Household Power Consumption Prediction Method Based on Selective Ensemble Learning

In the context power big data, the household power consumption data on the user side has the characteristics of large quantity, wide distribution and many types. Ensemble learning is very excellent in the analysis and mining of power data with large amount of data, strong timeliness and many influencing factors. Based on the data of household power consumption, this paper analyzes and predicts the power consumption of some users in a city by using selective ensemble learning technology and combining with meteorological factors. In this paper, K-means algorithm is used to cluster the household power consumption, and then the clustering results are combined with the meteorological information. In the stage of power consumption prediction, a Filter Iterative Optimization Ensemble Strategy (FIOES) is proposed to selectively ensemble the basic learners and get the final prediction model. Experimental results show that the FIOES algorithm has better performance in time cost and prediction accuracy than the traditional ensemble learning algorithm.