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Short‐term electric power load forecasting using factor analysis and long short‐term memory for smart cities
Author(s) -
Veeramsetty Venkataramana,
Chandra D. Rakesh,
Salkuti Surender Reddy
Publication year - 2021
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/cta.2928
Subject(s) - term (time) , electrical load , electric power system , reliability engineering , load factor , power (physics) , engineering , power factor , factor (programming language) , peak load , electric power , long short term memory , computer science , real time computing , simulation , electrical engineering , automotive engineering , artificial neural network , artificial intelligence , voltage , structural engineering , physics , quantum mechanics , recurrent neural network , programming language
Summary Electric load estimation is an important activity for electrical power system operators to operate the system stably and optimally. This paper develops a machine learning model with a long short‐term memory and a factor analysis to predict the load at a specific hour of the day on an electrical power substation. Historical load data from the 33‐/11‐kV substation near Kakatiya University in Warangal are taken at each hour of the day for the period from September 2018 to November 2018. A new long short‐term memory architecture with factor analysis is being designed based on the approach used to predict substation loads by simulation in Microsoft Azure Notebooks. Based on the study, it was found that the proposed design predicts loads with good accuracy.