[HTML][HTML] Day-ahead residential electricity demand response model based on deep neural networks for peak demand reduction in the Jordanian power sector
Featured Application The presented model can be used as a baseline model for the
implementation of peak demand response systems in Jordan. The day-ahead prediction …
implementation of peak demand response systems in Jordan. The day-ahead prediction …
Day‐ahead residential electricity demand response model based on deep neural networks for peak demand reduction in the jordanian power sector
A Shaqour, H Farzaneh… - Applied Sciences …, 2021 - kyushu-u.elsevierpure.com
In this paper, a comprehensive demand response model for the residential sector in the
Jordanian electricity market is introduced, considering the interaction between the power …
Jordanian electricity market is introduced, considering the interaction between the power …
Day-Ahead Residential Electricity Demand Response Model Based on Deep Neural Networks for Peak Demand Reduction in the Jordanian Power Sector
A Shaqour, H Farzaneh, H Almogdady - Applied Sciences, 2021 - search.proquest.com
In this paper, a comprehensive demand response model for the residential sector in the
Jordanian electricity market is introduced, considering the interaction between the power …
Jordanian electricity market is introduced, considering the interaction between the power …
[PDF][PDF] Day-Ahead Residential Electricity Demand Response Model Based on Deep Neural Networks for Peak Demand Reduction in the Jordanian Power Sector
A Shaqour, H Farzaneh, H Almogdady - Appl. Sci, 2021 - researchgate.net
Day-Ahead Residential Electricity Demand Response Model Based on Deep Neural Networks for
Peak Demand Reduction in the Jordanian Page 1 Appl. Sci. 2021, 11, 6626 …
Peak Demand Reduction in the Jordanian Page 1 Appl. Sci. 2021, 11, 6626 …
[PDF][PDF] Day-Ahead Residential Electricity Demand Response Model Based on Deep Neural Networks for Peak Demand Reduction in the Jordanian Power Sector
MA Shaqour - 2021 - catalog.lib.kyushu-u.ac.jp
Due to Jordan's challenges, its energy sector, from high dependency on importing around
97% of its energy resources, expensive fuels as well as a rapid increase in the integration of …
97% of its energy resources, expensive fuels as well as a rapid increase in the integration of …
Day-Ahead Residential Electricity Demand Response Model Based on Deep Neural Networks for Peak Demand Reduction in the Jordanian Power Sector.
A Shaqour, H Farzaneh… - Applied Sciences (2076 …, 2021 - search.ebscohost.com
Abstract Featured Application: The presented model can be used as a baseline model for
the implementation of peak demand response systems in Jordan. The day-ahead prediction …
the implementation of peak demand response systems in Jordan. The day-ahead prediction …
[PDF][PDF] Day-Ahead Residential Electricity Demand Response Model Based on Deep Neural Networks for Peak Demand Reduction in the Jordanian Power Sector
AM Shaqour, H Farzaneh - Energy, 2021 - api.lib.kyushu-u.ac.jp
Due to Jordan's challenges, its energy sector, from high dependency on importing around
97% of its energy resources, expensive fuels as well as a rapid increase in the integration of …
97% of its energy resources, expensive fuels as well as a rapid increase in the integration of …
Day-Ahead Residential Electricity Demand Response Model Based on Deep Neural Networks for Peak Demand Reduction in the Jordanian Power Sector
A Shaqour, H Farzaneh, H Almogdady - agris.fao.org
In this paper, a comprehensive demand response model for the residential sector in the
Jordanian electricity market is introduced, considering the interaction between the power …
Jordanian electricity market is introduced, considering the interaction between the power …
[PDF][PDF] Day-Ahead Residential Electricity Demand Response Model Based on Deep Neural Networks for Peak Demand Reduction in the Jordanian Power Sector
AM Shaqour, H Farzaneh - Energy, 2021 - farzaneh-lab.kyushu-u.ac.jp
Due to Jordan's challenges, its energy sector, from high dependency on importing around
97% of its energy resources, expensive fuels as well as a rapid increase in the integration of …
97% of its energy resources, expensive fuels as well as a rapid increase in the integration of …