TY - GEN
T1 - Smart grid ready BEMS adopting model-based HVAC control for energy saving
AU - Murayama, D.
AU - Mitsumoto, K.
AU - Takagi, Y.
AU - Iino, Y.
AU - Yamamori, S.
PY - 2012/11/1
Y1 - 2012/11/1
N2 - The newly developed building energy management system (BEMS) is presented. The main feature of the BEMS is the following 2 technologies. (1) The new air-conditioning control technology for the energy saving of buildings. The method is mainly focused on the compatibility of energy savings and comfort. The energy saving is achieved through the twin coil air handling unit that controls room humidity and temperature independently without energy loss and by the optimal operation of HVAC (Heating, Ventilating and air-conditioning) system, manipulating the supplying airflow temperature to the rooms, room temperature and the humidity. The comfort is kept by the index (PMV: Predicted Mean Vote) that is calculated with room temperature, humidity, radiation temperature, wind velocity and so on. In order to avoid large-scale nonlinear programming for optimal conditions, the driving function is introduced. The effectiveness of the HVAC control technology is proved through a building HVAC data and the simulations using the data. (2) Automated demand responding mechanism. The mechanism has function that predicts how much electricity demand is trimmed for a given reward. It also has ability to communicate with community energy management server for finding appropriate balances between electricity demand and generations.
AB - The newly developed building energy management system (BEMS) is presented. The main feature of the BEMS is the following 2 technologies. (1) The new air-conditioning control technology for the energy saving of buildings. The method is mainly focused on the compatibility of energy savings and comfort. The energy saving is achieved through the twin coil air handling unit that controls room humidity and temperature independently without energy loss and by the optimal operation of HVAC (Heating, Ventilating and air-conditioning) system, manipulating the supplying airflow temperature to the rooms, room temperature and the humidity. The comfort is kept by the index (PMV: Predicted Mean Vote) that is calculated with room temperature, humidity, radiation temperature, wind velocity and so on. In order to avoid large-scale nonlinear programming for optimal conditions, the driving function is introduced. The effectiveness of the HVAC control technology is proved through a building HVAC data and the simulations using the data. (2) Automated demand responding mechanism. The mechanism has function that predicts how much electricity demand is trimmed for a given reward. It also has ability to communicate with community energy management server for finding appropriate balances between electricity demand and generations.
KW - air-conditioning
KW - buildings
KW - comfort
KW - control
KW - demand response margins
KW - demand side energy management
KW - energy saving
KW - optimization
UR - http://www.scopus.com/inward/record.url?scp=84867940932&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84867940932&partnerID=8YFLogxK
U2 - 10.1109/TDC.2012.6281521
DO - 10.1109/TDC.2012.6281521
M3 - Conference contribution
AN - SCOPUS:84867940932
SN - 9781467319348
T3 - Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference
BT - 2012 IEEE PES Transmission and Distribution Conference and Exposition, T and D 2012
T2 - 2012 IEEE PES Transmission and Distribution Conference and Exposition, T and D 2012
Y2 - 7 May 2012 through 10 May 2012
ER -