Energy Demand Forecasting

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.