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

TL;DR

This paper proposes a PSO-based demand response model that uses ANN for load prediction, aiming to reduce costs and peak loads in residential areas by optimizing load shifting based on predicted data.

Contribution

It introduces a novel combination of PSO and ANN for demand response, specifically for load prediction and cost reduction in residential energy management.

Findings

Reduced payment costs in residential demand response

Lowered peak load through load shifting

Effective load prediction with ANN

Abstract

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.