confidence: 99%

“…This is in contrast to results obtained when using artificial wind data (Figure 4Figure 5) and could be due to deviations of the observed wind data from idealised BW distributions. It should be noted that the LR approach implemented here includes a Gaussian model of the scatter term about the predicted wind speeds, which has been shown to increase the accuracy of predictions [9]. Without this term, the LR method would be considerably less competitive with the BW and BW2 approaches.…”

Section: Comparison Between the Bivariate Weibull And Baseline Measurmentioning

confidence: 99%

“…MCP applied to very short-term measurement periods may also be a viable approach [9] providing the performance of the techniques as a function of the measurement period has been investigated.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Comparison between the bivariate Weibull probability approach and linear regression for assessment of the long-term wind energy resource using MCP

Weekes

Tomlin

2014

Renewable Energy

Self Cite

View full text Add to dashboard Cite

Highlights Measure-correlate-predict approach based on bivariate Weibull probability tested at 22 sites  Deviations from ideal bivariate Weibull behaviour investigated using observed and artificial data  Error metrics calculated using 120 test periods over an 11 year data record  Performance compared to existing regression methods using variable onsite measurement periods Keywords: measure-correlate-predict, wind resource assessment, bivariate Weibull distribution Abstract A detailed investigation of a measure-correlate-predict (MCP) approach based on the bivariate Weibull (BW) probability distribution of wind speeds at pairs of correlated sites has been conducted. Since wind speeds are typically assumed to follow Weibull distributions, this approach has a stronger theoretical basis than widely used regression MCP techniques. Building on previous work that applied the technique to artificially generated wind data, we have used long-term (11 year) wind observations at 22 pairs of correlated UK sites. Additionally, 22 artificial wind data sets were generated from ideal BW distributions modelled on the observed data at the 22 site pairs. Comparison of the fitting efficiency revealed that significantly longer data periods were required to accurately extract the BW distribution parameters from the observed data, compared to artificial wind data, due to seasonal variations. The overall performance of the BW approach was compared to standard regression MCP techniques for the prediction of the 10 year wind resource using both observed and artificially generated wind data at the 22 site pairs for multiple short-term measurement periods of 1-12 months. Prediction errors were quantified by comparing the predicted and observed values of mean wind speed, mean wind power density, Weibull shape factor and standard deviation of wind speeds at each site. Using the artificial wind data, the BW approach outperformed the regression approaches for all measurement periods.When applied to the real wind speed observations however, the performance of the BW approach was comparable to the regression approaches when using a full 12 month measurement period and generally worse than the regression approaches for shorter data periods. This suggests that real wind observations at correlated sites may differ from ideal BW distributions and hence regression approaches, which require less fitting parameters, may be more appropriate, particularly when using short measurement periods.

show abstract

Section: Variance Ratio Methodsmentioning

confidence: 99%

Section: Linear Regressionmentioning

confidence: 99%

Section: Meteorological Measurementsmentioning

confidence: 99%

Section: Comparison Between the Bivariate Weibull And Baseline Measurmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Comparison between the bivariate Weibull probability approach and linear regression for assessment of the long-term wind energy resource using MCP

Weekes

Tomlin

2014

Renewable Energy

Self Cite

View full text Add to dashboard Cite

show abstract

Prediction of local wind climatology from Met Office models: Virtual Met Mast techniques

2016

View full text Add to dashboard Cite

The Met Office has developed the Virtual Met Mast ™ (VMM) tool for assessing the feasibility of potential wind farm sites. It provides site-specific climatological wind information for both onshore and offshore locations. The VMM relies on existing data from past forecasts from regional-scale numerical weather prediction (NWP) models, to which corrections are applied to account for local site complexity. The techniques include corrections to account for the enhanced roughness lengths used in NWP models to represent drag due to sub-grid orography and downscaling methods that predict local wind acceleration over small-scale terrain. The corrected NWP data are extended to cover long periods (decades) using a technique in which the data are related to alternative long-term datasets. For locations in the UK, the VMM currently relies on operational mesoscale model forecast data at 4 km horizontal resolution. Predictions have been verified against observations made at typical wind turbine hub heights at over 80 sites across the UK. In general, the predictions compare well with the observations. The techniques provide an efficient method for screening potential wind resource sites. Examples of how the VMM techniques can be used to produce local wind maps are also presented. 411 2. The long-term data at the reference location must be of a consistently high quality, with known instrument faults or changes corrected. The period must be sufficiently long to represent the local climatology. Typically, a minimum period of 20 years is required. The reference location should be close to the site of interest and sufficiently similar in nature that the wind at the two sites is strongly correlated. Clearly, if the winds experienced at the sites are very different (due to local topographic variations, for example), then the method will fail. Modelling techniques can be used to account for the effects of local site complexity, but the difficulties of modelling airflow in complex terrain mean this may not be successful. [8][9][10] An alternative approach to MCP is to generate synthetic reference climatology data. Over recent years, there has been a growing trend of the use of numerical weather prediction (NWP) data for the generation of climatological wind predictions. 4,11 Run at sufficiently high horizontal resolution, modern NWP models can, in principle at least, account for local variations in orography and the land surface and therefore be used to generate site-specific wind predictions. One advantage of using model data over reference observations is that model data can be co-located with the target site, avoiding the uncertainties due to using observations at a different site. A recent study 12 shows that using model data in place of reference site observations can work well in a MCP approach.The use of NWP data for climatological wind predictions relies on an accurate model run at high spatial resolution. The resolution requirements will generally depend on the site location. A relatively coarse horizontal grid spacing of a fe...

show abstract

Reconstructing long-term wind speed data based on measure correlate predict method for micro-grid planning

Borujeni

Dideban

Foroud

2021

J Ambient Intell Human Comput

View full text Add to dashboard Cite

Data efficient measure-correlate-predict approaches to wind resource assessment for small-scale wind energy

Cited by 29 publications

References 8 publications

Comparison between the bivariate Weibull probability approach and linear regression for assessment of the long-term wind energy resource using MCP

Comparison between the bivariate Weibull probability approach and linear regression for assessment of the long-term wind energy resource using MCP

Prediction of local wind climatology from Met Office models: Virtual Met Mast techniques

Reconstructing long-term wind speed data based on measure correlate predict method for micro-grid planning

Contact Info

Product

Resources

About