MLfus: A real-time forecasting architecture for low communication costs in electricity IoT based on ensemble learning

Abstract

With the application and popularity of Internet of Things (IoT) technology, real-time prediction of time series data has become the focus of electricity IoT data governance. At present, most of the time-series data prediction methods for the electricity IoT have the defect of being unable to process-related information between sequences. What's worse, the mainstream data fusion methods all have the problem of limited data dimension. This paper proposes a decision-level fusion architecture MLfus for multi-source time-series data generated under the distributed cloud edge structure of the electricity IoT. The model uses ensemble learning to make decisions and judgments on distributed time-series data and integrates multi-source data to make real-time predictions. MLfus solves the problem of significantly biased predictions from a single model and excels in handling complex nonlinear problems. What's more, MLfus can reduce data and additional training requirements substantially by using decision-level fusion. Experimental results show that MLfus has a clear advantage in the problem of real-time electricity price prediction, providing better accuracy while reducing the communication burden.