Autoregressive Load Forecasting Using Genetic Algorithm

This article presents a load forecasting methodology employing genetic algorithms (GA). Genetic algorithms are evolutionary computation techniques that optimize solutions through simulated natural selection processes. The implementation takes seven consecutive days of historical load data as input features to predict the subsequent day's load demand. This approach finds applications across various domains including power system management and transportation optimization to enhance operational efficiency. Key implementation aspects include: - Chromosome encoding of load patterns as solution candidates - Fitness function design based on prediction accuracy metrics - Selection, crossover, and mutation operators for population evolution - Termination criteria configuration for convergence optimization Successful implementation requires comprehensive understanding of genetic algorithm principles and load forecasting fundamentals, particularly regarding feature engineering and hyperparameter tuning for temporal data processing.