Optimally designed Variational Autoencoders for Efficient Wind Characteristics Modelling

Miriyala, Srinivas Soumitri and Chowdhury, Subhankar and Pujari, NagaSree Keerthi and Mitra, Kishalay (2020) Optimally designed Variational Autoencoders for Efficient Wind Characteristics Modelling. In: 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, 1 December 2020 - 4 December 2020.

Full text not available from this repository. (Request a copy)


Wind energy is increasingly applied as a large scale clean energy generating alternative to fossil fuels. However, limited amount of real wind data results in inaccurate construction of Wind Frequency Maps (WFMs), which model the stochastic nature of wind. The inaccuracies in WFMs may lead to over or under estimation of wind power eventually causing significant losses to wind-farmers. Hence, to resolve this crisis, deep generative models such as convolutional Variational Autoencoders (VAEs) are implemented in this work to enable accurate construction of WFMs from limited amount of real wind characteristics data. However, the heuristics based estimation of hyper-parameters in VAEs decrease their efficiency. Thus, in this work, a novel multi-objective evolutionary neural architecture search (NAS) strategy is devised for simultaneously estimating the optimal number of convolutional and feedforward layers, number of filters/nodes in each layer, filter size, pooling option and nonlinear activation choice in VAEs. The proposed framework is designed to balance the conflicting objectives of generalizability and parsimony in VAEs, thereby reducing the chances of their over-fitting. The optimally designed VAE (with 92% accuracy) is used to generate new wind frequency scenarios for accurate construction of WFM. Additionally, the effect of number of new scenarios required for accurate WFM construction is also studied while performing the comparison with an ideal case. It was found that WFM constructed with original limited data resulted in 9% deficit in energy calculation from a single wind turbine, justifying the need for generative models such as VAEs for accurate wind characteristics modelling.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Miriyala, Srinivas SoumitriUNSPECIFIED
Chowdhury, SubhankarUNSPECIFIED
Pujari, NagaSee KeerthiUNSPECIFIED
Mitra, Kishalayhttp://orcid.org/0000-0001-5660-6878
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Alternative to fossil fuels; Conflicting objectives; Energy calculation; Multi-objective evolutionary; Neural architectures; Non-linear activation; Stochastic nature; Wind characteristics; Convolution; Evolutionary algorithms; Fossil fuels; Intelligent computing; Learning systems; Optimal systems; Optimization; Stochastic models; Stochastic systems; Wind power
Subjects: Chemical Engineering
Divisions: Department of Chemical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 28 Jun 2021 09:34
Last Modified: 28 Jun 2021 09:34
URI: http://raiith.iith.ac.in/id/eprint/8030
Publisher URL: http://doi.org/10.1109/SSCI47803.2020.9308245
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 8030 Statistics for this ePrint Item