TY - GEN
T1 - Fuzzy logic and neural network approximation to indoor comfort and energy optimization
AU - Ari, S.
AU - Khalifa, H. E.
AU - Dannenhoffer, J. F.
AU - Wilcoxen, P.
AU - Isik, C.
PY - 2006
Y1 - 2006
N2 - Research about indoor environmental satisfaction has indicated that allowing building occupants to adjust their local environment to their preferences increases thermal satisfaction and human performance at the workplace. However, such systems have been considered as a reason of possible increase in energy consumption of environmental control systems. In our previous study [1], we minimized the energy consumption of distributed environmental control systems without increasing occupant thermal dissatisfaction using gradient-based optimization. We then approximated the optimal models using fuzzy systems based on the nearest neighbors. Those fuzzy models were specific to different outside temperatures. This required a considerable storage space to keep all the fuzzy rules of each outside temperature. In this study, we generated a fuzzy system and a neural network system, which are generated for any outside temperature. Our results show these two models approximate the results of gradient-based optimization in a practically feasible fashion.
AB - Research about indoor environmental satisfaction has indicated that allowing building occupants to adjust their local environment to their preferences increases thermal satisfaction and human performance at the workplace. However, such systems have been considered as a reason of possible increase in energy consumption of environmental control systems. In our previous study [1], we minimized the energy consumption of distributed environmental control systems without increasing occupant thermal dissatisfaction using gradient-based optimization. We then approximated the optimal models using fuzzy systems based on the nearest neighbors. Those fuzzy models were specific to different outside temperatures. This required a considerable storage space to keep all the fuzzy rules of each outside temperature. In this study, we generated a fuzzy system and a neural network system, which are generated for any outside temperature. Our results show these two models approximate the results of gradient-based optimization in a practically feasible fashion.
UR - http://www.scopus.com/inward/record.url?scp=46749092036&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=46749092036&partnerID=8YFLogxK
U2 - 10.1109/NAFIPS.2006.365493
DO - 10.1109/NAFIPS.2006.365493
M3 - Conference contribution
AN - SCOPUS:46749092036
SN - 1424403634
SN - 9781424403639
T3 - Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS
SP - 692
EP - 695
BT - Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS
T2 - NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society
Y2 - 3 June 2006 through 6 June 2006
ER -