An Ensemble Learning Approach for Short-Term Load Forecasting of Grid-Connected Multi-energy Microgrid

In grid-connected multi-energy microgrid, fluctuation of renewable energy generation and coupling of multiple energy resources make the power load difficult to forecast accurately. In this paper, we focus on the short-term gateway load forecasting of grid-connected multi-energy microgrid. Consider spatial correlation between microgrid nodes, the information of multiple nodes, e.g., renewable energy access node, gas turbine access node and some critical load nodes, is utilized to implement information fusion forecasting. We propose an ensemble model that integrates GBRT, XGboost, Decison Tree and Seq2Seq to solve the problem. An IEEE33 bus system based simulation is conducted on an integrated platform with OpenDSS and Simulink. The experimental results show that the proposed approach outperforms several classical time series models with higher accuracy and better stability.