Reconstructing long-term wind speed data based on measure correlate predict method for micro-grid planning

One of the most significant uncertain parameters in microgrid planning is wind speed. Wind speed has a complex dynamic behavior and developing models with high accuracy for this parameter can help decrease microgrid costs and can help improve reliability. Previous methods for wind speed modeling have been mostly statistical methods (e.g., the auto-regressive integrated moving average (ARIMA), the multivariate distribution functions, Copula function). Afore-mentioned methods are suitable for modeling stationary data. However, since the wind speed data are not stationary, the measure correlate predict (MCP) method, capable of modeling the non-stationary data, is used in this paper to generate the wind speed scenarios. Based on radial basis function (RBF) artificial neural network, this work has proposed a novel hybrid computational model to improve MCP method's performance. The results indicate that the Hybrid-MCP method is more accurate than the conventional statistical methods, which are used to generate the wind speed scenarios (24 h).

Automated summary

The study utilized the MCP method for wind speed modeling and proposed a hybrid computational model to enhance the performance of the MCP method, demonstrating that it is more accurate than conventional methods.