- Isaac Newton -

• SEE 2003: Introduction to Energy and Environmental Data Analysis

The course will provide students with the knowledge of using statistical methods in energy and environmental science. Analysis methods, such as probability, random variable (discrete & continuous), parameter estimation, confidence internal and hypothesis testing, inferences involving one and two populations, simple linear regression, analysis of variance and goodness-of-fit test, are very helpful for students to understand the physical processes occurring in the environment, and to work on climate prediction. Students are required to use the knowledge learnt from this course to analyse the data with computational tools, such as Python. Overall, students would gain the understanding of statistical methods in energy and environmental science and they would be capable to analyse the data using statistical methods. The course topics include:

1. The role of statistics and the data analysis process

2. Numerical method of describing data

3. Probability

4. Population distributions

5. Random variable (discrete & continuous)

6. Hypothesis testing and confidence interval

7. Inferences involving one population (e.g. t-distribution, chi-square distribution, etc.)

8. Inferences involving two populations (e.g. comparison of two populations, f-distribution)

9. Simple linear regression

10. Analysis of variance

11. Goodness-of-fit test

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• SEE 4118: Wind and Marine Energy

Wind and marine energy are two of the most important types of renewable energy. This course introduces the basic science and engineering behind systems that convert wind, wave and tide into usable energy. Advanced fluid dynamics and aerodynamics are introduced to understand the working principle of wind and marine energy systems. The outcome is to furnish students with the skills to evaluate the performance of wind and marine energy systems. Topics include resource availability and characteristics, working principle of wind and marine energy systems, aerodynamics and fluid dynamics for energy systems, design consideration and environmental impact. Computational labs will expose students to the design of wind and marine energy systems via computational fluid dynamics. The course topics include:​

1. Wind energy

2. Marine energy

3. Marine fluid dynamics

4. Wind turbines

5. Fundamentals of aerodynamics

6. Computational fluid dynamic

7. Environmental aspects of wind and marine energy systems

• SEE 6116/8116: Building Performance Assessment

This course aims to provide students with basic knowledge of the design construction and operation of low energy and green buildings. The outcome is to furnish students with the skills to assess if a particular building is fulfilling its design targets and aspirations. Topics include building energy, building science, indoor air quality, thermal comfort in buildings, international trends in building performance evaluation techniques, building energy simulation, building energy audit, retro-commissioning, and net-zero energy buildings. The course topics include:

1. Building energy and science

2. Ventilation theory

3. Indoor air quality and infectious disease transmission

4. Thermal comfort in buildings

5. Green building certification

6. Building energy audit and retro-commissioning

7. Building energy simulation (EnergyPlus)

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• SEE 5211/8212: Data Analysis in Environmental Applications

The course is designed for beginning postgraduate students. The course will provide students with knowledge in understanding and using statistical methods in environmental science and applications. Probability distributions, parametric tests of significance against non-parametric tests, Monte Carlo methods, spatial and time series data analysis, Principal Component Analysis, and correlation method etc. will be taught in the course facilitated by extensive use of real world problems as example. The students will be able to apply these methods in various environmental applications and learn to interpret the data to solve environmental problems. The course topics include:

1. Probability distributions 

2. Tests of hypothesis 

3. Regression analysis

4. Spatial and time series data analysis 

5. Principal component analysis