Open AccessElectrical load forecasting through long short term memory
Author(s) -
Debasis Mishra,
Sreedhar Madichetty,
Rakesh Kumar Yadav,
Rishabh Vishnoi,
Surender Reddy Salkuti
Publication year - 2022
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v25.i1.pp42-50
Subject(s) - automation , term (time) , artificial neural network , computer science , demand forecasting , digitization , long short term memory , electrical load , supply and demand , industrial engineering , operations research , electric potential energy , power (physics) , reliability engineering , recurrent neural network , engineering , artificial intelligence , telecommunications , electrical engineering , economics , mechanical engineering , physics , quantum mechanics , voltage , microeconomics
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.