Statistics of wind resource assessment using measure-correlate-predict methodology
Başlık çevirisi mevcut değil.
- Tez No: 401991
- Danışmanlar: DR. WOLF-GERRIT FRUH, DR. PETER J. M. CLIVE
- Tez Türü: Yüksek Lisans
- Konular: Enerji, Energy
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2011
- Dil: İngilizce
- Üniversite: Heriot-Watt University
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 81
Özet
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Özet (Çeviri)
Six data sets with widely varying characteristics wind speed and direction data are analyzed within the developed Measure-Correlate-Predict (MCP) methodology and so as to investigate the predictive capability of the method, comparisons are made by performing the Linear Regression Method.“Sector Pairing”procedure, which is another method that is developed for the purpose of this project, applied to each datasets in each geographical group and in order to deal with meaningful intrinsic direction sectors 15 month of short-term concurrent data are used to perform the procedure. Measure-Correlate-Predict method allows establishing a relationship between wind speeds measured at target and reference site during the short-term wind speed measurement campaign. This relationship then can be used to predict the long-term wind regime at a potential wind farm development site using the long-term data from a nearby meteorological station. Analysis had shown that increase in the length of data used when predicting the long-term wind speed regime at a site, regardless the direction, tends to increase the predictive capability of the developed MCP methodology and it has been investigated that the correlation coefficient“r”between the reference and the target sites över the short-term tends to increase as the distance between and elevation differences between the sites decreases, however this has been uncertain for some group predictions. In the analysis for each individual group predictions are made by performing the developed method, this method named as“Modifıed Weibull Scaling Method”and had showed better agreement in predicting the long-term wind speeds at the target sites compared to that of the Linear Regression method, where the linear regression has been used to make comparisons to see that how well the predictions are. Linear regression method has the potential to generate robust predictions of wind speed at a target site. Of the evaluated measure-correlate-predict methodologies, the“Modifıed Weibull Scaling Method”is recommended for general usage regardless of the correlation coefficient value or the availability of historical data from a nearby station. However, on the success of the Modifıed Weibull Scaling Method more analysis are necessary to disaggregate the influence of correlation coefficient, length of historical data över the long-term and the length of concurrent data över the short-term. The correlation coefficient is highest between sites with uniform terrain such as coastal locations between sites with complex topography. In addition to this, predictions performed by performing the“Modifıed Weibull Scaling Method”showed better agreement of the predictions with the measured wind speeds for the sites located in coastal areas and experience from Atlantic wind speed patterns. Though both MCP methods produced highly uncertain results for sites with lower correlation coeffıcients. Though for sites with higher correlation, special consideration should be paid to the uncertainty of estimated metrics introduced by the Modifıed Weibull Scaling Method and an increase in sector pairs such as taking arbitrary 9 pairs of 40-degree sectors rather than 3 pairs would enhance the predictive capability of the Modifıed Weibull Scaling Method. However in case of an increase in sector pairing, the use of longer period of short-term concurrent data will be necessary so as to deal with meaningful amount of data in each paired dataset, meaning that there would not be enough data to represent a better prediction if the use of longer concurrent data is disregarded. The Linear Regression Method is not recommended due to high prediction error observations from the analysis.
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