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10 contributions to Energy Data Scientist
Senior Power System Protection Role
Good day everyone, I’ve been invited for an initial virtual screening for the above job role at EirGrid in Ireland. Anyone has an idea what they are likely to ask please? Thank you
2 likes • 29d
So, since a recruiter reached out, they're already interested in you! I think the chances are very high they will offer interviews later on!
Machine Learning for Energy Economics - books
I recommend and I am sharing the full PDF of 5 books about ML for energy below.
Switching to gas reduces emissions in short term but increases in long run
Sharing the key points of a Financial Times article below. Link to the original article: https://www.ft.com/content/e969af59-0b97-4b21-942d-704bdbca667e Natural gas has been considered a bridge fuel in the fight against climate change because it produces about half the carbon emissions of coal when burned for energy. Many countries, including those in Europe, have planned to increase their use of gas with the idea that it will help reduce emissions while we build up renewable energy sources like wind and solar. This logic seems straightforward: if we need energy now and can't instantly switch everything to renewables, using gas instead of coal should at least cut our emissions in half in the meantime. However, research reveals what experts call "the gas trap," which shows this strategy backfires in the long run. When countries increase gas production to replace coal, it drives down overall energy prices, which makes investing in renewable energy less attractive and profitable. So while gas does reduce emissions in the short term by pushing out coal, it actually leads to higher total emissions over time because it discourages the renewable energy investments we need. The study found that if a country like Norway could commit in advance to producing less gas, it would reduce gas production by 10 % to encourage renewable investment. But without that commitment, countries end up increasing gas production by about 9 % instead, ultimately replacing renewables rather than coal. Norway is primarily a gas producer and exporter - it sells natural gas to other European countries. The article is about how much gas Norway (and other producers) choose to produce and export, and how that affects what happens in the countries that use that gas. Norway's role: Produces natural gas and exports it to Europe. Europe's role: Buys and uses that gas (burning it for electricity and heating), choosing between gas, coal, and renewables.
Switching to gas reduces emissions in short term but increases in long run
0 likes • 29d
@Lanre Adefolahan Yes!
Flowchart for the IF statement and for the WHILE loop
This is a very basic interview question but many get stuck. Question: Draw the FLOWCHART for the IF statement , and for the WHILE loop. Answer: The flowcharts are attached. The if statement is on the left of the picture. So, it checks a condition once and decides whether to do the action of not. If the condition is true, it executes the action and then ends. If the condition is false, it skips the action and goes straight to the end. This means the action either happens once or not at all. The while loop is on the right of the image. It checks a condition. If True, then it does the action. If condition was true then It loops back to check the condition again instead of ending. This creates a cycle where the action keeps getting performed repeatedly as long as the condition remains true. Only when the condition finally becomes false does the program exit the loop.
Flowchart for the IF statement and for the WHILE loop
1 like • Oct 10
thank you
Paper on Green AI
In 6.1, in Classroom you can find a paper that discusses the growing energy consumption problem in AI (Artificial Intelligence) systems. The paper proposes an optimization model that will significantly reduce energy usage while maintaining high AI model performance. This is actually a key challenge in AI - energy: if we reduce energy consumption, will the performance reduce? we don't want that. The key challenge is that as AI becomes more prevalent in enterprise systems for automation and decision-making, the computational demands lead to substantial energy costs and environmental concerns. So, the paper discusses energy-efficient optimization algorithms that work during both the training and inference phases of AI models, using a multi-objective optimization approach to balance energy reduction against performance requirements. Feel free to download it , in the Classroom.
0 likes • Oct 10
Very good! AI is a green enabler
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Sipho Dlamini
3
30points to level up
@sipho-dlamini-8526
Smart Grid Economics

Active 11h ago
Joined Sep 23, 2025
ENFP
South Africa