The study proposes a predictive home energy management system
with a customizable bidirectional real-time pricing
mechanism
SEOUL, South Korea,
July 25, 2024 /PRNewswire/ --
Consumer motivation to participate in residential demand
response (DR) has historically been low due to inflexible
electricity pricing mechanisms that do not account for individual
use. A new predictive home energy management system (PHEMS)
addresses this issue with a real-time pricing mechanism.
This system customizes electricity prices based on end-user energy
consumption, thereby motivating consumers to actively participate
in DR. Going ahead, it holds the potential to enhance
engagement and efficiency in residential energy management.
With a continuous rise in the global population, energy
consumption and its associated environmental and economic costs are
also increasing. One effective approach to manage these rising
costs is by promoting the use of smart home appliances, leveraging
Internet of Things (IoT) technologies to connect devices within a
single network. This connectivity can enable users to monitor and
control their real-time power consumption via home energy
management systems (HEMS). Energy providers can, in turn, utilize
HEMS to gauge residential demand response (DR) and adjust the power
consumption of residential customers in response to grid
demand.
Efforts to promote residential DR, such as by offering monetary
incentives under the real-time pricing (RTP) model, have
historically struggled to foster lasting behavioral change among
consumers. This challenge stems from unidirectional electricity
pricing mechanisms, which diminish consumer engagement in
residential DR activities.
To address these issues, Professor Mun Kyeom Kim and
Hyung Joon Kim, a doctoral candidate
from Chung-Ang University, recently conducted a study published in
the IEEE Internet of Things Journal. Their study, proposing
a predictive home energy management system (PHEMS), was published
online on March 27, 2024, and in
print on July 15, 2024. Prof. Mun
Kyeom Kim led the study, introducing a customized bidirectional
real-time pricing (CBi-RTP) mechanism integrated with an advanced
price forecasting model. These innovations provide compelling
reasons for consumers to participate actively in residential DR
efforts.
The CBi-RTP system empowers end-users by allowing them to
influence their hourly RTPs through managing their transferred
power and household appliance usage. Moreover, PHEMS incorporates a
deep-learning-based forecasting model and optimization strategy to
analyze spatial-temporal variations inherent in RTP
implementations. This capability ensures robust and cost-effective
operation for residential users by adapting to irregularities as
they arise.
Experimental results from the study demonstrate that the PHEMS
model not only enhances user comfort but also surpasses previous
models in accuracy of forecasting, peak reduction, and cost
savings. Despite its superior performance, the researchers
acknowledge room for further development. Prof. Mun Kyeom Kim
notes, "The main challenge with our predictive home energy
management system lies in accurately determining the baseline load
for calculating hourly shifted power. Future research will focus on
enhancing the reliability of PHEMS through improved baseline load
calculation methods tailored to specific end-users."
Reference
Title of original paper: New Customized Bidirectional Real-Time
Pricing Mechanism for Demand Response in Predictive Home Energy
Management System
Journal: IEEE Internet Of Things Journal
DOI: https://doi.org/10.1109/JIOT.2024.3381606
About Chung-Ang University
Website:
https://neweng.cau.ac.kr/index.do
Media Contact:
Sungki Shin
380811@email4pr.com
02-820-6614
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SOURCE Chung-Ang University