Multiple Load Forecasting of Integrated Energy System Based on Sequential-Parallel Hybrid Ensemble Learning

Abstract

Accurate multivariate load forecasting plays an important role in the planning management and safe operation of integrated energy systems. In order to simultaneously reduce the prediction bias and variance, a hybrid ensemble learning method for load forecasting of an integrated energy system combining sequential ensemble learning and parallel ensemble learning is proposed. Firstly, the load correlation and the maximum information coefficient (MIC) are used for feature selection. Then the base learner uses the Boost algorithm of sequential ensemble learning and uses the Bagging algorithm of parallel ensemble learning for hybrid ensemble learning prediction. The grid search algorithm (GS) performs hyper-parameter optimization of hybrid ensemble learning. The comparative analysis of the example verification shows that compared with different types of single ensemble learning, hybrid ensemble learning can better balance the bias and variance and accurately predict multiple loads such as electricity, cold, and heat in the integrated energy system.