A comparative analysis of Deep Neural Networks and Gradient Boosting Algorithms in long-term wind power forecasting
A vital step toward a sustainable future is the power grid's incorporation of renewable energy sources. Wind energy is significant because of its broad availability and minimal environmental impact. The paper presents a comparative analysis of recurrent neural network algorithms and gradient bo...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Electrical Engineering Institute Nikola Tesla
2024-01-01
|
| Series: | Zbornik Radova: Elektrotehnički Institut "Nikola Tesla" |
| Subjects: | |
| Online Access: | https://scindeks-clanci.ceon.rs/data/pdf/0350-8528/2024/0350-85282434015I.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | A vital step toward a sustainable future is the power grid's incorporation of renewable energy sources. Wind energy is significant because of its broad availability and minimal environmental impact. The paper presents a comparative analysis of recurrent neural network algorithms and gradient boosting machines applied to time series data for the regression issue of estimating the active power generated by a wind farm. Gradient boosting algorithms combine the advantages of a few machine learning models (decision trees, random forests, etc.) to produce a powerful prediction model. In addition to conventional recurrent neural networks, the article deals with long short-term memory and gated recurrent unit as cutting-edge models for time series analysis and predictions. A comprehensive analysis was carried out on a large wind power generation data set. |
|---|---|
| ISSN: | 0350-8528 2406-1212 |