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

04/02/2022
by   Nasrin Bayat, et al.
0

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.

READ FULL TEXT
research
05/11/2019

Accuracy Improvement of Neural Network Training using Particle Swarm Optimization and its Stability Analysis for Classification

Supervised classification is the most active and emerging research trend...
research
02/13/2023

Restoring the saturation response of a PMT using pulse-shape and artificial-neural-networks

The linear response of a photomultiplier tube (PMT) is a required proper...
research
03/10/2015

Technical Analysis on Financial Forecasting

Financial forecasting is an estimation of future financial outcomes for ...
research
11/30/2020

Extracting Electron Scattering Cross Sections from Swarm Data using Deep Neural Networks

Electron-neutral scattering cross sections are fundamental quantities in...
research
06/24/2003

Predicting Response-Function Results of Electrical/Mechanical Systems Through Artificial Neural Network

In the present paper a newer application of Artificial Neural Network (A...
research
03/07/2022

High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networks

This paper presents a method for estimating high-resolution electricity ...

Please sign up or login with your details

Forgot password? Click here to reset