Particle Swarm Optimization Based Demand Response Using Artificial Neural Network Based Load Prediction
This addresses electricity cost and grid stability for residential consumers, but is incremental as it applies existing methods to a specific dataset.
The study tackled the problem of reducing electricity costs and peak load in residential demand response by proposing a model combining Particle Swarm Optimization with Artificial Neural Network-based load prediction, achieving decreased payment costs and peak load.
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.