wire Expo 2024 News: Researchers from Central University in Seoul Study Real-Time Electricity Pricing Model
With the global population continuously growing, energy consumption and its associated environmental and economic costs are also on the rise. Management costs are increasing as well. An effective approach to mitigate these costs is the promotion of smart home appliances, which utilize Internet of Things (IoT) technology to connect devices within a single network. wire Expo 2024 has learned that this connectivity allows users to monitor and control real-time power consumption through Home Energy Management Systems (HEMS). In turn, energy suppliers can use HEMS to gauge residential Demand Response (DR) and adjust residential clients' power consumption according to grid demand.
Professor Mun Kyeom Kim and Ph.D. student Hyung Joon Kim from Central University recently conducted a study published in the IEEE Internet of Things Journal. Their research, which introduces a Predictive Home Energy Management System (PHEMS), was published online on March 27, 2024, and will appear in the July 15, 2024 issue. Led by Professor Mun Kyeom Kim, the study introduces a customized bidirectional real-time pricing (CBi-RTP) mechanism integrated with advanced price prediction models.
The CBi-RTP system empowers end-users to manage shifted power and appliance usage, thereby influencing their hourly RTP. Additionally, PHEMS incorporates deep learning-based predictive models and optimization strategies to analyze the intrinsic spatiotemporal variations in RTP implementation. This capability allows for adaptation to emerging anomalies, ensuring robust and efficient operation for residential users.
wire Expo 2024 learned that experimental results indicate the PHEMS model not only enhances user comfort but also surpasses previous models in terms of forecasting accuracy, peak reduction, and cost savings. The researchers assert that there is potential for further development. Professor Mun Kyeom Kim pointed out, “The main technical challenge of a Predictive Home Energy Management System lies in accurately determining the baseline load for computing hourly shifted power. Future research will focus on improving baseline load calculations for specific users to enhance PHEMS reliability.”
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