Open Access

ARTICLE

Hybrid Deep Learning Architecture to Forecast Maximum Load Duration Using Time-of-Use Pricing Plans

by Jinseok Kim¹, Babar Shah², Ki-Il Kim^3,*

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 the Special Issue: Deep Learning and Parallel Computing for Intelligent and Efficient IoT)

Computers, Materials & Continua 2021, 68(1), 283-301. https://doi.org/10.32604/cmc.2021.016042

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.

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