Open AccessHousehold Load forecasting using Deep Learning neural networks
Author(s) -
et. al. C.Srisailam
Publication year - 2021
Publication title -
türk bilgisayar ve matematik eğitimi dergisi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.218
H-Index - 3
ISSN - 1309-4653
DOI - 10.17762/turcomat.v12i2.1085
Subject(s) - computer science , artificial neural network , field (mathematics) , term (time) , long short term memory , artificial intelligence , electricity , deep learning , electric power system , sample (material) , electrical load , data collection , time series , machine learning , power (physics) , recurrent neural network , data mining , industrial engineering , engineering , electrical engineering , chemistry , physics , statistics , mathematics , chromatography , quantum mechanics , pure mathematics
Advancements in different types of electrical meters and computing technologies aiding the data collection and sensing of various parameters of the electrical power system has been made possible with the availability of vast amount of electrical data. With the help of such technology and data, statistical prediction of load can be made smarter and more accurate. This can help stop excessive electricity production. With the help of deep learning techniques such as a long-short-term neural network (LSTM), it is possible to build time-series models that map non-linear parameters that can be used for precise memory sequences. An increase in recognition is witnessed in the field of forecasting with a short-term demand. In the field of power system control, it is now considered important. When proper pre-data is available, precision results can be high. Here, we are employing long short term neural network to forecast the load of a sample household.