Home Energy Management: New Real-Time Pricing Models

Researchers at Chung-Ang University propose a predictive energy management system with a customized real-time bidirectional pricing mechanism to improve power grid balance.

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Home energy management is becoming crucial in the face of rising global consumption and associated costs.
Home energy management systems (HEMS) using Internet of Things (IoT) technologies offer promising solutions for monitoring and controlling consumption in real time.
These systems enable energy suppliers to adjust household consumption in response to grid demand, but challenges remain, not least in terms of consumer engagement.

Current Challenges in Demand Response

Real-time pricing (RTP) models were introduced to encourage residential demand response (DR) by offering financial incentives.
However, these traditional models are often unidirectional and fail to generate sustainable consumer engagement.
This limitation reduces the effectiveness of DR programs, requiring new approaches to maximize user participation.

Innovation from Chung-Ang University

Researchers at Chung-Ang University, led by Professor Mun Kyeom Kim and PhD student Hyung Joon Kim, propose a predictive home energy management system (PHEMS) with a customized two-way real-time pricing mechanism (CBi-RTP).
Published in the IEEE Internet of Things Journal, their study incorporates an advanced price prediction model, offering compelling reasons for consumers to actively participate in DR.

How the CBi-RTP system works

CBi-RTP enables users to manage their RTP hourly rates by controlling their energy consumption and the use of their household appliances.
PHEMS uses a predictive model based on deep learning and an optimization strategy to analyze spatio-temporal variations in RTPs.
This ensures robust, cost-effective operation for residential users, adapting to irregularities.

Results and future prospects

Experimental results show that the PHEMS model improves user comfort and outperforms previous models in terms of forecast accuracy, peak reduction and cost savings.
The researchers do, however, identify areas for improvement, notably the precise determination of the base load for calculating hourly shifted power.
Prof. Mun Kyeom Kim
Mun Kyeom Kim points out that future research will focus on improving the reliability of PHEMS through user-specific base load calculation methods.
This innovative real-time pricing approach represents a promising step forward in improving the efficiency of home energy management and encouraging greater consumer participation.
Future developments will aim to enhance the reliability and acceptability of this system.

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