Smart Energy and the Built Environment
| Code | School | Level | Credits | Semesters |
| ABEE2046 | Architecture and Built Environment | 2 | 20 | Spring UK |
- Code
- ABEE2046
- School
- Architecture and Built Environment
- Level
- 2
- Credits
- 20
- Semesters
- Spring UK
Summary
This module will introduce students to smart energy systems, which exploit synergies across multiple sub-systems such as electricity, heating, and transport to optimise energy use and reduce carbon intensity. The importance of data science to enable increased efficiency at the building and systems level will also be highlighted. You will be introduced to renewable energy technologies and investigate their performance in hands-on lab sessions. You will also build a model of a smart energy system in the context of a real-life case study.
Target Students
Year 2 BEng/ MEng Architectural Environment Engineering students; U6UAAEENG; U6UAEENGY; U7UAEENG; U7UAEENGY; (K240, K24B, K241, K24A)
Assessment
- 30% Coursework 1: Laboratory reportsA 2000 word laboratory report covering both lab sessions
- 10% Coursework 2: Continuous assessment
- 60% Coursework 3: A report and presentation on the design and simulation of a smart energy system
Assessed by end of spring semester
Educational Aims
This module aims to provide students with a foundation in smart energy systems for the built environment and to demonstrate their role in contributing to net-zero objectives in the context of the broader energy system.Learning Outcomes
This module aims to enable students to:
• Understand the impact of the built environment on global energy use.
• Understand the characteristics of smart energy and its role in helping deliver net-zero carbon objectives.
• Understand the role of data science in smart energy systems.
• Evaluate the performance of smart energy components through practical investigation.
• Design and develop a smart energy system utilising appropriate hardware and software tools.
This module contributes to the delivery of the following Engineering Council outcomes:
M1 Apply a comprehensive knowledge of mathematics, statistics, natural science and engineering principles to
the solution of complex problems. Much of the knowledge will be at the forefront of the particular subject of
study and informed by a critical awareness of new developments and the wider context of engineering
M3 Select and apply appropriate computational and analytical techniques to model complex problems, discussing
the limitations of the techniques employed
M4 Select and critically evaluate technical literature and other sources of information to solve complex problems
M6 Apply an integrated or systems approach to the solution of complex problems
M7 Evaluate the environmental and societal impact of solutions to complex problems (to include the entire life
cycle of a product or process) and minimise adverse impacts
M8 Identify and analyse ethical concerns and make reasoned ethical choices informed by professional codes of
conduct
M10 Adopt a holistic and proportionate approach to the mitigation of security risks
M12 Use practical laboratory and workshop skills to investigate complex problems
M13 Select and apply appropriate materials, equipment, engineering technologies and processes, recognising
their limitations
M16 Function effectively as an individual, and as a member or leader of a team. Evaluate effectiveness of own and
team performance