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

arXiv: https://arxiv.org/abs/2204.13990 · HTML: https://mlsanity.com/paper/2204.13990/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 27 references that are in the map.
- First posted on arXiv 2 Apr 2022.

## Builds on (most cited first, 1 of 1)

- [Human Gait Recognition Using Bag of Words Feature Representation Method](https://mlsanity.com/paper/2203.13317/) (2022)

## Built on by (most cited first, 1 of 1)

- [Improving Performance of Object Detection using the Mechanisms of Visual Recognition in Humans](https://mlsanity.com/paper/2301.09667/) (2023)

## Abstract (arXiv)

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.