부산대학교 도서관

Power generation (PG) prediction from renewable energy sources (RESs) plays a vital role in effective energy management in smart cities. However, harnessing the potential of edge intelligence in well-controlled Internet of Things (IoT) networks poses significant challenges. To address this, we propose an IoT-based framework for intelligent and efficient PG prediction in smart microgrids. The framework begins by acquiring data from various RESs, including wind and solar. Before the training process, the data undergoes cleaning and normalization steps that use denoising and cleansing filters. For forecasting renewable energy (RE), we introduce a hybrid model that integrates a multi-head attention (MHA)-based deep autoencoder (AE) with extreme gradient boosting (XGB) algorithm. The AE’s encoder component extracts discriminative features from the cleaned data sequence, which are then learned by XGB to provide a final PG forecast. This edge computing layer facilitates information sharing through fog computing, which ensures power balancing between suppliers and consumers. Furthermore, the framework also incorporates various power consumption (PC) sectors and entities within smart cities, such as transportation and healthcare, to ensure efficient management. We evaluate the proposed hybrid model using publicly accessible benchmarks and locally gathered data sets, demonstrating state-of-the-art performance in terms of error metrics. The computational complexity of the proposed model is also suitable for resource-constrained IoT devices connected to a shared IoT-Fog setup, enabling seamless communication with smart microgrids for effective power management.