TY - GEN
T1 - Construction of energy measuring system in a university for cluster Energy Management System
AU - Kamiyoshi, Yuto
AU - Nakabe, Tomohisa
AU - Mine, Gouki
AU - Nishi, Hiroaki
PY - 2010/12/1
Y1 - 2010/12/1
N2 - To deal with environmental problems such as global warming and depletion of energy resources, energy conservation measures in the civilian sector and the spread of renewable energy are required strongly. As a next-generation energy system to achieve them, Cluster Energy Management System (CEMS) is beginning to be studied in Japan. CEMS is the energy management system, which cooperates with various dispersed power sources and buildings using information technology. CEMS monitors and adjusts energy supply and demand in real-time to form the best combination of them, and achieves high efficiency energy usage. In this study, the electricity energy measurement system using KNIVES (Keio University Network oriented Intelligent and Versatile Energy saving System) was build in Keio University Shonan Fujisawa Campus (SFC). In addition, it is declared that this system can predict electric power demand based on the past data obtained from itself.
AB - To deal with environmental problems such as global warming and depletion of energy resources, energy conservation measures in the civilian sector and the spread of renewable energy are required strongly. As a next-generation energy system to achieve them, Cluster Energy Management System (CEMS) is beginning to be studied in Japan. CEMS is the energy management system, which cooperates with various dispersed power sources and buildings using information technology. CEMS monitors and adjusts energy supply and demand in real-time to form the best combination of them, and achieves high efficiency energy usage. In this study, the electricity energy measurement system using KNIVES (Keio University Network oriented Intelligent and Versatile Energy saving System) was build in Keio University Shonan Fujisawa Campus (SFC). In addition, it is declared that this system can predict electric power demand based on the past data obtained from itself.
UR - http://www.scopus.com/inward/record.url?scp=78751560359&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78751560359&partnerID=8YFLogxK
U2 - 10.1109/IECON.2010.5675410
DO - 10.1109/IECON.2010.5675410
M3 - Conference contribution
AN - SCOPUS:78751560359
SN - 9781424452262
T3 - IECON Proceedings (Industrial Electronics Conference)
SP - 2423
EP - 2429
BT - Proceedings - IECON 2010, 36th Annual Conference of the IEEE Industrial Electronics Society
T2 - 36th Annual Conference of the IEEE Industrial Electronics Society, IECON 2010
Y2 - 7 November 2010 through 10 November 2010
ER -
