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

Vidhi Tiwari, Kirti Pal

Source Title: International Journal of Information Retrieval Research (IJIRR)12(1)

ISSN: 2155-6377|EISSN: 2155-6385|EISBN13: 9781683182085|DOI: 10.4018/IJIRR.289613

MLA

Tiwari, Vidhi, and Kirti Pal. "Short-Term Load Forecasting for a Captive Power Plant Using Artificial Neural Network." IJIRR vol.12, no.1 2022: pp.1-11. http://doi.org/10.4018/IJIRR.289613

APA

Tiwari, V. & Pal, K. (2022). Short-Term Load Forecasting for a Captive Power Plant Using Artificial Neural Network. International Journal of Information Retrieval Research (IJIRR), 12(1), 1-11. http://doi.org/10.4018/IJIRR.289613

Chicago

Tiwari, Vidhi, and Kirti Pal. "Short-Term Load Forecasting for a Captive Power Plant Using Artificial Neural Network," International Journal of Information Retrieval Research (IJIRR) 12, no.1: 1-11. http://doi.org/10.4018/IJIRR.289613

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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.