TELKOMNIKA Telecommunication, Computing, Electronics and Control
Feature engineering and long short-term memory for energy use of appliances prediction
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Feature engineering and long short-term memory for energy use of appliances prediction
Feature engineering and long short-term memory for energy use of appliances prediction
Subject
Appliances
Feature engineering
Long short-term memory
Principal component analysis
Prediction
Feature engineering
Long short-term memory
Principal component analysis
Prediction
Description
Electric energy consumption in a residential household is one of the key factors
that affect the overall national electricity demand. Household appliances are
one of the most electricity consumers in a residential household. Therefore, it
is crucial to make a proper prediction for the electricity consumption of these
appliances. This research implemented feature engineering technique and long
short-term memory (LSTM) as a model predictor. Principal component
analysis (PCA) was implemented as a feature extractor by reducing the final
62 features to 25 principal components for the LSTM inputs. Based on the
experiments, the two-layered LSTM model (composed by 25 and 20 neurons
for the first and second later respectively) with lookback number of 3 found to
give the best performance with the error rates of 62.013 and 26.982 for root
mean squared error (RMSE) and mean average error (MAE), respectively.
that affect the overall national electricity demand. Household appliances are
one of the most electricity consumers in a residential household. Therefore, it
is crucial to make a proper prediction for the electricity consumption of these
appliances. This research implemented feature engineering technique and long
short-term memory (LSTM) as a model predictor. Principal component
analysis (PCA) was implemented as a feature extractor by reducing the final
62 features to 25 principal components for the LSTM inputs. Based on the
experiments, the two-layered LSTM model (composed by 25 and 20 neurons
for the first and second later respectively) with lookback number of 3 found to
give the best performance with the error rates of 62.013 and 26.982 for root
mean squared error (RMSE) and mean average error (MAE), respectively.
Creator
I Wayan Aditya Suranata, I Nyoman Kusuma Wardana, Naser Jawas, I Komang Agus Ady Aryanto
Source
http://journal.uad.ac.id/index.php/TELKOMNIKA
Date
Aug 31, 2020
Contributor
peri irawan
Format
pdf
Language
english
Type
text
Files
Collection
Citation
I Wayan Aditya Suranata, I Nyoman Kusuma Wardana, Naser Jawas, I Komang Agus Ady Aryanto, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Feature engineering and long short-term memory for energy use of appliances prediction,” Repository Horizon University Indonesia, accessed April 27, 2025, https://repository.horizon.ac.id/items/show/3817.
Feature engineering and long short-term memory for energy use of appliances prediction,” Repository Horizon University Indonesia, accessed April 27, 2025, https://repository.horizon.ac.id/items/show/3817.