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Big Data Analytic: Cases for Communications Systems Modeling and Renewable Energy Forecast

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Control and Systems Engineering

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 27))

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Abstract

Data analytic-based approaches are proposed for different cases of communications systems modeling based on qualitative methods and solar energy forecasting based on artificial intelligence in order to represent the spread of areas to which such techniques are applicable.

The first case uses qualitative methods for modeling of communications systems. Radio over fiber (RoF) communications system is considered for cable television (CATV) channels over wavelength division multiplexing (WDM) network using an optical direct modulator (DM). The link performance is studied for different combinations of AC electrical power and Module to Bias ratio. The best setting options are proposed for 30 CATV channels with 64- QAM constellation transmitted over the 4-WDM link.

Moreover, the communications system is modeled by Fuzzy logic using dataset obtained from simulation. Combination of Fuzzy logic and genetic algorithms (GA) is implemented to provide the user with reasonable estimates of electrical power required to achieve desired performance. Fuzzy-GA modeling capabilities are illustrated and discussed.

The second example considers application of data mining and artificial intelligence in forecasting of expected solar energy. The models are developed and validated utilizing a raw large data base from the National Renewable Energy Laboratory (NREL) archive sampled at 1-minute intervals during 8 years (2004 - 2012) at the NREL site in Golden, Colorado.

The methodology uses an integrated serial time-domain analysis coupled with multivariate analysis for pre-processing of the data. The resulting enhanced data set is then used for training of the forecast core engine core implemented using an artificial neural networks (ANN). Characteristics of the proposed data analytic-based approach are illustrated and compared to the physics-based methods. Performance of the proposed framework is compared to those of the current state-of-the-art in 24-hour ahead solar energy forecasting in order to provide a clear understanding of its capabilities.

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Author information

Authors and Affiliations

  1. ACE Laboratory, Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, Texas, USA

    Yashar Sahraei Manjili

  2. Photonics Research Lab., Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, Texas, USA

    Maryam Niknamfar

Authors
  1. Yashar Sahraei Manjili
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  2. Maryam Niknamfar
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Correspondence to Yashar Sahraei Manjili .

Editor information

Editors and Affiliations

  1. Electrical Engineering Department, New Mexico Institute of Mining and Technology, Socorro, New Mexico, USA

    Aly El-Osery

  2. Electrical and Computer Engineering Department, The University of Texas at San Antonio, San Antonio, Texas, USA

    Jeff Prevost

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Manjili, Y.S., Niknamfar, M. (2015). Big Data Analytic: Cases for Communications Systems Modeling and Renewable Energy Forecast. In: El-Osery, A., Prevost, J. (eds) Control and Systems Engineering. Studies in Systems, Decision and Control, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-14636-2_6

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  • DOI: https://doi.org/10.1007/978-3-319-14636-2_6

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14635-5

  • Online ISBN: 978-3-319-14636-2

  • eBook Packages: EngineeringEngineering (R0)

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