Applied LSTM neural network time series to forecast household energy consumption
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2021-07
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SCOPUS
Resumen
In Ecuador, energy consumption is accentuated in
the residential sector due to population growth and other
parameters, which leads to an increase in energy costs,
greenhouse gas emissions and fossil fuel subsidies. Hence, there
is a need to optimize and reduce energy consumption in
buildings. One approach considered is predictive control
systems, for which high accuracy consumption predictions are
required. In this work we will apply supervised machine
learning techniques using neural networks to forecast the
energy consumption behavior of a family house; for this
purpose, an experimental design is proposed using a dataset of
almost four years of energy measurements, four different Long
Short-Term Memory (LSTM) architectures are tested and
about 200 models are run by varying hyperparameters. Metrics
such as root mean square error (RMSE), mean absolute error
(MAE) and mean absolute percent error (MAPE) are
considered to compare and select the best LSTM model, being
the best simple LSTM structure with vectorial output.
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Palabras clave
BUILDINGS, ENERGY EFFICIENCY, FORECASTING, LSTM, TIME SERIES
Citación
Segura, G., Guamán, J., Mite-León, M., Macas-Espinosa, V., & Barzola-Monteses, J. (2021). Applied LSTM neural network time series to forecast household energy consumption. In 19th LACCEI International Multi-Conference for Engineering, Education, and Technology:“Prospective and trends in technology and skills for sustainable social development”“Leveraging emerging technologies to construct the future (