292 Views · 112 Downloads · ★★★★☆ 4.3

Artificial intelligence in smart grid for energy management: power load prediction

PUBLISHED August 15, 2023 (DOI: https://doi.org/10.54985/peeref.2308p8584388)

NOT PEER REVIEWED

Authors

Chibuzo Valentine Nwadike1
  1. Illinois Institute of Technology, IEEE

Conference / event

23rd Annual IEEE International Conference on Electro Information Technology (eit2023), May 2023 (Lewis University, United States)

Poster summary

This project focuses on analysing load and temperature data collected from utility companies in the US, consisting of 20 load zones with varying patterns of hourly load values and 11 temperature stations with distinct locations. The objective is to identify patterns and correlations between temperature data and load values for each zone and develop a predictive model for load values using machine learning algorithms. A thorough data exploration was conducted to examine potential correlations between temperature stations and load values in each zone. In cases where strong correlations were found, the temperature data from the correlated station was utilized to predict load values in the corresponding zones. However, in instances where strong correlations were not identified, a method was devised to select temperature data from a station and incorporate it into machine learning algorithms for predicting load values in each load zone.

Keywords

Load prediction, Regression, Machine learning, Smart grid, Load zones, Temperature zones

Research areas

Computer and Information Science , Electrical Engineering, Energy Engineering

References

No data provided

Funding

No data provided

Supplemental files

No data provided

Additional information

Competing interests
No competing interests were disclosed.
Data availability statement
The datasets generated during and / or analyzed during the current study are available from the corresponding author on reasonable request.
Creative Commons license
Copyright © 2023 Nwadike. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Rate
Cite
Nwadike, C. Artificial intelligence in smart grid for energy management: power load prediction [not peer reviewed]. Peeref 2023 (poster).
Copy citation

Become a Peeref-certified reviewer

The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.

Get Started

Ask a Question. Answer a Question.

Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.

Get Started