Browsing by Author "Rocha, T."
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- Evaluation of MCP Correlation Algorithms Applied to Wind Data SeriesPublication . Moreira, A.; Rocha, T.; Mendonça, J.; Pilão, R.; Pinto, P.This work aimed to develop methodologies for analysing statistical correlations among wind data series using various Measure-Correlate-Predict (MCP) methods, with the goal of selecting the most suitable method for extrapolating long-term data with minimal associated uncertainty. It was analysed the minimum time required for a wind measurement campaign when applying this methodology. Fifteen local wind measurement stations were selected. The long-term wind data reanalysis series that exhibited the strongest correlation with the measured wind data at each station was then chosen. Multiple tests were conducted with different simultaneous periods between the measured data series and the long-term series. Fifteen correlation algorithms were tested for each concurrent period. The performance of each model was evaluated using the RMSE (Root Mean Square Error) and MBE (Mean Bias Error) associated with each MCP. Analysis of the errors identified measurement periods with the lowest associated error ranging from 1 to 5 years and a single-factor ANOVA analysis was conducted. Finally, t-significance tests were performed. The study concluded that the Neural Network was the most effective MCP method. Additionally, it was determined that the minimum number of years required for a local measurement campaign should be between 2 and 3 years.
- Evaluation of MCP correlation algorithms applied to wind data seriesPublication . Moreira, A.; Rocha, T.; Mendonça, J.; Pilão, R.; Pinto, P.(Objectives) This work aimed to develop methodologies for analyzing statistical correlations among wind data series using various Measure-Correlate-Predict (MCP) methods, with the goal of selecting the most suitable method for extrapolating long-term data with minimal associated uncertainty. Furthermore, the study intends to investigate how the concurrent period used to build the correlation can affect the performance indicators of MCP methods.
- Validation of a methodology for post-construction Energy Yield Assessment of an operational wind farmPublication . Costa, M.; Rocha, T.; Mendonça, J.; Pilão, R.; Pinto, P.The uncertainty associated with the prospective Energy Yield Assessment (EYA) of a wind farm may be reduced by re estimating the energy yield after it enters normal operation. This study aims to validate a simple methodology for conducting post-construction EYA of an operational wind farm. The proposed methodology derives a linear relationship between a historical source of wind speed data and the observed wind farm production on a monthly basis. In a first stage, the impact of different data sources on the accuracy of the Long-Term energy yield estimate was assessed. Results suggest that the determination coefficient R 2 is a reliable indicator for selecting the most adequate source of historical wind speed data to be used in the Long-Term energy yield estimate. In a second stage, the model was validated from a statistical point of view by testing the premises of the linear regression model, namely the significance of the linear correlation (ANOVA test), and normally-distributed (Shapiro-Wilk test), non-self-correlated (Durbin-Watson), homoscedastic (Breusch-Pagan test) residuals. Results show these premises are verified for most test cases, indicating that the model is statistically robust that the model is statistically robust for most test cases.