A simple maths model beat neural networks at predicting server load. Most autoscaling waits until your servers are already struggling. It adds machines after the slowdown has started. So teams keep extra machines running all day, just in case. That spare capacity uses power around the clock. The better way is to scale before the busy moment arrives. And you don't need a heavy AI model to see it coming. GreenCode researchers found that a simple maths model predicted load better than neural networks. They compared twenty-nine ways to forecast, on real traffic from working systems. The most accurate was a simple method called exponential smoothing. It beat the big neural network models. Here is the fix you can use today. Forecast processor load from the number of requests you get. Try a simple model like exponential smoothing first. Scale up ahead of the peaks you predict, using tools like KEDA. Then shrink the spare machines you keep running all day. Check each Kubernetes pod's energy afterwards, using tools like Kepler. GreenCode helps tackle this by mapping your running systems and the energy they use. It shows services running hot, and services barely running at all. That was GreenCode Tech Tip number five. Follow for the next tip, and find more on greencode.ai/tech-tips.