Wind Speed Probability Density Forecast Using Optimization Based Kernel Density Estimation and Quantile Regression

In the past few decades, wind power has experienced significant progress. A number of nations have achieved a significant operational wind power capacity, while others are developing at rapid speeds in their wake. Power system management presents a challenge when integrating wind power on such a wide scale. Wind is, in fact, an unpredictable and fluctuating resource. For instance, extra reserves are typically set aside to handle this unpredictability. This reduces the advantages of employing such a RES supply and raises the total cost of the energy produced. Using forecasting technologies is one technique to lessen the uncertainty surrounding the production of wind power. These instruments are forecasting models with multiple steps forward, or look-ahead times, that offer data for multiple horizons. Most of the forecasting methods currently in use offer a single projected value for each prediction horizon known as deterministic, spot, or point forecasts. The primary disadvantage of these forecasts is that no information regarding deviations from the expected values is given. This restricts its application in scenarios involving decision-making, particularly those involving risk assessment or stochastic optimization. This research proposes a Quantile Regression (QR) and Kernel Density Estimation (KDE) based probability density forecasting system for wind speed. EMD is used to lower raw wind speed series noise. Furthermore, the de-noised wind speed signal is studied using both LQR and NQR models. Moreover, the KDE “bandwidth” and quantile regression random forest (QRRF) “numbins” are optimally tuned by means of an Adaptive Snake Optimization (ASO) process. Actual wind speed data is used for the implementation, and the suggested approach's resilience and performance are compared to those of other approaches to validate the proposed approach.