Feedforward Error Learning Deep Neural Networks for Multivariate Deterministic Power Forecasting

Min Seung Ko, Kwangsuk Lee, Kyeon Hur

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


This article proposes a deep neural network (DNN) framework for multivariate deterministic power forecasting in the context of the high penetration of variable and uncertain renewable energy sources. The deep learning model is organized based on the 1-D convolutional neural network to lessen the computational burden, typical of recurrent neural network based models, and combines WaveNet and EfficientNet to improve the forecasting accuracy. Motivated by the inefficiency that all the models conduct the same tasks in the popular ensemble approach, we also designed a feedforward error learning DNN, which computes the error of the basic model separately. We further incorporated embedded and filter methods for feature selection to enhance the model visibility and the utility of the framework. Comprehensive studies on the public load and PV datasets demonstrate that the proposed framework outperforms the conventional methods in applicability, computational efficiency, and forecasting accuracy.

Original languageEnglish
Pages (from-to)6214-6223
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Issue number9
Publication statusPublished - 2022 Sept 1

Bibliographical note

Funding Information:
This work was supported in part by the National Research Foundation of Korea funded by the Korea government (MSIT) under Grant 2021R1A2C2095503 and in part by the Korea Electric Power Corporation under Grant R21XO01-8.

Publisher Copyright:
© 2005-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering


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