Auto-tuned Deep Recurrent Neural Networks for Application in Wind Energy Conversion Systems

Pujari, NagaSree Keerthi and Miriyala, Srinivas Soumitri and Mitra, Kishalay (2020) Auto-tuned Deep Recurrent Neural Networks for Application in Wind Energy Conversion Systems. In: 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, 1 December 2020 - 4 December 2020.

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Abstract

As the uncertain nature of wind energy is the main reason behind inconsistency in functioning of the wind energy conversion systems (WECS), the accurate forecasting of wind characteristics such as wind speed and wind direction is helpful for more realistic and consistent results. Due to the efficiency in addressing long term temporal features and extreme nonlinearities, the authors have utilized Long Short Term Memory Networks (LSTMs) to model the wind characteristics in this work. However, the heuristic way of hyper-parameter selection (architecture design and activation function choice) in LSTMs makes their modelling inefficient and tedious. A novel algorithm to design LSTMs optimally by balancing the trade-off between number of parameters and accuracy, is, therefore, proposed. These LSTMs are utilized for long term forecasting by training on 4 years of real wind data with an accuracy of R^{2}=0.97. Further, to demonstrate the significance of accurate forecasting, the predictions are used to generate wind frequency maps, which forms the basis for energy calculations in WECS. These maps are subsequently used for calculating energy produced by a windfarm layout containing 37 turbines, where a recently developed Gaussian wake model is utilized for accounting the turbulence due to the presence of multiple turbines. The results and comparative studies have shown that realistic energy production is possible with the use of original and accurately forecasted data obtained from optimal LSTMs

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IITH Creators:

IITH Creators	ORCiD
Pujari, NagaSree Keerthi	UNSPECIFIED
Miriyala, Srinivas Soumitri	UNSPECIFIED
Mitra, Kishalay	http://orcid.org/0000-0001-5660-6878

Item Type:

Conference or Workshop Item (Paper)

Uncontrolled Keywords:

Activation functions; Architecture designs; Comparative studies; Energy calculation; Energy productions; Long-term forecasting; Wind characteristics; Wind energy conversion system

Subjects:

Chemical Engineering

Divisions:

Department of Chemical Engineering

Depositing User:

. LibTrainee 2021

Date Deposited:

28 Jun 2021 09:44

Last Modified:

28 Jun 2021 09:44

URI:

http://raiithold.iith.ac.in/id/eprint/8031