Low dimension active power load data using autoencoder
Published: 2 February 2023| Version 1 | DOI: 10.17632/7vdt5rz47x.1
Contributors:
Venkataramana Veeramsetty, , , , , Description
Dimensionality Reduction (DR) is key machine learning technique used to convert data from higher dimensional space to lower dimensionality space in order to build a predictive machine learning models with less number of model parameters. Original active power load dataset is prepared by collecting the data from 33/11KV substation near Godishala village in Telangana state, India. It consists total 12 features like L(T-1), L(T-2), L(T-3), L(T-4), L(T-24), L(T-48), L(T-72), L(T-96), Temperature, Humidity, Season and Day. This 12 features data is reconstructed in to 10 features using autoencoder with a training loss of 0.0061 and validation loss of 0.0062.
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Institutions
SR Engineering College
Categories
Electricity, Electric Power, Electric Power Distribution, Autoencoder