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Paper:Weekes, SM and Tomlin, AS (2014) Highlights: Long-term wind energy resource predicted using just three months onsite wind speed measurements.  Robust error statistics obtained using 120 test periods at 22 sites over 11 years.  Link between measurement season and prediction accuracy investigated.  Data-driven prediction approaches compared to semi-empirical boundary layer model. AbstractThe feasibility of predicting the long-term wind resource at 22 UK sites using a measurecorrelate-predict (MCP) approach based on just three months onsite wind speed measurements has been investigated. Three regression based techniques were compared in terms of their ability to predict the wind resource at a target site based on measurements at a nearby reference site. The accuracy of the predicted parameters of mean wind speed, mean wind power density, standard deviation of wind speeds and the Weibull shape factor was assessed, and their associated error distributions were investigated, using long-term measurements recorded over a period of 10 years. For each site, 120 wind resource predictions covering the entire data period were obtained using a sliding window approach to account for inter-annual and seasonal variations. Both the magnitude and sign of the prediction errors were found to be strongly dependent on the season used for onsite measurements. Averaged across 22 sites and all seasons, the best performing MCP approach resulted in mean absolute and percentage errors in the mean wind speed of 0.21 ms -1 and 4.8% respectively, and in the mean wind power density of 11 wm -2 and 14%. The average errors were reduced to 3.6% in the mean wind speed and 10% in the mean wind power density when using the optimum season for onsite wind measurements. These values were shown to be a large improvement on the predictions obtained using an established semi-empirical model based on boundary layer scaling. The results indicate that the MCP approaches applied to very short onsite measurement periods have the potential to be a valuable addition to the wind resource assessment toolkit for smallscale wind developers.

Highlights Wind resource predicted at 37 sites using operational forecast data and MCP  Forecast reference data highly competitive with nearby meteorological observations  Systematic improvement when using forecast data at coastal sites AbstractOutput from a state-of-the-art, 4 km resolution, operational forecast model (UK4) was investigated as a source of long-term historical reference data for wind resource assessment. The data were used to implement measure-correlate-predict (MCP) approaches at 37 sites throughout the United Kingdom (UK). The monthly and hourly linear correlation between the UK4-predicted and observed wind speeds indicates that UK4 is capable of representing the wind climate better than the nearby meteorological stations considered. Linear MCP algorithms were implemented at the same sites using reference data from UK4 and nearby meteorological stations to predict the long-term (10-year) wind resource. To obtain robust error statistics, MCP algorithms were applied using onsite measurement periods of 1-12 months initiated at 120 different starting months throughout an 11 year data record. Using linear regression MCP over 12 months, the average percentage errors in the long-term predicted mean wind speed and power density were 3.0% and 7.6% respectively, using UK4, and 2.8% and 7.9% respectively, using nearby meteorological stations. The results indicate that UK4 is highly competitive with nearby meteorological observations as an MCP reference data source. UK4 was also shown to systematically improve MCP predictions at coastal sites due to better representation of local diurnal effects.2

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.

A two-stage approach to low-cost wind resource assessment for small-scale wind installations has been investigated in terms of its ability to screen for non-viable sites and to provide accurate wind power predictions at promising locations. The approach was implemented as a case study at ten UK locations where domestic-scale turbines were previously installed. In stage one, sites were pre-screened using a boundary layer scaling model to predict the mean wind power density, including estimated uncertainties, and these predictions were compared to a minimum viability criterion. Using this procedure, five of the seven non-viable sites were correctly identified without direct onsite wind measurements and none of the viable sites were excluded. In stage two, more detailed analysis was carried out using three months onsite wind measurements combined with measure-correlate-predict (MCP) approaches. Using this process, the remaining two non-viable sites were identified and the available wind power density at the three viable sites was accurately predicted. The effect of seasonal variability on the MCP predicted wind resource was considered and the implications for financial projections were highlighted. The study provides a framework for low-cost wind resource assessment in cases where long-term onsite measurements may be too costly or impractical.2