A particle swarm optimization and branch and bound based algorithm for economical smart home scheduling

Yangyizhou Wang; Cong Hao; Takeshi Yoshimura

doi:10.1109/MWSCAS.2017.8052898

A particle swarm optimization and branch and bound based algorithm for economical smart home scheduling

Yangyizhou Wang, Cong Hao, Takeshi Yoshimura

Graduate School of Information, Production and Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

Smart home scheduling, as one of the most effective techniques in Demand Side Management (DSM), is now attracting more and more research interests in the recent years. In this paper we propose an efficient scheduling algorithm for smart home resident to reduce the monetary cost of the electricity. The proposed algorithm is an improved particle swarm optimization(PSO) algorithm that can schedule the smart appliances under discrete power level and quadratic pricing model. Branch and bound method is adopted to map real number values to discrete power level values. Simulation results shows that our method exceeds the previous methods both in total monetary cost and execution time.

Original language	English
Title of host publication	2017 IEEE 60th International Midwest Symposium on Circuits and Systems, MWSCAS 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	213-216
Number of pages	4
Volume	2017-August
ISBN (Electronic)	9781509063895
DOIs	https://doi.org/10.1109/MWSCAS.2017.8052898
Publication status	Published - 2017 Sept 27
Event	60th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2017 - Boston, United States Duration: 2017 Aug 6 → 2017 Aug 9

Other

Other	60th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2017
Country/Territory	United States
City	Boston
Period	17/8/6 → 17/8/9

Keywords

Branch and Bound
Particle Swarm Optimization
Smart Home Scheduling

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Electrical and Electronic Engineering

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10.1109/MWSCAS.2017.8052898

Cite this

Wang, Y., Hao, C., & Yoshimura, T. (2017). A particle swarm optimization and branch and bound based algorithm for economical smart home scheduling. In 2017 IEEE 60th International Midwest Symposium on Circuits and Systems, MWSCAS 2017 (Vol. 2017-August, pp. 213-216). Article 8052898 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MWSCAS.2017.8052898

A particle swarm optimization and branch and bound based algorithm for economical smart home scheduling. / Wang, Yangyizhou; Hao, Cong; Yoshimura, Takeshi.
2017 IEEE 60th International Midwest Symposium on Circuits and Systems, MWSCAS 2017. Vol. 2017-August Institute of Electrical and Electronics Engineers Inc., 2017. p. 213-216 8052898.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Wang, Y, Hao, C & Yoshimura, T 2017, A particle swarm optimization and branch and bound based algorithm for economical smart home scheduling. in 2017 IEEE 60th International Midwest Symposium on Circuits and Systems, MWSCAS 2017. vol. 2017-August, 8052898, Institute of Electrical and Electronics Engineers Inc., pp. 213-216, 60th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2017, Boston, United States, 17/8/6. https://doi.org/10.1109/MWSCAS.2017.8052898

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abstract = "Smart home scheduling, as one of the most effective techniques in Demand Side Management (DSM), is now attracting more and more research interests in the recent years. In this paper we propose an efficient scheduling algorithm for smart home resident to reduce the monetary cost of the electricity. The proposed algorithm is an improved particle swarm optimization(PSO) algorithm that can schedule the smart appliances under discrete power level and quadratic pricing model. Branch and bound method is adopted to map real number values to discrete power level values. Simulation results shows that our method exceeds the previous methods both in total monetary cost and execution time.",

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N2 - Smart home scheduling, as one of the most effective techniques in Demand Side Management (DSM), is now attracting more and more research interests in the recent years. In this paper we propose an efficient scheduling algorithm for smart home resident to reduce the monetary cost of the electricity. The proposed algorithm is an improved particle swarm optimization(PSO) algorithm that can schedule the smart appliances under discrete power level and quadratic pricing model. Branch and bound method is adopted to map real number values to discrete power level values. Simulation results shows that our method exceeds the previous methods both in total monetary cost and execution time.

AB - Smart home scheduling, as one of the most effective techniques in Demand Side Management (DSM), is now attracting more and more research interests in the recent years. In this paper we propose an efficient scheduling algorithm for smart home resident to reduce the monetary cost of the electricity. The proposed algorithm is an improved particle swarm optimization(PSO) algorithm that can schedule the smart appliances under discrete power level and quadratic pricing model. Branch and bound method is adopted to map real number values to discrete power level values. Simulation results shows that our method exceeds the previous methods both in total monetary cost and execution time.

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A particle swarm optimization and branch and bound based algorithm for economical smart home scheduling

Abstract

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Keywords

ASJC Scopus subject areas

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