Profile
- A creative researcher and fast learner expert in building and applying data-driven models
- Passionate about data analysis, renewable energy, information technology and finance
- Keen on coding, cooking, and travelling
- Bilingual in Mandarin and English
Data analyst
Specialised in regression, clustering and classification, correlation, Bayesian learning, and time series
Researcher
Final year PhD student in Engineering (Energy Systems) at University of Edinburgh
Developer
LinkedIn Assessments certificated Python, Matlab, C++, HTML, R and Django skills
Education
PhD Engineering (Energy Systems)
October 2017 - Present, University of Edinburgh
Sponsor: Fully funded by School of Engineering, University of Edinburgh
Dissertation: Evaluation of Uncertainties for Improved Reliability of Smart Grids
Developed technical skills: High-dimension nonlinear correlation (Vine Copula and mixture models), Bayesian deep learning, regression (GBRT, LSTM), clustering (DBSCAN, LOF), optimisation (PSO, GA, Bayesian optimisation), anomaly detection, spectral analysis, high-dimension Markov chain, Monte Carlo
BEng (Hons) Electronics and Electrical Engineering
September 2015 - July 2017, University of Edinburgh
Dissertation: Smart Grid Functionalities for Improving Reliability and Resilience of the UK/Scottish Low-Voltage Networks
Grade: First Class Honours (Obtained)
BEng Electrical Engineering and Automation
September 2013 - June 2015, North China Electric Power University
GPA: 87.8% (Obtained, equivalent to UK First Class)
IT Knowledge
Python (Libraries: Pandas, SciPy, Scikit-learn, Tensorflow, Edward2, PyTorch, Pyro, Django. Techniques: functional programming, OOP, metaprogramming)
Matlab
C++
R
HTML
Photoshop
Amazon Web Services
WORK EXPERIENCE
Laboratory Demonstrator and Marker
Part-time during the study
January 2018 - January 2021, University of Edinburgh, Edinburgh, UK
- Present and explain weekly laboratory material to students, and take part in the marking process
- Initiate to modify, improve and update the existing teaching materials and marking schemes, benefiting both the students and the demonstrators/markers team
- Manage the training of newly-joint demonstrators, and organise demonstrators/markers board meetings to work out rotas and arrange marking tasks
I receive very good verbal feedback from the students. And our updated marking schemes and my organised board meetings have improved the whole team’s efficiency, allowing us to mark more accurately and easily and send the assignment scripts back to students typically 3 days earlier
Academic Department Minister
Part-time volunteering during the study
January 2018 - March 2018, Doctorate Association, Edinburgh, UK
- Lead Academic Department (team of 6) to organise regular interdisciplinary academic symposiums
- Cooperate with other departments, such as Outreach Department and Publicity Department to advertise our activities through various social media and expand our influence
I hold efficient meetings regularly within my department, encouraging everyone to work as a team and make contributions. I evaluate every member’s personal strength and potential so I can guide them and assign different but suitable tasks, and I am also good at learning from the past. We have been delivering higher quality of academic symposiums with an increasing number of attendances
Big Data Analyst
Intern
June 2018 - July 2018, Artificial Intelligence Research Centre, China Electric Power Research Institute, Beijing, China
Project: A Research on AI Technology for Power Dispatching and Control Service based on Data-Mining methods
- Learned the new data-mining theory quickly, e.g., deep LSTM using TensorFlow, and reviewed relevant literature and identified points needed to be improved
- Published two papers based on the work I did during the internship
I enjoyed spending my annual holiday doing this internship. Both my co-workers and senior managers were very satisfied and surprised by my fast-learning, critical thinking and collaboration skills, and they were also very happy with my published papers
Sales
Intern
June 2016 - September 2016, Gold Brothers, Edinburgh, UK
- Collaborated with a sales team of 4 with different cultural backgrounds for scarf selling. Serving the customers from all around the world had improved my English and communication skills significantly
- Helped check goods delivery, classify the inventory, and do day/weekly cash-up
I tried to persuade and help the manager to re-design and re-order the labels and items, which helped the customers to find the most popular items and increase their sales. And I also won the highest commission among the sales team because of my excellent performance in customer service.
OTHER ACTIVITIES
• Enjoy doing workouts regularly and other sports, e.g., finisher for 2018 Edinburgh Half Marathon
• Hold the UK full driving licence
Publications
- M. Zou and S. Z. Djokic, "A Review of Approaches for the Detection and Treatment of Outliers in Processing Wind Turbine and Wind Farm Measurements," Energies, vol. 13, no. 16, p. 4228, 2020.
- D. Fang et al., "Deterministic and Probabilistic Assessment of Distribution Network Hosting Capacity for Wind-Based Renewable Generation," in 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 2020.
- D. Fang, M. Zou, G. Harrison, J. Gunda, S. Djokic, and A. Vaccaro, "Multi-Stage Congestion Management Considering Maximum Lead Time and Voltage-Dependent Load Models," in 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), 2020.
- V. Di Giorgio, R. Langella, A. Testa, S. Djokic, and M. Zou, "First Order Non-homogeneous Markov Chain Model for Generation of Wind Speed and Direction Synthetic Time Series ," in 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 2020.
- M. Zou, D. Fang, S. Z. Djokic, V. Di Giorgio, R. Langella, and A. Testa, "Evaluation of Wind Turbine Power Outputs with and without Uncertainties in Input Wind Speed and Wind Direction Data," IET Renewable Power Generation, 2020.
- M. Zou et al., "Comparison of Three Methods for a Weather Based Day-Ahead Load Forecasting," in 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), Bucharest, Romania, 2019, pp. 1-5.
- M. Zou, D. Fang, G. Harrison, and S. Djokic, "Weather Based Day-Ahead and Week-Ahead Load Forecasting using Deep Recurrent Neural Network," in 2019 IEEE 5th International forum on Research and Technology for Society and Industry (RTSI), Florence, Italy, 2019, pp. 341-346.
- D. Fang, J. Gunda, M. Zou, G. Harrison, S. Z. Djokic, and A. Vaccaro, "Dynamic Thermal Rating for Efficient Management of Post-Contingency Congestions," in 2019 IEEE Milan PowerTech, 2019, pp. 1-6.
- M. Zou, D. Fang, and S. Djokic, "Equivalent power curves and short-term forecasting of power outputs of an off-shore wind farm based on a multi-state operational model," in 4th International Conference on Offshore Renewable Energy (CORE), August, 2019.
- S. Z. Djokic, M. Zou, D. Fang, V. D. Giorgio, R. Langella, and A. Testa, "On the Importance of Correlating Wind Speed and Wind Direction for Evaluating Uncertainty in Wind Turbine Power Output," in 2019 International Conference on Clean Electrical Power (ICCEP), 2019, pp.36-45.
- D. Fang, M. Zou, G. Coletta, A. Vaccaro, and S. Z. Djokic, “Handling uncertainties with affine arithmetic and probabilistic OPF for increased utilisation of overhead transmission lines,” Electric Power Systems Research, vol. 170, pp. 364-377, 2019.
- M. Zou, D. Fang, and S. Djokic, "Assessment of wind energy resources and identification of outliers in on-shore and off-shore wind farm measurements," in 3rd International Conference on Offshore Renewable Energy (CORE), August, 2018.
- D. Fang, M. Zou, and S. Djokic, "Probabilistic OPF Incorporating Uncertainties in Wind Power Outputs and Line Thermal Ratings," in 2018 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), June, 2018.
Contact
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