Document Type

Article

Source of Publication

Computers, Materials and Continua

Publication Date

3-22-2021

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 benchmarkmodels. 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 17,535,700KRWeach 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.

ISSN

1546-2218

Publisher

Computers, Materials and Continua (Tech Science Press)

Volume

68

First Page

283

Last Page

301

Disciplines

Computer Sciences

Keywords

deep learning, hybrid architecture, Load forecasting, maximum load duration, time-of-use

Scopus ID

85103615925

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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