A novel Bagged-CNN architecture for short-term wind power forecasting

Abstract

ABSTRACT Wind power forecasting plays a pivotal role in ensuring the dependable integration of wind energy into power grids. This research introduces a novel Bagged-CNN architecture tailored for short-term wind power forecasting. This architecture harnesses the combined strengths of the Bagging ensemble technique and Convolutional Neural Networks (CNN) to enhance prediction accuracy and robustness. Our approach demonstrates superior forecasting capabilities by exploiting the spatial and temporal features captured by CNNs. To validate our proposed architecture, we have conducted a comprehensive evaluation across three distinct wind farms, each characterized by unique geographical and climatic conditions. We systematically compared the performance of our Bagged-CNN model against several standalone machine-learning algorithms commonly employed in wind power forecasting. The hyperparameters of our Bagged-CNN model are meticulously optimized using the GRID search algorithm, which systematically explores a predefined parameter space. Notably, our predictions shed light on the substantial impact of training data period length on wind power prediction accuracy. Our research underscores the potential of coupling Bagging and CNN techniques for short-term wind power forecasting. The Bagged-CNN architecture, as proposed, serves as a robust framework for capturing intricate spatial and temporal patterns in wind power data, thereby facilitating more precise and dependable predictions.