AI Driven Wind Energy Forecasting: Case Study for Sustainable Energy in Pakistan

Abstract

Wind power is having a significant effect on the global energy landscape, providing sustainable choices that address environmental, economic, and social concerns. Forecasting wind power is of the utmost importance for the successful incorporation of wind power into the network of electrical power distribution systems. Accurate forecasts empower electric grid operators to foresee variations in wind energy generation. In order to accurately anticipate wind power, meteorological data, weather occurrences, wind turbine performance, and grid limits are all taken into consideration. In this context, we have proposed a novel approach called DBSCAN-RFE-XGBoost a two-stage process. The initial stage, we have proposed a clustering algorithm that uses density-based spatial clustering (DBSCAN) to automate the EPS value that is required for DBSCAN clustering by using K-dist plot and Knee Point Detection Algorithm. Additionally, we have removed outliers from the dataset. In the second stage, we applied two fold scheme, first we enginered temporal features and then applied recursive feature elimination to the preprocessed dataset which identifies best suited features to feed into the XGBoost algorithm to predict wind power. To determine effectiveness of proposed model, we have utilized SCADA dataset obtained from a wind farm in Pakistan. In comparison to existing benchmarking approaches, our suggested model achieves a performance that is 38.89% higher (RMSE), and its effectiveness is demonstrated by an R2 value of roughly 5.10%.