Result Details
Transferability of ML Time Series Prediction for Energy Forecasting in Smart Homes
John Petr, Ing., DIFS (FIT), DCGM (FIT)
Hynek Jiří, Ing., Ph.D., DIFS (FIT)
Hruška Tomáš, prof. Ing., CSc., DIFS (FIT)
This work explores the use of machine learning models (ML) in the context of
Internet of Things-enabled smart energy management systems, particularly focusing
on home energy management systems (HEMS). With the growing adoption of such
devices, these systems have the potential to improve energy efficiency and reduce
costs. This paper examines the feasibility of using time series prediction models
for energy consumption forecasting, replacing traditional methods like
Auto-Regressive Moving Average (ARMA) with deep learning approaches, namely Time
Convolutional Network (TCN) and Temporal Convolutional Network - Long Short-Term
Memory (TCN-LSTM) architectures. Using two smart home datasets, NIST and IHEPC,
the paper evaluates the transferability and accuracy of the models. Results
indicate that while the models perform well within a single dataset, they
struggle to transfer reliably between datasets, likely due to the limited feature
set used. Despite this, the models can be deployed on low-power devices with
artificial intelligence (AI) chips, though their real-world application may
require significant investment in sensors or reliance on third-party Application
Programming Interfaces. The findings highlight the potential of machine learning
in smart energy systems, while also addressing challenges related to model
transferability and practical deployment. These findings contribute to Smart
Cities Modeling by highlighting the role of machine learning in optimizing energy
use for sustainable urban systems.
electricity consumption prediction, IoT, TCN, TCN-LSTM, smart grid
@inproceedings{BUT197684,
author="Filip {Štolfa} and Petr {John} and Jiří {Hynek} and Tomáš {Hruška}",
title="Transferability of ML Time Series Prediction for Energy Forecasting in Smart Homes",
booktitle="IEEE Xplore",
year="2025",
series="2025 Smart City Symposium Prague (SCSP)",
pages="1--6",
publisher="Institute of Electrical and Electronics Engineers",
address="Prague",
doi="10.1109/SCSP65598.2025.11037688",
isbn="979-8-3315-2550-7",
url="https://ieeexplore.ieee.org/document/11037688"
}