A probabilistic graphical model built to analyze, predict, and diagnose electricity load shedding in Pakistan. Developed as Assignment #3 for the Introduction to Artificial Intelligence course at the Institute of Business Administration (IBA), Karachi — Spring 2026.
Load shedding is one of Pakistan's most persistent infrastructure challenges. This project models the causal relationships behind power outages using a Bayesian Network (BN) with 11 variables — from fuel prices and circular debt to grid infrastructure and monsoon rainfall.
The model was built in GeNIe Modeler Academic Edition and can answer questions like:
- How bad does load shedding get when fuel prices spike and the government can't pay its bills?
- Does fixing the power grid help if there is still a fuel shortage?
- Given a severe outage, which cause was most likely responsible?
| ID | Variable | States |
|---|---|---|
| V1 | Fuel Price | Low / High |
| V2 | Govt Payment Capacity | Low / Adequate |
| V3 | Fuel Supply | Insufficient / Sufficient |
| V4 | Rainfall Level | Low / High |
| V5 | Hydro Generation | Low / High |
| V6 | Thermal Generation | Low / High |
| V7 | Grid Infrastructure | Poor / Good |
| V8 | Electricity Demand | Low / High |
| V9 | Total Generation | Low / Adequate / High |
| V10 | Transmission Loss | Low / High |
| V11 | Load Shedding Severity (outcome) | Low / Moderate / High |
The DAG has 12 directed edges capturing key causal chains:
- Fuel Price → Govt Payment → Fuel Supply → Thermal Generation
- Rainfall → Hydro Generation
- Thermal + Hydro → Total Generation
- Grid Infrastructure → Transmission Loss
- Total Generation + Demand + Transmission Loss + Grid → Load Shedding Severity
Simulates Pakistan's 2022–23 situation: high fuel prices, circular debt, peak summer demand, poor grid.
- Result: Probability of high-severity load shedding jumps from 44% → 66%
Tests whether hydropower boost + grid investment can offset a fuel problem.
- Result: High-severity load shedding drops from 66% → 34%
Given that a severe outage is observed, which cause was most likely?
- Result: High electricity demand (Bayes factor 1.22×) and poor grid infrastructure (1.20×) are the top indicators
| File | Description |
|---|---|
Load_Shedding_Severity_in_Pakistan.pdf |
Full report with variables, CPTs, DAG, and use case analysis |
load_shedding_bn.xdsl |
GeNIe Modeler network file — open to run inference |
Assignment_3_-_S26.pdf |
Original assignment specification |
- GeNIe Modeler Academic Edition — Bayesian Network construction and inference
- NEPRA. (2023). State of Industry Report 2022–2023. Government of Pakistan. https://www.nepra.org.pk
- World Bank. (2022). Pakistan Power Sector: Circular Debt and Reform Options. https://www.worldbank.org
- WAPDA. (2022). Annual Report 2021–2022: Hydropower Generation Statistics. https://www.wapda.gov.pk
- Koller, D., & Friedman, N. (2009). Probabilistic Graphical Models. MIT Press.
Course: Introduction to Artificial Intelligence
Instructor: Dr. Syed Ali Raza
Institution: Institute of Business Administration, Karachi
Semester: Spring 2026