Echo state neural network based ensemble deep learning for short-term load forecasting

Ruobin Gao; P. N. Suganthan; Qin Zhou; Kum Fai Yuen; M. Tanveer

doi:10.1109/SSCI51031.2022.10022067

Echo state neural network based ensemble deep learning for short-term load forecasting

Ruobin Gao, P. N. Suganthan, Qin Zhou, Kum Fai Yuen, M. Tanveer

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Citations (Scopus)

Abstract

Precise electricity load forecasts assist in planning, maintaining, and developing power systems. However, the electricity load's un-stationary and non-linear characteristics impose substantial challenges in anticipating future demand. Recently, a deep echo state network (DESN) with multi-scale features has been proposed for sequential tasks. Inspired by its structure, this paper offers a novel ensemble deep learning algorithm, the ensemble deep ESN (edESN), for load forecasting. First, hierarchical reservoirs are stacked to enforce the deep representation similar to the DESN. Then, instead of computing the readout weights based on the global states, the edESN trains a different readout layer for each scale. Finally, the network combines the outputs from each scale as the final prediction. The edESN is evaluated on twenty publicly available load datasets. This paper compares the edESN with eleven forecasting methods, and the comparative results demonstrate the proposed model's superiority in load forecasting.

Original language	English
Title of host publication	Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022
Editors	Hisao Ishibuchi, Chee-Keong Kwoh, Ah-Hwee Tan, Dipti Srinivasan, Chunyan Miao, Anupam Trivedi, Keeley Crockett
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	277-284
Number of pages	8
ISBN (Electronic)	9781665487689
DOIs	https://doi.org/10.1109/SSCI51031.2022.10022067
Publication status	Published - 2022
Externally published	Yes
Event	2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 - Singapore, Singapore Duration: Dec 4 2022 → Dec 7 2022

Publication series

Name	Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022

Conference

Conference	2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022
Country/Territory	Singapore
City	Singapore
Period	12/4/22 → 12/7/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

ASJC Scopus Subject Areas

Artificial Intelligence
Computer Science Applications
Decision Sciences (miscellaneous)
Computational Mathematics
Control and Optimization
Transportation

Keywords

deep echo state network
deep learning
echo state network
Forecasting
machine learning

Access to Document

10.1109/SSCI51031.2022.10022067

Cite this

Gao, R., Suganthan, P. N., Zhou, Q., Fai Yuen, K., & Tanveer, M. (2022). Echo state neural network based ensemble deep learning for short-term load forecasting. In H. Ishibuchi, C.-K. Kwoh, A.-H. Tan, D. Srinivasan, C. Miao, A. Trivedi, & K. Crockett (Eds.), Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 (pp. 277-284). (Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI51031.2022.10022067

Gao, Ruobin ; Suganthan, P. N. ; Zhou, Qin et al. / Echo state neural network based ensemble deep learning for short-term load forecasting. Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022. editor / Hisao Ishibuchi ; Chee-Keong Kwoh ; Ah-Hwee Tan ; Dipti Srinivasan ; Chunyan Miao ; Anupam Trivedi ; Keeley Crockett. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 277-284 (Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022).

@inproceedings{8f27194909294137aa90fab97b351e30,

title = "Echo state neural network based ensemble deep learning for short-term load forecasting",

abstract = "Precise electricity load forecasts assist in planning, maintaining, and developing power systems. However, the electricity load's un-stationary and non-linear characteristics impose substantial challenges in anticipating future demand. Recently, a deep echo state network (DESN) with multi-scale features has been proposed for sequential tasks. Inspired by its structure, this paper offers a novel ensemble deep learning algorithm, the ensemble deep ESN (edESN), for load forecasting. First, hierarchical reservoirs are stacked to enforce the deep representation similar to the DESN. Then, instead of computing the readout weights based on the global states, the edESN trains a different readout layer for each scale. Finally, the network combines the outputs from each scale as the final prediction. The edESN is evaluated on twenty publicly available load datasets. This paper compares the edESN with eleven forecasting methods, and the comparative results demonstrate the proposed model's superiority in load forecasting.",

keywords = "deep echo state network, deep learning, echo state network, Forecasting, machine learning",

author = "Ruobin Gao and Suganthan, {P. N.} and Qin Zhou and {Fai Yuen}, Kum and M. Tanveer",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 ; Conference date: 04-12-2022 Through 07-12-2022",

year = "2022",

doi = "10.1109/SSCI51031.2022.10022067",

language = "English",

series = "Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Gao, R, Suganthan, PN, Zhou, Q, Fai Yuen, K & Tanveer, M 2022, Echo state neural network based ensemble deep learning for short-term load forecasting. in H Ishibuchi, C-K Kwoh, A-H Tan, D Srinivasan, C Miao, A Trivedi & K Crockett (eds), Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022. Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022, Institute of Electrical and Electronics Engineers Inc., pp. 277-284, 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022, Singapore, Singapore, 12/4/22. https://doi.org/10.1109/SSCI51031.2022.10022067

Echo state neural network based ensemble deep learning for short-term load forecasting. / Gao, Ruobin; Suganthan, P. N.; Zhou, Qin et al.
Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022. ed. / Hisao Ishibuchi; Chee-Keong Kwoh; Ah-Hwee Tan; Dipti Srinivasan; Chunyan Miao; Anupam Trivedi; Keeley Crockett. Institute of Electrical and Electronics Engineers Inc., 2022. p. 277-284 (Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Echo state neural network based ensemble deep learning for short-term load forecasting

AU - Gao, Ruobin

AU - Suganthan, P. N.

AU - Zhou, Qin

AU - Fai Yuen, Kum

AU - Tanveer, M.

PY - 2022

Y1 - 2022

N2 - Precise electricity load forecasts assist in planning, maintaining, and developing power systems. However, the electricity load's un-stationary and non-linear characteristics impose substantial challenges in anticipating future demand. Recently, a deep echo state network (DESN) with multi-scale features has been proposed for sequential tasks. Inspired by its structure, this paper offers a novel ensemble deep learning algorithm, the ensemble deep ESN (edESN), for load forecasting. First, hierarchical reservoirs are stacked to enforce the deep representation similar to the DESN. Then, instead of computing the readout weights based on the global states, the edESN trains a different readout layer for each scale. Finally, the network combines the outputs from each scale as the final prediction. The edESN is evaluated on twenty publicly available load datasets. This paper compares the edESN with eleven forecasting methods, and the comparative results demonstrate the proposed model's superiority in load forecasting.

AB - Precise electricity load forecasts assist in planning, maintaining, and developing power systems. However, the electricity load's un-stationary and non-linear characteristics impose substantial challenges in anticipating future demand. Recently, a deep echo state network (DESN) with multi-scale features has been proposed for sequential tasks. Inspired by its structure, this paper offers a novel ensemble deep learning algorithm, the ensemble deep ESN (edESN), for load forecasting. First, hierarchical reservoirs are stacked to enforce the deep representation similar to the DESN. Then, instead of computing the readout weights based on the global states, the edESN trains a different readout layer for each scale. Finally, the network combines the outputs from each scale as the final prediction. The edESN is evaluated on twenty publicly available load datasets. This paper compares the edESN with eleven forecasting methods, and the comparative results demonstrate the proposed model's superiority in load forecasting.

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BT - Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022

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Gao R, Suganthan PN, Zhou Q, Fai Yuen K, Tanveer M. Echo state neural network based ensemble deep learning for short-term load forecasting. In Ishibuchi H, Kwoh CK, Tan AH, Srinivasan D, Miao C, Trivedi A, Crockett K, editors, Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 277-284. (Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022). doi: 10.1109/SSCI51031.2022.10022067

Echo state neural network based ensemble deep learning for short-term load forecasting

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Keywords

Access to Document

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