Adama II wind farm long-term power generation forecasting based on machine learning models
The present article develops time series machine learning models to forecast the Adama II wind farm's long-term power production using SCADA data. The study applied data from the previous six years (2016 to 2021) with five years of data for training the model and the remaining one year for test...
Main Authors: | Solomon Terefe Ayele, Mesfin Belayneh Ageze, Migbar Assefa Zeleke, Temesgen Abriham Miliket |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2023-09-01
|
Series: | Scientific African |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2468227623002879 |
Similar Items
-
Improving wind speed forecasting at Adama wind farm II in Ethiopia through deep learning algorithms
by: Mesfin Diro Chaka, et al.
Published: (2024-06-01) -
Experimental characterizations of hybrid natural fiber-reinforced composite for wind turbine blades
by: Temesgen Abriham Miliket, et al.
Published: (2022-03-01) -
Day-Ahead Hourly Solar Photovoltaic Output Forecasting Using SARIMAX, Long Short-Term Memory, and Extreme Gradient Boosting: Case of the Philippines
by: Ian B. Benitez, et al.
Published: (2023-11-01) -
Spatial dispersion of wind speeds and its influence on the forecasting error of wind power in a wind farm
by: Gang Mu, et al.
Published: (2016-01-01) -
Onshore Wind Farm Development: Technologies and Layouts
by: Francisco Haces-Fernandez, et al.
Published: (2022-03-01)