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Grant Award View - GA54675-V1
Dynamic Deep Learning for Electricity Demand Forecasting
GA ID:
GA54675-V1
Agency:
Australian Research Council
Approval Date:
5-Jul-2019
Variation Publish Date:
7-Apr-2020
Variation Date:
13-Apr-2020
Category:
Science, Technology, Engineering and Mathematics (STEM) Research
Grant Term:
13-Apr-2020 to 12-Apr-2023
Value (AUD):
$321,000.00
(GST inclusive where applicable)
Varies:
GA54675
- Dynamic Deep Learning for Electricity Demand Forecasting
One-off/Ad hoc:
No
Aggregate Grant Award:
No
PBS Program Name:
ARC 18/19 Linkage
Grant Program:
Linkage Projects
Grant Activity:
Dynamic Deep Learning for Electricity Demand Forecasting
Purpose:
This project aims at developing a deep learning technology for high resolution electricity demand forecasting and residential demand response modelling. Electricity consumption data are dynamic and highly uncertain. The deep learning technology expects to provide accurate demand forecasting, and thus enabling optimal use of existing
grid assets and guiding future investments. The expected outcome can support data-driven decision-making in Australia's electricity distribution network planning and operation by considering future challenges such as integrating battery storage and electric vehicles into the grid, and thus providing reliable energy. The project expects to train next generation expert workforce for Australia's future power grid.
GO ID:
GO Title:
Linkage Projects for funding applied for in 2018
Internal Reference ID:
LP18 Round 1
Selection Process:
Targeted or Restricted Competitive
Confidentiality - Contract:
No
Confidentiality - Outputs:
No
Grant Recipient Details
Recipient Name:
RMIT University
Recipient ABN:
49 781 030 034
Grant Recipient Location
Suburb:
MELBOURNE
Town/City:
MELBOURNE
Postcode:
3000
State/Territory:
VIC
Country:
AUSTRALIA
Grant Delivery Location
State/Territory:
VIC
Postcode:
3000
Country:
AUSTRALIA