wind power forecasting with a hybrid deep learning approach including lstm and attention mechanism

The intermittent nature of wind energy generation poses substantial challenges to the stability of electrical grids and the efficiency of energy management systems. accurate and reliable wind power forecasting is therefore critical for a multitude of reasons: to optimize the operational efficiency of grids, to effectively balance energy supply and demand, to enhance the planning and execution of energy storage strategies, to minimize the reliance on backup power sources, and ultimately, to reduce operational costs within renewable energy infrastructures. this study introduces a novel hybrid deep learning approach designed to improve the accuracy of wind power forecasting through the integration of long short-term memory (lstm) networks with an attention mechanism. the model's efficacy was rigorously evaluated using high-resolution data, recorded at 10-minute intervals, from two distinct meteorological stations located in khomein, saveh and tafresh, iran. the performance of the hybrid model was benchmarked against traditional machine learning methodologies, including random forest (rf), xgboost, and standalone lstm networks. the results of the evaluation demonstrate the superior performance of the hybrid lstm-attention model, which achieved notable coefficient of determination (r²) values of 0.9812, 0.9911 and 0.9842 at the khomein, saveh and tafresh stations, respectively, indicating significant advancements in forecasting accuracy compared to the other models. these enhanced forecasting capabilities have significant implications for facilitating the efficient integration of wind energy into electrical grids, thereby enabling more effective grid management practices and supporting optimized energy distribution strategies.