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Optimisation in the Energy Sector and Some Career Advice

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Eugene D.
Optimisation in the Energy Sector and Some Career Advice

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This month we have two very different presentations : one on the use of linear programming in tbe energy sector, and one with some valuable career advice.
Unsolicited career advice - Sachin Abeywardana
Outline: In this talk I will give some controversial thoughts on expanding your career. This talk will not be recorded as I do not want my current and potential future employers seeing this. Treat this as a comedy special more than anything serious.
What every data scientist should understand linear programming - Jack Simpson
Outline: In the energy sector, one of the most powerful optimisation techniques we employ is that of linear and mixed integer programming. Everything from long term planning of the grid through to how we dispatch power stations utilises this approach.
If you have a problem where you are trying to maximise or minimise something (i.e. profit, expenses, time), and have hard constraints the model cannot violate, then this optimisation technique can be incredibly powerful. Many modern industries (airlines, mining, energy, etc) would be unable to function if it were not for linear programming. Linear programming is particularly powerful when you have little or no training data, or the model can no longer generalise into the future based on your historic data.
In this presentation we’ll discuss how we use linear programming to optimise the electrical grid, demonstrate how a linear program works, and give you some tips for how to distinguish between a machine learning and linear programming problem.
Bio - Jack Simpson
Jack Simpson is a director of Endgame Economics, a firm that specialises in providing quantitative advice in the energy sector. Jack has over 10 years’ experience working as a consultant, data scientist, and an academic researcher. He holds a PhD in computational biology from the Australian National University.
Bio: Sachin Abeywardana
Sachin is a recovering Bayesian. Having done his PhD in Bayesian Machine Learning, his current focus is on anything Deep Learning. He is currently a Senior Applied Scientist at Canva and is focusing on getting LLMs into production.

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