Zigbee Wireless Networking
Design of a Reliable Communication System for Grid-Style Traffic Light Networks
RTAS '10 Proceedings of the 2010 16th IEEE Real-Time and Embedded Technology and Applications Symposium
Design of a power scheduler based on the heuristic for preemptive appliances
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
Design of an energy consumption scheduler based on genetic algorithms in the smart grid
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
Reservation-based charging service for electric vehicles
ICA3PP'11 Proceedings of the 11th international conference on Algorithms and architectures for parallel processing - Volume Part II
Fast scheduling policy for electric vehicle charging stations in smart transportation
Proceedings of the 2011 ACM Symposium on Research in Applied Computation
Measurement of global peak load reduction by power consumption scheduling for smart places
ACA'12 Proceedings of the 11th international conference on Applications of Electrical and Computer Engineering
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This paper presents a design and evaluates the performance of a power consumption scheduler in smart grid homes, aiming at reducing the peak load in individual homes as well as in the system-wide power transmission network. Following the task model consist of actuation time, operation length, deadline, and a consumption profile, the scheduler copies or maps the profile according to the task type, which can be either preemptive or nonpreemptive. The proposed scheme expands the search space recursively to traverse all the feasible allocations for a task set. A pilot implementation of this scheduling method reduces the peak load by up to 23.1% for the given task set. The execution time greatly depends on the search space of a preemptive task, as its time complexity is estimated to be O (MNnp · (MM/2)Np), where M, Nnp, and Np are the number of time slots, preemptive tasks, and nonpreemptive tasks, respectively. However, it can not only be reduced almost to 2% but also made stable with a basic constraint processing mechanism which prunes a search branch when the partial peak value already exceeds the current best.