Day-Ahead Wind Power Forecasting Using Machine Learning Algorithms

2020 ◽  
pp. 329-341
Author(s):  
R. Akash ◽  
A. G. Rangaraj ◽  
R. Meenal ◽  
M. Lydia
Keyword(s):  
Machine Learning ◽  
Wind Power ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Wind Power Forecasting ◽  
Power Forecasting

Comparative Investigation of Machine Learning Algorithms for Wind Power Forecasting

2021 ◽  
pp. 513-522
Author(s):  
Ayush Kumar ◽  
Neeraj Kumar ◽  
Bharat Singh ◽  
Aditya Chaudhary ◽  
Karan Dikshit ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wind Power ◽  
Learning Algorithms ◽  
Comparative Investigation ◽  
Machine Learning Algorithms ◽  
Wind Power Forecasting ◽  
Power Forecasting

Wind power forecasting based on time series model using deep machine learning algorithms

2021 ◽  
Author(s):  
V. Chandran ◽  
Chandrashekhar K. Patil ◽  
Anto Merline Manoharan ◽  
Aritra Ghosh ◽  
M.G. Sumithra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Wind Power ◽  
Learning Algorithms ◽  
Time Series Model ◽  
Machine Learning Algorithms ◽  
Wind Power Forecasting ◽  
Power Forecasting

scholarly journals An Evaluation on Diverse Machine Learning Algorithms for Hourly Univariate Wind Power Prediction in the Arctic

2021 ◽  
Vol 2141 (1) ◽  
pp. 012016
Author(s):  
Hao Chen ◽  
Yngve Birkelund
Keyword(s):  
Machine Learning ◽  
Wind Power ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The Arctic ◽  
Support Vector ◽  
Power Prediction ◽  
Wind Power Forecasting ◽  
Arctic Regions ◽  
Power Forecasting

Abstract Wind power forecasting is crucial for wind power systems, grid load balance, maintenance, and grid operation optimization. The utilization of wind energy in the Arctic regions helps reduce greenhouse gas emissions in this environmentally vulnerable area. In the present study, eight various models, seven of which are representative machine learning algorithms, are used to make 1, 2, and 3 step hourly wind power predictions for five wind parks inside the Norwegian Arctic regions, and their performance is compared. Consequently, we recommend the persistence model, multilayer perceptron, and support vector regression for univariate time-series wind power forecasting within the time horizon of 3 hours.

scholarly journals Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 338
Author(s):  
Lorenzo Donadio ◽  
Jiannong Fang ◽  
Fernando Porté-Agel
Keyword(s):  
Machine Learning ◽  
Wind Energy ◽  
Wind Power ◽  
Numerical Weather Prediction ◽  
Wind Farm ◽  
Weather Prediction ◽  
Wind Power Forecasting ◽  
Numerical Weather ◽  
Artificial Neural ◽  
Power Forecasting

In the past two decades, wind energy has been under fast development worldwide. The dramatic increase of wind power penetration in electricity production has posed a big challenge to grid integration due to the high uncertainty of wind power. Accurate real-time forecasts of wind farm power outputs can help to mitigate the problem. Among the various techniques developed for wind power forecasting, the hybridization of numerical weather prediction (NWP) and machine learning (ML) techniques such as artificial neural networks (ANNs) are attracting many researchers world-wide nowadays, because it has the potential to yield more accurate forecasts. In this paper, two hybrid NWP and ANN models for wind power forecasting over a highly complex terrain are proposed. The developed models have a fine temporal resolution and a sufficiently large prediction horizon (>6 h ahead). Model 1 directly forecasts the energy production of each wind turbine. Model 2 forecasts first the wind speed, then converts it to the power using a fitted power curve. Effects of various modeling options (selection of inputs, network structures, etc.) on the model performance are investigated. Performances of different models are evaluated based on four normalized error measures. Statistical results of model predictions are presented with discussions. Python was utilized for task automation and machine learning. The end result is a fully working library for wind power predictions and a set of tools for running the models in forecast mode. It is shown that the proposed models are able to yield accurate wind farm power forecasts at a site with high terrain and flow complexities. Especially, for Model 2, the normalized Mean Absolute Error and Root Mean Squared Error are obtained as 8.76% and 13.03%, respectively, lower than the errors reported by other models in the same category.

scholarly journals Long-Term Wind Power Forecasting Using Tree-Based Learning Algorithms

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 151511-151522
Author(s):  
Amirhossein Ahmadi ◽  
Mojtaba Nabipour ◽  
Behnam Mohammadi-Ivatloo ◽  
Ali Moradi Amani ◽  
Seungmin Rho ◽  
...  
Keyword(s):  
Wind Power ◽  
Learning Algorithms ◽  
Wind Power Forecasting ◽  
Power Forecasting
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