Rakesh Kumar Yadav
IIIT Bhubaneswar

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Electrical load forecasting through long short term memory Debani Prasad Mishra; Sanhita Mishra; Rakesh Kumar Yadav; Rishabh Vishnoi; Surender Reddy Salkuti
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp42-50

Abstract

For a power supplier, meeting demand-supply equilibrium is of utmost importance. Electrical energy must be generated according to demand, as a large amount of electrical energy cannot be stored. For the proper functioning of a power supply system, an adequate model for predicting load is a necessity. In the present world, in almost every industry, whether it be healthcare, agriculture, and consulting, growing digitization and automation is a prominent feature. As a result, large sets of data related to these industries are being generated, which when subjected to rigorous analysis, yield out-of-the-box methods to optimize the business and services offered. This paper aims to ascertain the viability of long short term memory (LSTM) neural networks, a recurrent neural network capable of handling both long-term and short-term dependencies of data sets, for predicting load that is to be met by a Dispatch Center located in a major city. The result shows appreciable accuracy in forecasting future demand.