Energy Optimization in Smart Homes Using Customer Preference and Dynamic Pricing
by
Muhammad Babar Rasheed 1, Nadeem Javaid 1,*
, Ashfaq Ahmad 1, Mohsin Jamil 2, Zahoor Ali Khan 3, Umar Qasim 4 and Nabil Alrajeh 5
Muhammad Babar Rasheed 1, Nadeem Javaid 1,*, Ashfaq Ahmad 1, Mohsin Jamil 2, Zahoor Ali Khan 3, Umar Qasim 4 and Nabil Alrajeh 5
1
COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
2
School of Mechanical & Manufacturing Engineering, National University of Science and Technology, Islamabad 44000, Pakistan
3
Internetworking Program, Faculty of Engineering, Dalhousie University, Halifax, NS B3J 4R2, Canada
4
Cameron Library, University of Alberta, Edmonton, AB T6G 2J8, Canada
5
Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 11633, Saudi Arabia
*
Author to whom correspondence should be addressed.
Academic Editors: Jukka Heinonen and Chi-Ming Lai
Energies 2016, 9(8), 593; https://doi.org/10.3390/en9080593
Received: 19 November 2015 / Revised: 12 July 2016 / Accepted: 18 July 2016 / Published: 27 July 2016
(This article belongs to the Special Issue Energy Efficient City)
In this paper, we present an energy optimization technique to schedule three types of household appliances (user dependent, interactive schedulable and unschedulable) in response to the dynamic behaviours of customers, electricity prices and weather conditions. Our optimization technique schedules household appliances in real time to optimally control their energy consumption, such that the electricity bills of end users are reduced while not compromising on user comfort. More specifically, we use the binary multiple knapsack problem formulation technique to design an objective function, which is solved via the constraint optimization technique. Simulation results show that average aggregated energy savings with and without considering the human presence control system are 11.77% and 5.91%, respectively.
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Keywords:
demand response; peak load avoidance; energy optimization; time of use pricing; binary knapsack; smart grid
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Rasheed, M.B.; Javaid, N.; Ahmad, A.; Jamil, M.; Khan, Z.A.; Qasim, U.; Alrajeh, N. Energy Optimization in Smart Homes Using Customer Preference and Dynamic Pricing. Energies 2016, 9, 593.
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