If you’ve worked in the energy industry for more than five minutes, you’ve probably seen this picture:
It shows the relationship between Real Power, Reactive Power, and Apparent Power — the classic power triangle.
The formula is simple:
$$ kVAR^2+kW^2={kVA}^2 $$
No big deal… until you try to compute it millions of times.
In Australia, older meters record 288 intervals per day.
Newer meters running on the 5MS schema jump to 1440 intervals.
Now imagine you’re an electricity retailer with hundreds of thousands of NMIs.
Suddenly that cute little triangle turns into a CPU-melting monster.
Languages built around linear algebra — like Julia, MATLAB, and NumPy — are designed for exactly this type of workload. They let you write math that looks like math, while still running at near–native (C-level) performance.
As a recovering maths student, I absolutely love Julia’s LaTeX-style syntax:
kVA = √(kW^2 + kVAR^2)
It feels like cheating—but the good kind.
You also get to join a vibrant open-source community instead of wrestling with legacy systems or, in my case, stored procedures that should have been retired when the iPhone 4 was still shiny.