Publications

Description

Authors: Juan Manuel González Sopeña, Vikram Pakrashi, Bidisha Ghosh

Abstract: Lack of benchmarks for (opens in a new window)wind power forecasting models undermine their potential and consequently their implementation for industry applications. Despite the extensive existing literature in the field, a unified framework has not been created yet. Therefore, we propose a benchmark set-up where statistical wind power forecasting models can be tested under standardized criteria with respect to data, time resolution, and prediction horizon, while evaluating them under varied representative operational conditions of (opens in a new window)wind farms. The utility of this framework is shown with an example case applied to mode decomposition models, which have shown a higher performance compared to other statistical models in recent times. Data collected from two Irish (opens in a new window)wind farms are used to calculate the accuracy of statistical wind power forecasting models. Their robustness is also examined by providing an assessment of such models under several scenarios of (opens in a new window)power generation. In the example case of this benchmark, results indicate that the use of (opens in a new window)variational mode decomposition as decomposition algorithm together with advanced (opens in a new window)recurrent networks provide the best performance among the evaluated forecasting models.