Learning outcome
1.1

1.1 Comprehensive, theory based understanding of the underpinning natural and physical sciences and the engineering fundamentals applicable to the engineering discipline.
1.2

1.2 Conceptual understanding of the, mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.
1.3

1.3 In-depth understanding of specialist bodies of knowledge within the engineering discipline.
1.4

1.4 Discernment of knowledge development and research directions within the engineering discipline.
1.5

1.5 Knowledge of contextual factors impacting the engineering discipline.
1.6

1.6 Understanding of the scope, principles, norms, accountabilities and bounds of contemporary engineering practice in the specific discipline.
2.1

2.1 Application of established engineering methods to complex engineering problem solving.
2.2

2.2 Fluent application of engineering techniques, tools and resources.
2.3

2.3 Application of systematic engineering synthesis and design processes.
2.4

2.4 Application of systematic approaches to the conduct and management of engineering projects.
3.1

3.1 Ethical conduct and professional accountability.
3.2

3.2 Effective oral and written communication in professional and lay domains.
3.3

3.3 Creative, innovative and pro-active demeanour.
3.4

3.4 Professional use and management of information.
3.5

3.5 Orderly management of self, and professional conduct.
3.6

3.6 Effective team membership and team leadership.
A1

Apply industry-standard software analysis tools to simulate and study electrical demand and load forecasting.
A2

Interpret results from different predictive tools applied to electrical demand forecasting and management.
A3

Investigate the behavioural changes to load and demand in devising predictive and management models.
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.
K3

Identify appropriate tools for demand management and aggregated response.
S1

Synthesize load forecasting models for both static and dynamic states with given specifications and performance parameters.
S2

Appraise innovative forecasting models using different AI and machine learning methodologies.
S3

Evaluate and assess solutions to challenges associated with electrical demand forecasting and mangagement.