Short-Term Load Forecasting via Heterogeneous Ensemble Learning: A Study on Cross-Node Accuracy and Stability

Qifan Peng; Kang Jiang; Zixuan Dai

doi:doi:10.11648/j.ajee.20261401.12

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Short-Term Load Forecasting via Heterogeneous Ensemble Learning: A Study on Cross-Node Accuracy and Stability

Qifan Peng

, Kang Jiang

, Zixuan Dai^*

Published in American Journal of Energy Engineering (Volume 14, Issue 1)

Received: 23 December 2025 Accepted: 14 January 2025 Published: 31 January 2026

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Abstract

Short-term load forecasting (STLF) serves as a fundamental basis for the efficient operation of smart grids and energy management systems. Accurate nodal-level load prediction plays a vital role in optimizing power dispatching, reducing operational costs, and enhancing grid security. Variations in user behavior patterns, geographical location, and equipment characteristics among different electricity nodes typically result in load profiles that exhibit pronounced volatility and non-stationarity. Traditional single model forecasting approaches are highly sensitive to data distribution and often struggle to maintain consistently high predictive accuracy across all nodes. Specifically, individual models frequently lack the generalization capability required for diverse load types, leading to significant error variability when applied to nodes with contrasting properties. To address these limitations, this study proposes a highly robust heterogeneous ensemble learning framework. First, a forecasting repository comprising six differentiated base models is constructed to accommodate diversity in model errors. Subsequently, a stacking-based meta-learning strategy is applied to integrate the outputs of the base models, enabling the extraction of multi-dimensional temporal features. To comprehensively validate the effectiveness of the proposed method, extensive comparative experiments are conducted using datasets from ten electricity nodes with diverse distribution characteristics. Experimental results demonstrate that the ensemble model significantly outperforms the base models in terms of accuracy while exhibiting superior stability across various nodes.

Published in	American Journal of Energy Engineering (Volume 14, Issue 1)
DOI	10.11648/j.ajee.20261401.12
Page(s)	9-17
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Power Load Forecasting, Ensemble Learning, Stacking, Stability Analysis, Deep Learning

References

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APA Style

Peng, Q., Jiang, K., Dai, Z. (2026). Short-Term Load Forecasting via Heterogeneous Ensemble Learning: A Study on Cross-Node Accuracy and Stability. American Journal of Energy Engineering, 14(1), 9-17. https://doi.org/10.11648/j.ajee.20261401.12

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ACS Style

Peng, Q.; Jiang, K.; Dai, Z. Short-Term Load Forecasting via Heterogeneous Ensemble Learning: A Study on Cross-Node Accuracy and Stability. Am. J. Energy Eng. 2026, 14(1), 9-17. doi: 10.11648/j.ajee.20261401.12

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AMA Style

Peng Q, Jiang K, Dai Z. Short-Term Load Forecasting via Heterogeneous Ensemble Learning: A Study on Cross-Node Accuracy and Stability. Am J Energy Eng. 2026;14(1):9-17. doi: 10.11648/j.ajee.20261401.12

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@article{10.11648/j.ajee.20261401.12,
  author = {Qifan Peng and Kang Jiang and Zixuan Dai},
  title = {Short-Term Load Forecasting via Heterogeneous Ensemble Learning: A Study on Cross-Node Accuracy and Stability},
  journal = {American Journal of Energy Engineering},
  volume = {14},
  number = {1},
  pages = {9-17},
  doi = {10.11648/j.ajee.20261401.12},
  url = {https://doi.org/10.11648/j.ajee.20261401.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajee.20261401.12},
  abstract = {Short-term load forecasting (STLF) serves as a fundamental basis for the efficient operation of smart grids and energy management systems. Accurate nodal-level load prediction plays a vital role in optimizing power dispatching, reducing operational costs, and enhancing grid security. Variations in user behavior patterns, geographical location, and equipment characteristics among different electricity nodes typically result in load profiles that exhibit pronounced volatility and non-stationarity. Traditional single model forecasting approaches are highly sensitive to data distribution and often struggle to maintain consistently high predictive accuracy across all nodes. Specifically, individual models frequently lack the generalization capability required for diverse load types, leading to significant error variability when applied to nodes with contrasting properties. To address these limitations, this study proposes a highly robust heterogeneous ensemble learning framework. First, a forecasting repository comprising six differentiated base models is constructed to accommodate diversity in model errors. Subsequently, a stacking-based meta-learning strategy is applied to integrate the outputs of the base models, enabling the extraction of multi-dimensional temporal features. To comprehensively validate the effectiveness of the proposed method, extensive comparative experiments are conducted using datasets from ten electricity nodes with diverse distribution characteristics. Experimental results demonstrate that the ensemble model significantly outperforms the base models in terms of accuracy while exhibiting superior stability across various nodes.},
 year = {2026}
}

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TY  - JOUR
T1  - Short-Term Load Forecasting via Heterogeneous Ensemble Learning: A Study on Cross-Node Accuracy and Stability
AU  - Qifan Peng
AU  - Kang Jiang
AU  - Zixuan Dai
Y1  - 2026/01/31
PY  - 2026
N1  - https://doi.org/10.11648/j.ajee.20261401.12
DO  - 10.11648/j.ajee.20261401.12
T2  - American Journal of Energy Engineering
JF  - American Journal of Energy Engineering
JO  - American Journal of Energy Engineering
SP  - 9
EP  - 17
PB  - Science Publishing Group
SN  - 2329-163X
UR  - https://doi.org/10.11648/j.ajee.20261401.12
AB  - Short-term load forecasting (STLF) serves as a fundamental basis for the efficient operation of smart grids and energy management systems. Accurate nodal-level load prediction plays a vital role in optimizing power dispatching, reducing operational costs, and enhancing grid security. Variations in user behavior patterns, geographical location, and equipment characteristics among different electricity nodes typically result in load profiles that exhibit pronounced volatility and non-stationarity. Traditional single model forecasting approaches are highly sensitive to data distribution and often struggle to maintain consistently high predictive accuracy across all nodes. Specifically, individual models frequently lack the generalization capability required for diverse load types, leading to significant error variability when applied to nodes with contrasting properties. To address these limitations, this study proposes a highly robust heterogeneous ensemble learning framework. First, a forecasting repository comprising six differentiated base models is constructed to accommodate diversity in model errors. Subsequently, a stacking-based meta-learning strategy is applied to integrate the outputs of the base models, enabling the extraction of multi-dimensional temporal features. To comprehensively validate the effectiveness of the proposed method, extensive comparative experiments are conducted using datasets from ten electricity nodes with diverse distribution characteristics. Experimental results demonstrate that the ensemble model significantly outperforms the base models in terms of accuracy while exhibiting superior stability across various nodes.
VL  - 14
IS  - 1
ER  -

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Author Information

Qifan Peng

School of Advanced Technology, Xi'an Jiao-tong Liverpool University, Suzhou, China

Contact Email

http://orcid.org/0009-0002-3828-8524
Kang Jiang

School of Information Technology and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China

Contact Email

http://orcid.org/0009-0003-4271-6285
Zixuan Dai

School of Advanced Technology, Xi'an Jiao-tong Liverpool University, Suzhou, China

Contact Email

http://orcid.org/0009-0004-9915-1403

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Plain Text BibTeX RIS

APA Style

Peng, Q., Jiang, K., Dai, Z. (2026). Short-Term Load Forecasting via Heterogeneous Ensemble Learning: A Study on Cross-Node Accuracy and Stability. American Journal of Energy Engineering, 14(1), 9-17. https://doi.org/10.11648/j.ajee.20261401.12

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ACS Style

Peng, Q.; Jiang, K.; Dai, Z. Short-Term Load Forecasting via Heterogeneous Ensemble Learning: A Study on Cross-Node Accuracy and Stability. Am. J. Energy Eng. 2026, 14(1), 9-17. doi: 10.11648/j.ajee.20261401.12

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AMA Style

Peng Q, Jiang K, Dai Z. Short-Term Load Forecasting via Heterogeneous Ensemble Learning: A Study on Cross-Node Accuracy and Stability. Am J Energy Eng. 2026;14(1):9-17. doi: 10.11648/j.ajee.20261401.12

Copy | Download

@article{10.11648/j.ajee.20261401.12,
  author = {Qifan Peng and Kang Jiang and Zixuan Dai},
  title = {Short-Term Load Forecasting via Heterogeneous Ensemble Learning: A Study on Cross-Node Accuracy and Stability},
  journal = {American Journal of Energy Engineering},
  volume = {14},
  number = {1},
  pages = {9-17},
  doi = {10.11648/j.ajee.20261401.12},
  url = {https://doi.org/10.11648/j.ajee.20261401.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajee.20261401.12},
  abstract = {Short-term load forecasting (STLF) serves as a fundamental basis for the efficient operation of smart grids and energy management systems. Accurate nodal-level load prediction plays a vital role in optimizing power dispatching, reducing operational costs, and enhancing grid security. Variations in user behavior patterns, geographical location, and equipment characteristics among different electricity nodes typically result in load profiles that exhibit pronounced volatility and non-stationarity. Traditional single model forecasting approaches are highly sensitive to data distribution and often struggle to maintain consistently high predictive accuracy across all nodes. Specifically, individual models frequently lack the generalization capability required for diverse load types, leading to significant error variability when applied to nodes with contrasting properties. To address these limitations, this study proposes a highly robust heterogeneous ensemble learning framework. First, a forecasting repository comprising six differentiated base models is constructed to accommodate diversity in model errors. Subsequently, a stacking-based meta-learning strategy is applied to integrate the outputs of the base models, enabling the extraction of multi-dimensional temporal features. To comprehensively validate the effectiveness of the proposed method, extensive comparative experiments are conducted using datasets from ten electricity nodes with diverse distribution characteristics. Experimental results demonstrate that the ensemble model significantly outperforms the base models in terms of accuracy while exhibiting superior stability across various nodes.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - Short-Term Load Forecasting via Heterogeneous Ensemble Learning: A Study on Cross-Node Accuracy and Stability
AU  - Qifan Peng
AU  - Kang Jiang
AU  - Zixuan Dai
Y1  - 2026/01/31
PY  - 2026
N1  - https://doi.org/10.11648/j.ajee.20261401.12
DO  - 10.11648/j.ajee.20261401.12
T2  - American Journal of Energy Engineering
JF  - American Journal of Energy Engineering
JO  - American Journal of Energy Engineering
SP  - 9
EP  - 17
PB  - Science Publishing Group
SN  - 2329-163X
UR  - https://doi.org/10.11648/j.ajee.20261401.12
AB  - Short-term load forecasting (STLF) serves as a fundamental basis for the efficient operation of smart grids and energy management systems. Accurate nodal-level load prediction plays a vital role in optimizing power dispatching, reducing operational costs, and enhancing grid security. Variations in user behavior patterns, geographical location, and equipment characteristics among different electricity nodes typically result in load profiles that exhibit pronounced volatility and non-stationarity. Traditional single model forecasting approaches are highly sensitive to data distribution and often struggle to maintain consistently high predictive accuracy across all nodes. Specifically, individual models frequently lack the generalization capability required for diverse load types, leading to significant error variability when applied to nodes with contrasting properties. To address these limitations, this study proposes a highly robust heterogeneous ensemble learning framework. First, a forecasting repository comprising six differentiated base models is constructed to accommodate diversity in model errors. Subsequently, a stacking-based meta-learning strategy is applied to integrate the outputs of the base models, enabling the extraction of multi-dimensional temporal features. To comprehensively validate the effectiveness of the proposed method, extensive comparative experiments are conducted using datasets from ten electricity nodes with diverse distribution characteristics. Experimental results demonstrate that the ensemble model significantly outperforms the base models in terms of accuracy while exhibiting superior stability across various nodes.
VL  - 14
IS  - 1
ER  -

Copy | Download