AI-Based Optimization of Solar Energy Systems in Pakistan: Enhancing Efficiency and Predictive Performance
DOI:
https://doi.org/10.66021/Keywords:
AI Optimization, Solar Energy Pakistan, LSTM Forecasting, Reinforcement Learning, Grid Stability, Predictive Maintenance, Curtailment Reduction, LCOE, Quaid e Azam Solar Park, Renewable Energy Transition.Abstract
Pakistan's solar energy sector faces critical challenges from generation intermittency, grid instability and high curtailment rates limiting renewable penetration below 5% despite abundant irradiation. This study investigates AI driven optimization combining LSTM forecasting, reinforcement learning dispatch and autoencoder fault detection to enhance power distribution, stability and predictive performance in large scale solar farms.
Hybrid simulations using PMD meteorological data and NTDC grid models demonstrate 21.4% yield uplift, 60% curtailment reduction, 17.3% transmission loss cuts and LCOE decline to 3.8 ¢/kWh. Forecasting achieves 4.2% MAE maintaining 50 Hz frequency 99.2% of time across monsoon variability and dust accumulation scenarios.
Applied to Quaid e Azam Solar Park AI unlocks 214 GWh extra annual output positioning solar as baseload viable. Economic payback shortens to 6.1 years with 34% NPV gains. Findings offer policymakers deployable frameworks for 30% renewable targets, bridging urban rural energy equity while advancing NDCs through scalable, interpretable AI system
