In order to improve the efficiency of heat energy in apartment complexes that receive heat energy through district heating, heat loss costs must be minimized. To achieve this, a method for collecting heat energy information is needed. Information collection methods for power data have become common with the spread of AMI, but for heat energy data, there is still a lack of technology for systematic information collection and processing methods. To this end, this paper presents Energy BigData platform for residential heat information.

We are developing AI composite sensor technology enabling enhanced function and performance in detection and provision of energy consumption and energy influence factor. Based on SW sensors, we maximized the reliability and management efficiency of energy management. Energy consumption and energy impact factors were detected and provided through the acquisition of various new sensing information and AI convergence of learning and reasoning.

We are developing energy ICT technology that aligns with government policies such as Energy 3020, Green New Deal, and carbon neutrality promotion strategy, and ETRI AI's implementation strategy to build an 'x+AI' innovation platform to realize Korea's intelligence. In particular, R&D was promoted to drive the spread of reliable AI utilization, focusing on AI utilization technology in the public sector of the energy industry. Therefore, in this paper, we would like to introduce the Energy Maestro–Transactive Energy Mechanism technology.

Smart energy management is the development of smart energy platform technology to maximize energy efficiency through energy information collection, energy. The created device-to-device correlation is ultimately associated with energy context information. Energy has a lift cycle of generation, consumption and storage. The energy device's operational goal is the participatory behavior of these energy state cycles. Energy management services make decisions about where to transition from the current situation to the next in this energy cycle. When the energy state transition is determined, the behavior of each device is determined by the correlation of the devices. Effective management can be achieved without wasting energy through consistent behavior of related devices that match the energy situation cycle. This paper generated energy device relation map through information of 300 generations constructed by test-bed and provides the result of energy management service using it.