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Short-Term Load Forecasting for a Captive Power Plant Using Artificial Neural Network

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  • Vidhi Tiwari

    (Gautam Buddha University, India)

  • Kirti Pal

    (Gautam Buddha University, India)

Abstract

The irregularity of Indian grid system increases, with increase in the power demand. The quality of power supplied by the power grid is also poor due to continuous variation in frequency and voltage. To overcome this problem of power deficit, Captive Power Plants installed capacity has grown at a faster rate. Here short term load forecasting of Yara Fertilizers India Private limited installed at Babrala, Uttar Pradesh is performed using multi-layer feed-forward Neural network in MATLAB. The algorithm used is a Levenberg Marquardt algorithm. However, the training and results from ANN are very fast and accurate. Inputs given to the Neural Network are time, ambient air temperature from the compressor, cool air temperature at the compressor and IGV opening. The need, benefits and growth of CPP in India and use of ANN for short term load forecasting of CPP has been explained in detail in the paper.

Suggested Citation

  • Vidhi Tiwari & Kirti Pal, 2022. "Short-Term Load Forecasting for a Captive Power Plant Using Artificial Neural Network," International Journal of Information Retrieval Research (IJIRR), IGI Global, vol. 12(1), pages 1-11, January.
    • Handle: RePEc:igg:jirr00:v:12:y:2022:i:1:p:1-11
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    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIRR.289613
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    References listed on IDEAS

    as
    1. Salvatore Graziani & Maria Gabriella Xibilia, 2020. "Innovative Topologies and Algorithms for Neural Networks," Future Internet, MDPI, vol. 12(7), pages 1-4, July.
    2. Wenkuan Li & Peiyu Liu & Qiuyue Zhang & Wenfeng Liu, 2019. "An Improved Approach for Text Sentiment Classification Based on a Deep Neural Network via a Sentiment Attention Mechanism," Future Internet, MDPI, vol. 11(4), pages 1-15, April.
    3. Hongwei Zhao & Weishan Zhang & Haoyun Sun & Bing Xue, 2019. "Embedded Deep Learning for Ship Detection and Recognition," Future Internet, MDPI, vol. 11(2), pages 1-12, February.
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