인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract Customers of energy, both in residential and commercial structures, are now more interested in lowering their energy usage as an effect of the feed-in tariffs for renewable resources and the recent rise in electricity rates. The central control system and smart power Plug proposed in this study use the XBee communication protocol to manage energy use. Smart energy management systems are used to measure and optimize power use at the consumer premises level. The design and development of wireless smart Plugs that can assess several power characteristics and gather data on the real-time power use of individual consumer appliances is the main goal of this paper. An XBee transmitter and receiver node aids in the formation of the Consumer Area Network, which is created by the SEMS setup. The central node’s real-time data collection allows for the scheduling and prioritization of the appliances. Consumer appliance datasets may be created using the SEMS setup, and additional datasets can be utilized for load disaggregation. The configuration of the system allows for wireless data transfer from smart outlets to a central controller. The connected devices to the smart Plug are then turned on or off by the system using control instructions generated by the data analysis. According to test findings, the suggested smart Plug can assess the power consumption of wirelessly connected devices up to 18 meters away with accuracy and without compromising data. Based on a planned user program code, the central controller is capable of successfully controlling several Plugs. The proposed Smart Energy Management algorithm demonstrates that using smart Plugs as load controllers results in a decrease in energy consumption of 0.811 kW min (0.0134 kWh) with the right scheduling algorithm, the suggested smart Plug technology may, therefore, be used to its full potential in a smart energy management system. The data’s findings show how much better the proposed approach is than the standard ones in use now.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
