MACHINE LEARNING-BASED ENERGY DEMAND FORECASTING FOR SMART GRIDS IN PAKISTAN

Abstract

Accurate energy demand forecasting is critical for Pakistan’s power sector, which faces chronic challenges including circular debt exceeding PKR 2.4 trillion, high-capacity payments for idle generation, aging infrastructure, and a rapid shift toward behind-the-meter solarization that creates “dark demand” and grid instability. Traditional statistical models such as Multiple Linear Regression (MLR) and SARIMA struggle with the non-linear, high-dimensional, and context-specific patterns driven by weather variability, religious holidays (Hijri calendar effects like Ramadan), socio-economic factors, and intermittent renewables. Machine learning approaches, particularly Support Vector Regression (SVR), Long Short-Term Memory (LSTM) networks, Bidirectional LSTM (BiLSTM), and hybrid CNN-BiLSTM architectures, have demonstrated superior performance by capturing complex temporal dependencies and non-linear relationships. Studies using datasets like PRECON show SVR achieving R² of 99% and MAPE as low as 0.1355% for peak demand, while BiLSTM and CNN-BiLSTM hybrids yield MAPE values below 0.56% in regional forecasting for utilities such as LESCO and FESCO. Feature engineering incorporating temperature, humidity, GDP indicators, and dummy variables for religious events further enhances accuracy. Integration with IoT-enabled smart grid infrastructure and probabilistic methods (Monte Carlo simulations) enables real-time load management, better fuel scheduling, and reduced forced load shedding. Despite barriers such as outdated grid infrastructure, regulatory constraints, and data quality issues, ML-driven forecasting offers a pathway to optimize dispatch, minimize financial losses, and support the transition to a flexible, resilient smart grid in Pakistan.