IoT-Driven Energy Consumption Prediction in a Mexican Residence: ACase Study Utilizing Deep Learning with Attention Mechanism

Abstract

The energy consumption patterns exhibited
by individuals carry significant implications for both
the environment and energy production. Leveraging
historical data, inferential models can foresee future
consumption trends, assisting energy providers in
planning adequate energy generation while encouraging
shifts
towards more environmentally sustainable
practices. This, in turn, fosters a transition towards
more eco-friendly behaviors. This paper outlines the
development of a comprehensive system based on IoT
that gathers real-world energy consumption data from a
household in northeast Mexico, employs deep learning
models for prediction, and incorporates a visualization
tool to present energy demand. For the prediction, it was
conducted a comparative analysis on three advanced
deep learning models tailored for sequential data:
LSTM, GRU, and Seq2Seq. Additionally, we explored
the impact of enhancing each model with an Attention
mechanism. Our findings consistently demonstrate
that the incorporation of an Attention layer improves
model performance, leading to a reduction in error
metrics across the board. Specifically, we achieved
an average Mean Absolute Percentage Error of 8.83%
for daily predictions and 30.44% for hourly forecasts.
These results underscore the efficacy of our selected
models in accurately predicting energy consumption
patterns, marking a notable stride towards informed and
sustainable energy management.