Forecasting Electricity Demand in Khyber Pakhtunkhwa with Gradient-Boosted Trees

Abstract

A reproducible forecasting pipeline using LightGBM and weather covariates achieves a 6.8% MAPE on hourly demand ΓÇö a practical case study used in our Data Science curriculum.

Methodology

The study follows a reproducible pipeline with publicly documented datasets and open-source tooling. Full experimental details, code and evaluation notebooks are available on request from the authors.

Key Findings

• Practical, low-cost approaches can match or exceed commercial baselines in the local context.
• Reproducibility and documentation significantly improve adoption by practitioners.
• Results are integrated into the ITA Academia curriculum as case studies.