Analytical study of Wind power prediction system by using Feed Forward Neural Network

Jain, Vicky; Singh, Alok; Chauhan, Vibhor; Pandey, Alok Kumar

doi:10.1109/iccpeic.2016.7557247

Cited by 8 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Two are the keystones for circumventing the above issues, about wind power forecasting: Artificial Intelligence and data. Artificial Neural Networks (ANN) are often used for their capability in reconstructing non-linear dependency between input and outputs and they are often used to connect directly the mesoscale wind conditions to the power output of the wind turbines on site [19][20][21][22][23]. The ingredient to feed (and train) the ANNs with are data: the inputs (mesoscale) and the outputs (typically, the power of the wind turbines).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Two ANN Approaches for the Wind Power Forecast in a Mountainous Site

2017

IJRER

View full text Add to dashboard Cite

Accurate wind power forecast is very important in order to construct smart electric grids. Nevertheless, this task still constitutes a challenge because wind is a very variable and local phenomenon. It is difficult to downscale information coming from Numerical Weather Prediction (NWP) models down to wind farm level and this is especially true onshore, in complex terrain conditions. Artificial Intelligence often comes at hand, for its power in learning what is hidden inside data: Artificial Neural Networks (ANN) are therefore commonly employed for wind power forecast. In this work, a pure ANN method is compared against a hybrid method, based on the combination of ANN and a numerical method based on physically-consistent assumptions (Computational Fluid Dynamics). Both approaches are validated against the SCADA data of a wind farm sited in Italy in a very complex terrain. It arises that the two methods have overall similar performances on average. However, pure ANN turns out to forecast better at mid-energy levels and during cut-off events at the highest wind speed, whereas the hybrid method forecasts better during low and high wind speed ranges. This makes the two approaches complementary and promising for future applications through an ensemble strategy.

show abstract

Section: Introductionmentioning

confidence: 99%

Evaluation of Two ANN Approaches for the Wind Power Forecast in a Mountainous Site

2017

IJRER

View full text Add to dashboard Cite

show abstract

“…For instance, from temporal and geographic point of view and if we see from geographic perspective, each turbine's power outputs rely on its wind farm geographic location that is usually different. The industry standard is associate the power of turbine to the hub height wind velocity [3]. There are many forecasting methods available.…”

Section: Fig 1 Worldwide Wind Power Installed Capacity (2001-2006)mentioning

confidence: 99%

Wind Power Prediction by Using Matrix Factorization Technique

2017

IJAERD

View full text Add to dashboard Cite

show abstract

Artificial Intelligence as a Tool for Conservation and Efficient Utilization of Renewable Resource

Vinay

Bale

Tiwari

et al. 2022

Artificial Intelligence for Renewable Energy Systems

View full text Add to dashboard Cite

Analytical study of Wind power prediction system by using Feed Forward Neural Network

Cited by 8 publications

References 10 publications

Evaluation of Two ANN Approaches for the Wind Power Forecast in a Mountainous Site

Evaluation of Two ANN Approaches for the Wind Power Forecast in a Mountainous Site

Wind Power Prediction by Using Matrix Factorization Technique

Artificial Intelligence as a Tool for Conservation and Efficient Utilization of Renewable Resource

Contact Info

Product

Resources

About