SPATIO-TEMPORAL JOINT ROLLING LOAD FORECASTING BASED ON STACKING ENSEMBLE LEARNING

Yan Xiangwu; Cao Heyang; Tong Sihan; Shao Chen; Jia Jiaoxin; Lin Yixuan

doi:10.19912/j.0254-0096.tynxb.2025-0494

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (8) : 328-337. DOI: 10.19912/j.0254-0096.tynxb.2025-0494

SPATIO-TEMPORAL JOINT ROLLING LOAD FORECASTING BASED ON STACKING ENSEMBLE LEARNING

Yan Xiangwu¹, Cao Heyang¹, Tong Sihan¹, Shao Chen², Jia Jiaoxin¹, Lin Yixuan¹

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Abstract

Addressing the problem of short-term load forecasting difficulty in power systems, this paper proposes an integrated learning load forecasting model that considers the spatio-temporal joint influence of load areas. Firstly, a spatiotemporally joint sampling scheme for load areas is proposed, using recent load data from other load areas to predict the load in the current load area, thereby making more use of highly time-sensitive data. Secondly, base learners are constructed based on machine learning and neural network models such as Extreme Gradient Boosting （XGBoost）. Combined with cross-validation, an improved Bayesian algorithm is used to distributedly optimize model hyperparameters, aiming to maximize their respective prediction performance. Thirdly, a meta-learner is built based on deep learning to implement Stacking ensemble learning, enabling it to combine the learning results of each base learner to output the final predicted value. Finally, case study validation is performed using a load dataset from southern China, and comparisons are made with commonly used sampling methods, verifying the feasibility and superiority of the proposed integrated learning model and sampling feature scheme.

Key words

electrical loads / forecasting / sampling method / Bayesian optimization / Stacking ensemble learning / neural networks

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Yan Xiangwu, Cao Heyang, Tong Sihan, Shao Chen, Jia Jiaoxin, Lin Yixuan. SPATIO-TEMPORAL JOINT ROLLING LOAD FORECASTING BASED ON STACKING ENSEMBLE LEARNING[J]. Acta Energiae Solaris Sinica. 2026, 47(8): 328-337 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0494

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