Electrical load forecasting through long short term memory

Debani Prasad Mishra (IIIT Bhubaneswar)
Sanhita Mishra (KIIT Deemed to be University)
Rakesh Kumar Yadav (IIIT Bhubaneswar)
Rishabh Vishnoi (IIIT Bhubaneswar)
Surender Reddy Salkuti (Woosong University)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 25, No 1: January 2022

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.