| Effective Term: | 2025/05 |
| Institute / School : | Institute of Innovation, Science & Sustainability |
| Unit Title: | Electrical Demand, Forecasting and Management |
| Unit ID: | ENGRG9202 |
| Credit Points: | 15.00 |
| Prerequisite(s): | (ENGRG3201 or ENGRG9205) |
| Co-requisite(s): | Nil |
| Exclusion(s): | (ENGIN5103) |
| 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. Students 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: No |
| Supplementary Assessment: |
| Where supplementary assessment is available a student must have failed overall in the Unit but gained a final mark of 45 per cent or above, has completed all major assessment tasks (including all sub-components where a task has multiple parts) as specified in the Unit Description and is not eligible for any other form of supplementary assessment |
| 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. |
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| 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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| Unit Content: |
Topics may include:
1. Overview of demand flexibility
2. Static and dynamic state estimation
3. Short and long term forecasting models
4. Machine learning and AI in generating forecasting models
5. Prediction tools for aggregated response
6. Tools for customer side load and battery management
7. Options for automated response, market based vs sign-up contract
8. Exposure of customer willingness, utility command and decay of manual response |
| Graduate Attributes: |
| | Learning Outcomes Assessed | Assessment Tasks | Assessment Type | Weighting | | 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 | 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. | Mid-semester Exam | 20% - 40% | | 3. | K3, S1, S2, A1, A2 | Projects to verify students' ability to apply knowledge and skills acquired in the unit. | Team project and presentation | 30% - 50% |
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