  In the present study, a Particle Swarm Optimization (PSO) based Demand
Response (DR) model, using Artificial Neural Network (ANN) to predict load is
proposed. The electrical load and climatological data of a residential area in
Austin city in 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. According to the results, the proposed model has the ability
to decrease payment costs and peak load.
