Consumer engagement in residential demand response (DR) programmes has traditionally been low due to rigid electricity pricing models that fail to consider individual usage patterns. A new predictive home energy management system (PHEMS) aims to resolve this issue through a real-time pricing mechanism.
This system adjusts electricity prices based on each consumer’s energy consumption, thereby encouraging active participation in DR. Looking ahead, it holds promise for improving engagement and efficiency in residential energy management.
As the global population continues to rise, so do energy consumption and its associated environmental and economic costs. One effective method for managing these increasing costs is the promotion of smart home appliances, which use IoT technologies to connect devices within a unified network. This connectivity allows users to monitor and control their real-time power consumption through home energy management systems (HEMS). Energy providers can, in turn, use HEMS to gauge residential demand response and adjust power consumption according to grid demand.
Efforts to promote residential DR, such as offering financial incentives under the real-time pricing (RTP) model, have historically struggled to effect lasting behavioural change among consumers. This challenge arises from unidirectional electricity pricing mechanisms, which reduce consumer engagement in residential DR activities.
To tackle these issues, Professor Mun Kyeom Kim and doctoral candidate Hyung Joon Kim from Chung-Ang University have conducted a study, which proposes a predictive home energy management system (PHEMS), published in the IEEE Internet of Things Journal.
Led by Professor Mun Kyeom Kim, the study introduces a customised bidirectional real-time pricing (CBi-RTP) mechanism integrated with an advanced price forecasting model. These innovations offer compelling incentives for consumers to actively participate in residential DR initiatives.
The CBi-RTP system empowers end-users by enabling them to influence their hourly RTPs through managing their transferred power and household appliance usage. Additionally, PHEMS incorporates a deep-learning-based forecasting model and optimisation strategy to analyse spatial-temporal variations inherent in RTP implementations. This capability ensures robust and cost-effective operation for residential users by adapting to irregularities as they occur.
Experimental results from the study show that the PHEMS model not only enhances user comfort but also surpasses previous models in forecasting accuracy, peak reduction, and cost savings. Despite its superior performance, the researchers acknowledge the potential for further improvement.
Professor Mun Kyeom Kim said: “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.”
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