Due to the fast, dynamic, and continuous arrival of data streams in the green Internet of Things (IoT) environment, the probability distribution of data streams changes over time. In real IoT scenarios such as unmanned aerial vehicle (UAV) detection and smart light switch control, data distribution changes have reduced the trained model’s accuracy for data streams problems classification, making it challenging to detect UAV intruders and predict whether energy-saving lamps in smart buildings are on or off. In this paper, an incremental ensemble classification method is proposed to improve prediction accuracy for green IoT. Specifically, a fuzzy rule-based classifier is combined with a dynamic weighting algorithm for improving classification accuracy. Moreover, the model is updated by incrementally learning the characteristics of data streams, which can effectively handle concept drift caused by data distribution changes in data streams. Experimental evaluations of UAV intrusion detection, smart buildings, and other datasets show that the proposed approach yields 2% higher area under the curve (AUC) and geometric mean (G-mean) than existing methods on UAV Detection and Occupancy datasets and 5% higher AUC and G-mean on five benchmarking datasets. For all datasets, the proposed approach yields 50% faster average training time than other methods.