Short‐term load forecasting of multi‐scale recurrent neural networks based on residual structure

Author:

Zhao Jia1ORCID,Cheng Pengyu1,Hou Jiazhen1,Fan Tanghuai1,Han Longzhe1

Affiliation:

1. School of Information Engineering Nanchang Institute of Technology Nanchang China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Reference38 articles.

1. Transformer load forecasting based on adaptive deep belief network;Yang Z;Proc CSEE,2019

2. Short‐term load forecasting of power system for holiday point‐by‐point grown rate based on Kalman filtering;Chen P;Eng J Wuhan Univ,2020

3. Bidding strategy for time‐shiftable loads based on autoregressive integrated moving average model;Ai X;Autom Elect Power Syst,2017

4. Short-term load forecasting via ARMA model identification including non-gaussian process considerations

5. Pattern-based local linear regression models for short-term load forecasting

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