| Effective Term: | 2024/05 |
| Institute / School : | Institute of Innovation, Science & Sustainability |
| Unit Title: | Electrical Demand Forecast and Management |
| Unit ID: | ENGIN5103 |
| Credit Points: | 15.00 |
| Prerequisite(s): | (ENGIN3102) |
| Co-requisite(s): | Nil |
| Exclusion(s): | Nil |
| ASCED: | 031301 |
| Other Change: | |
| Brief description of the Unit |
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. |
| Grade Scheme: | Graded (HD, D, C, P, MF, F, XF) |
| Work Experience Indicator: |
| No work experience |
| Placement Component: | |
| Supplementary Assessment:No |
| Supplementary assessment is not available to students who gain a fail in this Unit. |
| Course Level: |
| Level of Unit in Course | AQF Level(s) of Course | | 5 | 6 | 7 | 8 | 9 | 10 | | Introductory | | | | | | | | Intermediate | | | | | | | | Advanced | | | | |  | |
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| Learning Outcomes: |
| Knowledge: |
| K1. | Recognize the key components in static and dynamic forecasting models and appraise the difference between them. |
|
| K2. | Differentiate between various state estimation techniques for demand forecasting. |
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| K3. | Identify appropriate tools for demand management and aggregated response. |
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| Skills: |
| S1. | Synthesize load forecasting models for both static and dynamic states with given specifications and performance parameters. |
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| S2. | Appraise innovative forecasting models using different AI and machine learning methodologies. |
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| S3. | Evaluate and assess solutions to challenges associated with electrical demand forecasting and mangagement. |
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| Application of knowledge and skills: |
| A1. | Apply industry-standard software analysis tools to simulate and study electrical demand and load forecasting. |
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| A2. | Interpret results from different predictive tools applied to electrical demand forecasting and management. |
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| A3. | Investigate the behavioural changes to load and demand in devising predictive and management models. |
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| Other outcomes: |
| Unit Content: |
•Overview of demand flexibility
•Static and dynamic state estimation
•Short and long term forecasting models
•Machine learning and AI in generating forecasting models
•Prediction tools for aggregated response
•Tools for customer side load and battery management
•Options for automated response, market based vs sign-up contract
•Exposure of customer willingness, utility command and decay of manual response |
| Graduate Attributes: |
| | Learning Outcomes Assessed | Assessment Tasks | Assessment Type | Weighting | Professional Standards |
| 1. |
K1, K2, A2, A3 |
Relevant tasks and problems to enforce understanding of the students and help in the gradual development of knowledge and skills throughout the unit. Questions and problems related to the materials covered in the unit. |
Quizzes/Online Test/Assignments |
20% - 30% |
|
| 2. |
S3, A1, A3 |
Relevant tasks and problems to enforce understanding of the students and help in the gradual development of knowledge and skills throughout the unit. |
Workshop/Lab Report/Presentation |
20% - 40% |
|
| 3. |
K3, S1, S2, A1, A2 |
Projects to verify students' ability to apply knowledge and skills acquired in the unit. |
Project Report/Workshop/Presentation |
30% - 50% |
|
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