Skip to Main Content
Catalogue Link
<a href="/ords/r/curriculum/catalogue/home" class="t-Header-logo-link"><span class="apex-logo-text">Handbook</span></a>
Hidden Page Items
New
7
New
HE Unit Template
New
Electrical Demand Forecast and Management
New
ENGIN5103 - Electrical Demand Forecast and Management
New
20240200
New
New
New
New
Course
New
Unit
New
New
1.1
New
New
2026-09-10
New
2026-09-10
New
Engineering
New
New
New
New
Institute of Innovation, Science & Sustainability
New
Engineering
New
Postgraduate - Coursework
New
15
New
New
.125
New
031301
New
Electrical Engineering
New
031301 - Electrical Engineering
New
<a href="http://www.federation.edu.au/locate/unit/engin5103">View unit fees</a>
New
Graded (e.g. HD, D, C, etc)
New
9
New
Masters Degree
New
New
New
New
New
0
New
New
New
New
New
false
New
New
New
<p>This unit provides in-depth knowledge and understanding of electrical demand forecasting and management, which includes an overview of demand flexibility and different short and long-term forecasting models. You will be exposed to various prediction tools for aggregated response and the applicability of intelligent forecasting models.</p>
New
New
<p>•Overview of demand flexibility<br>
•Static and dynamic state estimation<br>
•Short and long term forecasting models<br>
•Machine learning and AI in generating forecasting models<br>
•Prediction tools for aggregated response<br>
•Tools for customer side load and battery management<br>
•Options for automated response, market based vs sign-up contract<br>
•Exposure of customer willingness, utility command and decay of manual response</p>
New
false
New
New
true
Hidden Page Items
Semester
Semester
0200
Year
Year
2027
Current Year
2026
Breadcrumb Positioning
Handbook
ENGIN5103 - Electrical Demand Forecast and Management
Pseudo Breadcrumb
ENGIN5103 - Electrical Demand Forecast and Management
Breadcrumb Type
Unit
Catalogue Term and Year
Version Text
You are viewing a version for
Semester
Semester 1
Semester 2
Summer Semester
Year
2027
2026
Download PDF
RDS Holder
Overview
Key information
Requisites
Learning outcomes
Assessment
Parent Region
Overview
Unit description
<p>This unit provides in-depth knowledge and understanding of electrical demand forecasting and management, which includes an overview of demand flexibility and different short and long-term forecasting models. You will be exposed to various prediction tools for aggregated response and the applicability of intelligent forecasting models.</p>
Unit content
<p>•Overview of demand flexibility<br> •Static and dynamic state estimation<br> •Short and long term forecasting models<br> •Machine learning and AI in generating forecasting models<br> •Prediction tools for aggregated response<br> •Tools for customer side load and battery management<br> •Options for automated response, market based vs sign-up contract<br> •Exposure of customer willingness, utility command and decay of manual response</p>
Key information
Institute / school:
Institute of Innovation, Science & Sustainability
Discipline:
Engineering
Study level:
Postgraduate - Coursework
Credit points:
15
EFTSL:
0.125
Field of education:
031301 - Electrical Engineering
Grade scheme:
Graded (e.g. HD, D, C, etc)
Placement component:
No
Fee information:
<a href="http://www.federation.edu.au/locate/unit/engin5103">View unit fees</a>
Requisites
Prerequisite
<p>must have completed "ENGIN3102"</p>
Corequisite
None
Exclusion
None
Learning outcomes
Learning outcomes
<p>On successful completion of this unit, students are expected to gain the following [K]nowledge, [S]kills and [A]pplication of knowledge & skills</p>
Learning Outcomes
Ref
Outcome
K1
<p>Recognize the key components in static and dynamic forecasting models and appraise the difference between them.</p>
K2
<p>Differentiate between various state estimation techniques for demand forecasting.</p>
K3
<p>Identify appropriate tools for demand management and aggregated response.</p>
S1
<p>Synthesize load forecasting models for both static and dynamic states with given specifications and performance parameters.</p>
S2
<p>Appraise innovative forecasting models using different AI and machine learning methodologies.</p>
S3
<p>Evaluate and assess solutions to challenges associated with electrical demand forecasting and mangagement.</p>
A1
<p>Apply industry-standard software analysis tools to simulate and study electrical demand and load forecasting.</p>
A2
<p>Interpret results from different predictive tools applied to electrical demand forecasting and management.</p>
A3
<p>Investigate the behavioural changes to load and demand in devising predictive and management models.</p>
Assessment
Learning Tasks and Assessment
Supplementary assessment available:
No
Key information
Institute / school:
Institute of Innovation, Science & Sustainability
Discipline:
Engineering
Study level:
Postgraduate - Coursework
Credit points:
15
EFTSL:
0.125
Field of education:
031301 - Electrical Engineering
Grade scheme:
Graded (e.g. HD, D, C, etc)
Placement component:
No
Fee information:
<a href="http://www.federation.edu.au/locate/unit/engin5103">View unit fees</a>
Additional information
Add Info Static Text
Internal reference: ENGIN5103 - U1.1