TY - GEN
T1 - Wind Driven Optimization with Smart Home Battery for Power Scheduling Problem in Smart Home
AU - Makhadmeh, Sharif Naser
AU - Al-Betar, Mohammed Azmi
AU - Abasi, Ammar Kamal
AU - Awadallah, Mohammed A.
AU - Alkareem Alyasseri, Zaid Abdi
AU - Alomari, Osama Ahmad
AU - Doush, Iyad Abu
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The power scheduling problem in smart home (PSPSH) refers to schedule smart appliances at suitable times in accordance with pricing system(s). Smart appliances can be rearranged and scheduled by moving their operation times from one period to another. Such a process aims to decrease the electricity bill and the power demand at peak periods and improve user satisfaction. Different optimization approaches were proposed to address PSPSH, where metaheuristics are the most common methods. In this paper, wind-driven optimization (WDO) is adapted to handle PSPSH and optimize its objectives. Smart home battery (SHB) is modelled and used to improve the schedules by storing power at off-peak periods and using the stored power at peak periods. In the simulation results, the proposed approach proves its efficiency in reducing electricity bills and improving user satisfaction. In addition, WDO is compared with bacterial foraging optimization algorithm (BFOA) to evaluate and investigate its performance. WDO outperforms BFOA in optimizing PSPSH objectives.
AB - The power scheduling problem in smart home (PSPSH) refers to schedule smart appliances at suitable times in accordance with pricing system(s). Smart appliances can be rearranged and scheduled by moving their operation times from one period to another. Such a process aims to decrease the electricity bill and the power demand at peak periods and improve user satisfaction. Different optimization approaches were proposed to address PSPSH, where metaheuristics are the most common methods. In this paper, wind-driven optimization (WDO) is adapted to handle PSPSH and optimize its objectives. Smart home battery (SHB) is modelled and used to improve the schedules by storing power at off-peak periods and using the stored power at peak periods. In the simulation results, the proposed approach proves its efficiency in reducing electricity bills and improving user satisfaction. In addition, WDO is compared with bacterial foraging optimization algorithm (BFOA) to evaluate and investigate its performance. WDO outperforms BFOA in optimizing PSPSH objectives.
KW - Optimization
KW - Power Scheduling Problem in Smart Home
KW - Smart Home Battery
KW - Wind Driven Optimization
UR - http://www.scopus.com/inward/record.url?scp=85124011500&partnerID=8YFLogxK
U2 - 10.1109/PICICT53635.2021.00026
DO - 10.1109/PICICT53635.2021.00026
M3 - Conference contribution
AN - SCOPUS:85124011500
T3 - Proceedings - 2021 Palestinian International Conference on Information and Communication Technology, PICICT 2021
SP - 82
EP - 87
BT - Proceedings - 2021 Palestinian International Conference on Information and Communication Technology, PICICT 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 Palestinian International Conference on Information and Communication Technology, PICICT 2021
Y2 - 28 September 2021 through 29 September 2021
ER -