Particle Swarm Optimization Based Demand Response Using Artificial Neural Network Based Load Prediction

A new optimization method for Demand Response (DR) using Particle Swarm Optimization (PSO) is developed and validated where load prediction is done using Artificial Neural Network (ANN). The electrical load and climatological data of a residential area in Austin, Texas are used as the inputs of the ANN. Then, the outcomes with the day-ahead prices data are used to solve the load shifting and cost reduction problem. The results indicate that, the proposed model demonstrates the capability to optimize demand response and improve accuracy in load prediction, thereby reducing the cost and peak load.