Enhanced load forecasting for distributed multi-energy system: A stacking ensemble learning method with deep reinforcement learning and model fusion

Xiaoxiao Ren, Xin Tian, Kai Wang, Sifan Yang, Weixiong Chen and Jinshi Wang

Energy, 2025, vol. 319, issue C

Abstract: Accurate multi-energy load forecasting for distributed multi-energy systems is facing challenges due to the complexity of multi-energy coupling and the inherent stochasticity. In this regard, a novel stacking ensemble learning model based on reinforcement learning (RL) and model fusion is proposed. First, feature selection is performed using the maximal information coefficient (MIC), load data is decomposed and reconstructed through the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and sample entropy (SE). Subsequently, the fused models with strong predictive capabilities are selected as base learners, and the RL based on deep deterministic policy gradient (DDPG) with excellent adaptive learning ability is selected as meta-learner. Next, the hyperparameters of the base learners are optimized using an improved arctic puffin optimization (APO) algorithm. To reduce overfitting and improve the generalization ability of the model, k-fold cross-validation is adopted to construct the model. Tests on real-world datasets demonstrate that the proposed method achieves smaller prediction errors, enhanced robustness and reliability. Moreover, through careful base learner selection and the utilization of RL as the meta-learner, the model achieves up to a 1.53 % improvement in coefficient of determination, a 36.09 % increase in residual prediction deviation, and a 102.96 % reduction in rooted mean square error.

Keywords: Distributed multi-energy system; Load forecasting; Stacking ensemble learning; Reinforcement learning; Model fusion (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225006735
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:319:y:2025:i:c:s0360544225006735

DOI: 10.1016/j.energy.2025.135031

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().