Particle Swarm Optimization Algorithm Code for Electric Load Forecasting

This paper proposes a novel methodology employing Particle Swarm Optimization (PSO) algorithm for load forecasting in power systems. The approach develops a forecasting model based on historical load data and meteorological information. By integrating PSO algorithm into the model framework, more accurate load forecasting results can be achieved. The implementation typically involves initializing particles representing potential solutions, where each particle's position corresponds to forecast parameters. The algorithm iteratively updates particle velocities and positions using fitness evaluation based on prediction error minimization. Key functions include velocity calculation incorporating cognitive and social components, position updates, and fitness evaluation using mean squared error metrics. This methodology assists power systems in enhanced load management, thereby improving system stability and reliability through optimized generation scheduling and resource allocation.