TY - JOUR
T1 - Design of neuro-fuzzy based intelligent inference algorithm for energy-management system with legacy device
AU - Choi, In Hwan
AU - Yoo, Sung Hyun
AU - Jung, Jun Ho
AU - Lim, Myo Taeg
AU - Oh, Jung Jun
AU - Song, Moon Kyou
AU - Ahn, Choon Ki
PY - 2015/5/1
Y1 - 2015/5/1
N2 - Recently, home energy management system (HEMS) for power consumption reduction has been widely used and studied. The HEMS performs electric power consumption control for the indoor electric device connected to the HEMS. However, a traditional HEMS is used for passive control method using some particular power saving devices. Disadvantages with this traditional HEMS is that these power saving devices should be newly installed to build HEMS environment instead of existing home appliances. Therefore, an HEMS, which performs with existing home appliances, is needed to prevent additional expenses due to the purchase of state-of-the-art devices. In this paper, an intelligent inference algorithm for EMS at home for non-power saving electronic equipment, called legacy devices, is proposed. The algorithm is based on the adaptive network fuzzy inference system (ANFIS) and has a subsystem that notifies retraining schedule to the ANFIS to increase the inference performance. This paper discusses the overview and the architecture of the system, especially in terms of the retraining schedule. In addition, the comparison results show that the proposed algorithm is more accurate than the classic ANFIS-based EMS system.
AB - Recently, home energy management system (HEMS) for power consumption reduction has been widely used and studied. The HEMS performs electric power consumption control for the indoor electric device connected to the HEMS. However, a traditional HEMS is used for passive control method using some particular power saving devices. Disadvantages with this traditional HEMS is that these power saving devices should be newly installed to build HEMS environment instead of existing home appliances. Therefore, an HEMS, which performs with existing home appliances, is needed to prevent additional expenses due to the purchase of state-of-the-art devices. In this paper, an intelligent inference algorithm for EMS at home for non-power saving electronic equipment, called legacy devices, is proposed. The algorithm is based on the adaptive network fuzzy inference system (ANFIS) and has a subsystem that notifies retraining schedule to the ANFIS to increase the inference performance. This paper discusses the overview and the architecture of the system, especially in terms of the retraining schedule. In addition, the comparison results show that the proposed algorithm is more accurate than the classic ANFIS-based EMS system.
KW - Adaptive network based fuzzy inference system (ANFIS)
KW - Home energy management system (HEMS)
KW - Legacy device
KW - Training schedule notification
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U2 - 10.5370/KIEE.2015.64.5.779
DO - 10.5370/KIEE.2015.64.5.779
M3 - Article
AN - SCOPUS:84930698057
VL - 64
SP - 779
EP - 785
JO - Transactions of the Korean Institute of Electrical Engineers
JF - Transactions of the Korean Institute of Electrical Engineers
SN - 1975-8359
IS - 5
ER -