Atrous Convolutions and Residual GRU Based Architecture for Matching Power Demand with Supply

Nowadays, for efficient energy management, local demand-supply matching in power grid is emerging research domain. However, energy demand is increasing day by day in many countries due to rapid growth of the population and most of their work being reliant on electronic devices. This problem has high...

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Autores principales:	Samee Ullah Khan, Ijaz Ul Haq, Zulfiqar Ahmad Khan, Noman Khan, Mi Young Lee, Sung Wook Baik
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep learning energy management system energy consumption renewable energy smart grid Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/200884fc5d5a4221952fa5027d27f4a0
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spelling	oai:doaj.org-article:200884fc5d5a4221952fa5027d27f4a02021-11-11T19:10:49ZAtrous Convolutions and Residual GRU Based Architecture for Matching Power Demand with Supply10.3390/s212171911424-8220https://doaj.org/article/200884fc5d5a4221952fa5027d27f4a02021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7191https://doaj.org/toc/1424-8220Nowadays, for efficient energy management, local demand-supply matching in power grid is emerging research domain. However, energy demand is increasing day by day in many countries due to rapid growth of the population and most of their work being reliant on electronic devices. This problem has highlighted the significance of effectively matching power demand with supply for optimal energy management. To resolve this issue, we present an intelligent deep learning framework that integrates Atrous Convolutional Layers (ACL) with Residual Gated Recurrent Units (RGRU) to establish balance between the demand and supply. Moreover, it accurately predicts short-term energy and delivers a systematic method of communication between consumers and energy distributors as well. To cope with the varying nature of electricity data, first data acquisition step is performed where data are collected from various sources such as smart meters and solar plants. In the second step a pre-processing method is applied on raw data to normalize and clean the data. Next, the refined data are passed to ACL for spatial feature extraction. Finally, a sequential learning model RGRU is used that learns from complicated patterns for the final output. The proposed model obtains the smallest values of Mean Square Error (MSE) including 0.1753, 0.0001, 0.0177 over IHEPC, KCB, and Solar datasets, respectively, which manifests better performance as compared to existing approaches.Samee Ullah KhanIjaz Ul HaqZulfiqar Ahmad KhanNoman KhanMi Young LeeSung Wook BaikMDPI AGarticledeep learningenergy management systemenergy consumptionrenewable energysmart gridChemical technologyTP1-1185ENSensors, Vol 21, Iss 7191, p 7191 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning energy management system energy consumption renewable energy smart grid Chemical technology TP1-1185
spellingShingle	deep learning energy management system energy consumption renewable energy smart grid Chemical technology TP1-1185 Samee Ullah Khan Ijaz Ul Haq Zulfiqar Ahmad Khan Noman Khan Mi Young Lee Sung Wook Baik Atrous Convolutions and Residual GRU Based Architecture for Matching Power Demand with Supply
description	Nowadays, for efficient energy management, local demand-supply matching in power grid is emerging research domain. However, energy demand is increasing day by day in many countries due to rapid growth of the population and most of their work being reliant on electronic devices. This problem has highlighted the significance of effectively matching power demand with supply for optimal energy management. To resolve this issue, we present an intelligent deep learning framework that integrates Atrous Convolutional Layers (ACL) with Residual Gated Recurrent Units (RGRU) to establish balance between the demand and supply. Moreover, it accurately predicts short-term energy and delivers a systematic method of communication between consumers and energy distributors as well. To cope with the varying nature of electricity data, first data acquisition step is performed where data are collected from various sources such as smart meters and solar plants. In the second step a pre-processing method is applied on raw data to normalize and clean the data. Next, the refined data are passed to ACL for spatial feature extraction. Finally, a sequential learning model RGRU is used that learns from complicated patterns for the final output. The proposed model obtains the smallest values of Mean Square Error (MSE) including 0.1753, 0.0001, 0.0177 over IHEPC, KCB, and Solar datasets, respectively, which manifests better performance as compared to existing approaches.
format	article
author	Samee Ullah Khan Ijaz Ul Haq Zulfiqar Ahmad Khan Noman Khan Mi Young Lee Sung Wook Baik
author_facet	Samee Ullah Khan Ijaz Ul Haq Zulfiqar Ahmad Khan Noman Khan Mi Young Lee Sung Wook Baik
author_sort	Samee Ullah Khan
title	Atrous Convolutions and Residual GRU Based Architecture for Matching Power Demand with Supply
title_short	Atrous Convolutions and Residual GRU Based Architecture for Matching Power Demand with Supply
title_full	Atrous Convolutions and Residual GRU Based Architecture for Matching Power Demand with Supply
title_fullStr	Atrous Convolutions and Residual GRU Based Architecture for Matching Power Demand with Supply
title_full_unstemmed	Atrous Convolutions and Residual GRU Based Architecture for Matching Power Demand with Supply
title_sort	atrous convolutions and residual gru based architecture for matching power demand with supply
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/200884fc5d5a4221952fa5027d27f4a0
work_keys_str_mv	AT sameeullahkhan atrousconvolutionsandresidualgrubasedarchitectureformatchingpowerdemandwithsupply AT ijazulhaq atrousconvolutionsandresidualgrubasedarchitectureformatchingpowerdemandwithsupply AT zulfiqarahmadkhan atrousconvolutionsandresidualgrubasedarchitectureformatchingpowerdemandwithsupply AT nomankhan atrousconvolutionsandresidualgrubasedarchitectureformatchingpowerdemandwithsupply AT miyounglee atrousconvolutionsandresidualgrubasedarchitectureformatchingpowerdemandwithsupply AT sungwookbaik atrousconvolutionsandresidualgrubasedarchitectureformatchingpowerdemandwithsupply
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Atrous Convolutions and Residual GRU Based Architecture for Matching Power Demand with Supply

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