Clustering and dynamic recognition based auto-reservoir neural network: A wait-and-see approach for short-term park power load forecasting.

Jingyao Liu, Jiajia Chen, Guijin Yan, Wengang Chen, Bingyin Xu

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Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 authors.

Jingyao LiuSchool of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China.

Jiajia ChenSchool of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China.

Guijin YanSchool of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China.

Wengang ChenSchool of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China.

Bingyin XuSchool of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China.

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No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper proposes a novel clustering and dynamic recognition-based auto-reservoir neural network (CDbARNN) for short-term load forecasting (STLF) of industrial park microgrids. In CDbARNN, the available load sets are first decomposed into several clusters via K-means clustering. Then, by extracting characteristic information of the load series input to CDbARNN and the load curves belonging to each cluster center, a dynamic recognition technology is developed to identify which cluster of the input load series belongs to. After that, the input load series and the load curves of the cluster to which it belongs constitute a short-term high-dimensional matrix entered into the reservoir of CDbARNN. Finally, reservoir node numbers of CDbARNN which are used to match different clusters are optimized. Numerical experiments conducted on STLF of an actual industrial park microgrid indicate the dominating performance of the proposed approach through several cases and comparisons with other well-known deep learning methods.

Indexed as

Electrical engineeringEnergy engineeringEnergy systemsEngineeringMechanical engineering

Identifiers

PMID37575195

DOI10.1016/j.isci.2023.107456

PMCPMC10415916

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LicenceCC BY-NC-ND

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