Energy Demand Forecasting
A time-series forecasting system for energy demand, built for planning and load-balancing use cases.
Problem
Utilities need accurate short-term demand forecasts to balance load and avoid costly over- or under-generation.
Solution
A time-series forecasting model over historical consumption data, validated with proper time-based splits, projecting demand to support planning decisions.
Overview
Utilities that under-forecast demand risk brownouts; over-forecasting wastes costly generation capacity. Both failure modes are expensive, which makes forecast accuracy directly financially meaningful.
How it works
A time-series model is trained on historical energy consumption, capturing daily and seasonal demand cycles. Critically, it’s validated with proper time-based train/test splits (never testing on data that’s chronologically “before” the training window), which is the single most common mistake that makes time-series models look better in testing than they perform in reality.
Result
A demand forecast that planning teams can trust precisely because its accuracy claims are honest about the time-ordering of the data.
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