Investigation of Performance of Electric Load Power Forecasting in Multiple Time Horizons With New Architecture Realized in Multivariate Linear Regression and Feed-Forward Neural Network Techniques

A new multiple parallel input and parallel output architecture-based models are developed for forecasting electric load power consumption. Attention was paid toward the improvement of the accuracy of forecasting using this new architecture built-in by us, as compared to others in the latest literatu...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on industry applications 2020-09, Vol.56 (5), p.5603-5612
Main Authors: Selvi, M. Vetri, Mishra, Sukumar
Format: Article
Language:English
Subjects:
Architecture
Artificial neural network (ANN)
Biological system modeling
day-ahead forecasting
Delhi
Economic forecasting
electric load power forecasting (ELPF)
Electric power distribution
feed-forward neural network (FFNN)
Forecasting
Lead time
Linear regression
load forecasting
Load modeling
Mathematical models
Meteorology
Multivariate analysis
multivariate linear regression (MvLR)
Neural networks
Power consumption
Predictive models
regression
Regression analysis
week ahead
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A new multiple parallel input and parallel output architecture-based models are developed for forecasting electric load power consumption. Attention was paid toward the improvement of the accuracy of forecasting using this new architecture built-in by us, as compared to others in the latest literature. The models have an ability to forecast daily load profiles with a lead time of one to seven days. Both multivariate linear regression and feed-forward neural network techniques have been chosen for comparative performance study and analysis. The real-time data used for this research work were collected from Tata Power Delhi Distribution Limited, Delhi, India. Based on the performance criteria provided in the literature, each model is analyzed and the results are presented for two different lead times, i.e., day-ahead and week-ahead only.
ISSN:0093-9994
1939-9367
DOI:10.1109/TIA.2020.3009313