Vol.68, No.1, 2021, pp.283-301, doi:10.32604/cmc.2021.016042
OPEN ACCESS
ARTICLE
Hybrid Deep Learning Architecture to Forecast Maximum Load Duration Using Time-of-Use Pricing Plans
  • Jinseok Kim1, Babar Shah2, Ki-Il Kim3,*
1 Department of KDN Electric power IT Research Institute, KEPCO KDN, Naju, 58217, Korea
2 College of Technological Innovation, Zayed University, Abu Dhabi, UAE
3 Department of Computer Science & Engineering, Chungnam National University, Daejeon, 34134, Korea
* Corresponding Author: Ki-Il Kim. Email:
(This article belongs to this Special Issue: Deep Learning and Parallel Computing for Intelligent and Efficient IoT)
Received 20 December 2020; Accepted 28 January 2021; Issue published 22 March 2021
Abstract
Load forecasting has received crucial research attention to reduce peak load and contribute to the stability of power grid using machine learning or deep learning models. Especially, we need the adequate model to forecast the maximum load duration based on time-of-use, which is the electricity usage fare policy in order to achieve the goals such as peak load reduction in a power grid. However, the existing single machine learning or deep learning forecasting cannot easily avoid overfitting. Moreover, a majority of the ensemble or hybrid models do not achieve optimal results for forecasting the maximum load duration based on time-of-use. To overcome these limitations, we propose a hybrid deep learning architecture to forecast maximum load duration based on time-of-use. Experimental results indicate that this architecture could achieve the highest average of recall and accuracy (83.43%) compared to benchmark models. To verify the effectiveness of the architecture, another experimental result shows that energy storage system (ESS) scheme in accordance with the forecast results of the proposed model (LSTM-MATO) in the architecture could provide peak load cost savings of 17,535,700 KRW each year comparing with original peak load costs without the method. Therefore, the proposed architecture could be utilized for practical applications such as peak load reduction in the grid.
Keywords
Load forecasting; deep learning; hybrid architecture; maximum load duration; time-of-use
Cite This Article
J. Kim, B. Shah and K. Kim, "Hybrid deep learning architecture to forecast maximum load duration using time-of-use pricing plans," Computers, Materials & Continua, vol. 68, no.1, pp. 283–301, 2021.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.