Minh Nhat Pham made a special achievement: a 10 mark for the graduation thesis Embedded Systems Engineering. On what subject did Minh Nhat graduate?

Minh Nhat: “My thesis was Forecasting of Electric Vehicle Charging Demand with data collected during Covid-19 pandemic. It is a part of the HAN Smart Grid project. The research group at HAN tries to implement a sustainable system which maximises the use of renewable (solar) energy. The EV Charging demand (load) forecast and the Solar energy production forecast are 2 components that are crucial for the algorithm that controls the energy storage (batteries).”

What exactly did you research?

“EV Charging demand forecasting is not a new topic. Several studies about this topic have been done. However, none of them has considered the effect of Covid-19. With vast datasets available, researchers can ignore the Covid-influenced part of the data and conduct experiments using the Covid-free part.”

“At HAN, the data on EV charging demand started to be collected at the end of 2020, in the middle of the pandemic. So instead of waiting for about two more years to have enough Covid-free data to train a forecasting model, I researched a way to use the data currently available. Knowing that the anti-Covid policies strongly affect the charging behaviour at the Arnhem Campus of HAN University, I proposed a method to “grade” the Covid-19 measures in the Netherlands, then I used that information together with the historical charging data, and time-related data to train several forecasting models and chose the best model for deployment.”

What conclusions did you come to?

“I found that amongst 3 models (Long-Short Term Memory, Random Forest, Feed-Forward Neural network), the FFN gave the most accurate predictions with this particular dataset. When being deployed to forecast the real-time charging demand, this model worked very well. Therefore, I see this as a success. I hope that what I have found can open a direction for similar projects with Covid-polluted data, hence avoiding wasting a large amount of data, as well as helping to save time used for data collecting.”

How did you find the support of your lecturer Trung Nguyen?

“Trung, as a member of the research group, played the role of a company coach and a colleague of mine during this project. We discussed the project twice a week. Usually, he listened to what I had done since the latest meeting and told me what he thought about the research direction, my ideas, or the results of experiments. With his knowledge and experience in time series forecasting and machine learning, sometimes Trung gave me useful recommendations to get over problems that I encountered.”

What is your next step in your career?

“My long-term plan is to pursue a master’s degree in Machine Learning. However, currently, I am looking for a job as an Embedded System Engineer to gain some working experience and save money for my education later.”