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Influence of Wind Speed on Solar PV Plant Power Production—Prediction Model Using Decision-Based Artificial Neural Network

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Advances in Computational Intelligence and Communication Technology

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1086))

Abstract

An effort has been made to study the different factors influencing the output of a solar photovoltaic (PV) plant. Environmental factors play a significant role in planning the placement of PV modules. Various factors like irradiation, wind speed, atmospheric temperature and cloud coverage affect the power obtained from the plant. Establishing a relationship between the multiple factors and the output of the plant makes it easy to decide and plan the site of installation of the module and for further research purposes. The effect of wind speed on the power output of S. N. Mohanty solar plant located in Patapur, Cuttack (Odisha, IN) is studied. The research work has been carried out with data acquired every 15 min from January 2015 to December 2015 and keeping the irradiation constant. The monthly plots help establish a relationship between the wind speed and the power generated from the plant. The data is then used to create and train a neural network model which is used to predict the power output of the plant given the appropriate input data. This paper demonstrates the training of artificial neural network using environmental and PV module data (module temperature, wind speed and month).

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Abbreviations

PV:

Photovoltaic

ANN:

Artificial neural network

NOCT:

Nominal operating cell temperature

PCC:

Pearson’s correlation coefficient

MFFNNBP:

Multilayer feed-forward with backpropagation neural networks

GRNN:

General regression neural network

FFBP:

Feed-forward backpropagation

RNN:

Recursive neural network

MLP:

Multilayer perceptron

GM:

Gamma memory

RBFEF:

Radial basis function exact fit

FFNN:

Feed-forward neural network

DBNN:

Decision-based neural network

RMSE:

Root mean square error

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, National Institute of Technology, Sundargarh, Rourkela, Odisha, 769008, India

    Roshan Mohanty & Paresh G. Kale

Authors
  1. Roshan Mohanty
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  2. Paresh G. Kale
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Corresponding author

Correspondence to Roshan Mohanty .

Editor information

Editors and Affiliations

  1. School of Computing, University of Eastern Finland, Kuopio, Finland

    Xiao-Zhi Gao

  2. Computer Science Engineering Department, ABES Engineering College, Delhi, India

    Shailesh Tiwari

  3. Department of Computer Science and Engineering, National Institute of Technology Agartala, Agartala, Tripura, India

    Munesh C. Trivedi

  4. Motilal Nehru National Institute of Technology, Allahabad, Uttar Pradesh, India

    Krishn K. Mishra

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Mohanty, R., Kale, P.G. (2021). Influence of Wind Speed on Solar PV Plant Power Production—Prediction Model Using Decision-Based Artificial Neural Network. In: Gao, XZ., Tiwari, S., Trivedi, M., Mishra, K. (eds) Advances in Computational Intelligence and Communication Technology. Advances in Intelligent Systems and Computing, vol 1086. Springer, Singapore. https://doi.org/10.1007/978-981-15-1275-9_1

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