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

Echo state neural network-based ensemble deep learning for short-term load forecasting
Echo state neural network-based ensemble deep learning for short-term load forecasting
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.
Forecasting, deep echo state network, deep learning, echo state network, machine learning
277-284
Gao, Ruobin
0ccb66e0-4b50-442c-8619-620469b4974b
Suganthan, Ponnuthurai Nagaratnam
75d9dbea-5448-45bd-8bd6-da1616f08ace
Zhou, Qin
22cc3c1b-50f4-41e0-9c3e-8cdf183a022e
Yuen, Kum Fai
5acba8bd-2837-4913-8ec8-5b9b98cb04b9
Tanveer, M.
60db68fa-e818-4ea5-bdb4-a03a80628fdd
Gao, Ruobin
0ccb66e0-4b50-442c-8619-620469b4974b
Suganthan, Ponnuthurai Nagaratnam
75d9dbea-5448-45bd-8bd6-da1616f08ace
Zhou, Qin
22cc3c1b-50f4-41e0-9c3e-8cdf183a022e
Yuen, Kum Fai
5acba8bd-2837-4913-8ec8-5b9b98cb04b9
Tanveer, M.
60db68fa-e818-4ea5-bdb4-a03a80628fdd

Gao, Ruobin, Suganthan, Ponnuthurai Nagaratnam, Zhou, Qin, Yuen, Kum Fai and Tanveer, M. (2022) Echo state neural network-based ensemble deep learning for short-term load forecasting. 2022 IEEE Symposium Series on Computational Intelligence (SSCI), , Singapore, Singapore. 04 Dec 2022 - 07 Mar 2023 . pp. 277-284 . (doi:10.1109/SSCI51031.2022.10022067).

Record type: Conference or Workshop Item (Paper)

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.

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More information

Published date: 2022
Additional Information: Publisher Copyright: © 2022 IEEE.
Venue - Dates: 2022 IEEE Symposium Series on Computational Intelligence (SSCI), , Singapore, Singapore, 2022-12-04 - 2023-03-07
Keywords: Forecasting, deep echo state network, deep learning, echo state network, machine learning

Identifiers

Local EPrints ID: 475696
URI: http://eprints.soton.ac.uk/id/eprint/475696
PURE UUID: f2e289bc-20e2-4e04-969f-014c4bcac8f0
ORCID for Qin Zhou: ORCID iD orcid.org/0000-0002-0273-6295

Catalogue record

Date deposited: 24 Mar 2023 17:59
Last modified: 17 Mar 2024 04:18

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Contributors

Author: Ruobin Gao
Author: Ponnuthurai Nagaratnam Suganthan
Author: Qin Zhou ORCID iD
Author: Kum Fai Yuen
Author: M. Tanveer

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