Multiple Load Forecasting of Integrated Energy System Based on Sequential-Parallel Hybrid Ensemble Learning
Wenxia You,
Daopeng Guo (),
Yonghua Wu and
Wenwu Li
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Wenxia You: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
Daopeng Guo: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
Yonghua Wu: Hubei Electric Power Co., Ltd., Xiaogan Power Supply Company, Xiaogan 432000, China
Wenwu Li: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
Energies, 2023, vol. 16, issue 7, 1-16
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.
Keywords: load forecasting of integrated energy system; maximum information coefficient; ensemble learning; grid search (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)
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