Search

Abstract: So-called Measure-Correlate-Predict (MCP) methods have been extensively proposed in renewable energy related literature to estimate the wind resources that represent the long-term conditions at a target site where a short-term wind data measurement campaign has been held. The main differences between the various MCP methods lie fundamentally in the type of relationship established between the wind data (speed and direction) recorded at the target site and the wind data recorded simultaneously at one or various nearby weather stations which serve as reference stations and for which long-term data series are also available. This paper reviews a wide range of MCP methods that have been proposed in the context of wind energy analysis, a number of which have been implemented in wind energy industry software applications. This review includes the initial methods first proposed in the 1940s which generally attempted only to estimate the long-term mean annual wind speed from a single reference station, and extends up to the most recent methods proposed in the present century based on automatic learning techniques which use several reference stations. In addition to offering a description of the linear, non-linear and probabilistic transfer functions used by the different algorithms, the hypotheses on which these functions are based and the data format with which the various methods work (time series or frequency distributions), this review will also cover limitations in the use of MCP methods, the uncertainty associated with them and the different reference data sources that have been studied. In this sense, the extensive collection of MCP methods which is brought together and reviewed in this paper, ranging from the simplest and easiest-to-use models to the most complicated computational ones which require specific user experience, comprises an extremely useful catalogue when it comes to choosing the best predictor method. [Copyright &y& Elsevier]

Abstract: In the work presented in this paper Artificial Neural Networks (ANNs) were used to estimate the long-term wind speeds at a candidate site. The specific costs of the wind energy were subsequently determined on the basis of the knowledge of these wind speeds. The results were compared with those obtained with a linear Measure–Correlate–Predict (MCP) method. The mean hourly wind speeds and directions recorded over a 10year period at six weather stations located on different islands in the Canary Archipelago (Spain) were used as a case study. The power-wind speed curves for five wind turbines of different rated power were also used. The mean absolute percentage error (MAPE), Pearson’s correlation coefficient and the Index of Agreement (IoA) between measured and estimated data were used to evaluate the errors made with the different metrics analysed. Amongst the conclusions that can be drawn from the study it can be stated that, in all the cases analysed, the MAPE of the specific cost of the energy obtained using ANNs was lower than that obtained with the linear MCP method that was employed. In some cases reductions in the error of up to 26.5% were achieved with the ANNs with respect to the method used for purposes of comparison. However, when the correlation coefficients between the short-term data series of the candidate and reference sites were relatively low, the MAPEs generated by all the analysed metrics were higher when the estimation models were used than when the data for the short-term period at the candidate site was taken as being representative of the long-term wind performance at that site. [Copyright &y& Elsevier]

Abstract: This paper proposes the use of a new Measure–Correlate–Predict (MCP) method to estimate the long-term wind speed characteristics at a potential wind energy conversion site. The proposed method uses the probability density function of the wind speed at a candidate site conditioned to the wind speed at a reference site. Contingency-type bivariate distributions with specified marginal distributions are used for this purpose. The proposed model was applied in this paper to wind speeds recorded at six weather stations located in the Canary Islands (Spain). The conclusion reached is that the method presented in this paper, in the majority of cases, provides better results than those obtained with other MCP methods used for purposes of comparison. The metrics employed in the analysis were the coefficient of determination (R 2) and the root relative squared error (RRSE). The characteristics that were analysed were the capacity of the model to estimate the long-term wind speed probability distribution function, the long-term wind power density probability distribution function and the long-term wind turbine power output probability distribution function at the candidate site. [Copyright &y& Elsevier]

Abstract: In the scientific literature concerning renewable energies there have been proposals for the use of Artificial Neural Networks (ANNs) as a tool to estimate the mean wind speed at a target station for which only incomplete data series are available. In general terms, the wind speeds recorded at neighbouring reference stations are used as signals of the input layer of multilayer perceptron (MLP) architectures. An analysis is undertaken in this paper of the extent to which estimation of the wind speed, the wind power density and the power output of an installed wind turbine at a target site is affected by the number of reference stations, the degree of correlation between the wind speeds of the reference stations and the target station, the wind direction and the manner in which the direction signal is introduced into the input layer. For the purposes of this study mean hourly wind speeds and directions were used which had been recorded at twenty two weather stations located in the Canary Archipelago (Spain) in 2002 and 2006. The power-wind speed characteristic curves of five wind turbines of different rated power were also used. The MLP architectures were trained using the backpropagation algorithm. Amongst other conclusions reached with the tests performed with the set of data not used in the training nor in the validation of the models is that the estimation errors tend to decrease as the number of reference stations used increases, independently of the existing correlation coefficient values between the reference stations and the candidate station. It was also observed that if input signals of angular wind direction and wind speed are used then the number of reference stations required to achieve a certain decrease in the error is lower than the number required if only wind speed input signals are used. By using multiple reference stations with input signals of angular wind direction and speed, error reductions have been achieved for some stations in the estimation of the wind power density and the power output of wind turbines, with respect to the case of a single reference station and identical input signals, of 75% and 62%, respectively. [Copyright &y& Elsevier]