Name
Title | ||
|---|---|---|
A Comparative Analysis of Machine Learning Methods for Short-Term Load Forecasting Systems |
Abstract | ||
|---|---|---|
End-users' electricity consumption is highly affected by weather conditions. The uncertain nature of these circumstances can highly challenge energy supply and demand balancing. The identification of explanatory variables that influence energy usage plays a key role in addressing this issue. This paper conducts a benchmark study of several machine learning methods to compare their ability to deter... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/SmartGridComm51999.2021.9632002 | 2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) |
Keywords | DocType | ISBN |
Temperature measurement,Machine learning algorithms,Temperature,Supply and demand,Load forecasting,Prediction algorithms,Smart grids | Conference | 978-1-6654-1502-6 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. Parrado-Duque | 1 | 0 | 0.34 |
| S. Kelouwani | 2 | 0 | 0.34 |
| K. Agbossou | 3 | 0 | 0.34 |
| S. Hosseini | 4 | 0 | 0.34 |
| N. Henao | 5 | 0 | 0.34 |
| F. Amara | 6 | 0 | 0.34 |