Next Article in Journal
Realizing the Intended Nationally Determined Contribution: The Role of Renewable Energies in Vietnam
Next Article in Special Issue
City Carbon Footprint Networks
Previous Article in Journal
Environmental and Economic Performance of an Li-Ion Battery Pack: A Multiregional Input-Output Approach
Previous Article in Special Issue
Solar Energy as a Form Giver for Future Cities
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
Energies 2016, 9(8), 593; doi:10.3390/en9080593

Energy Optimization in Smart Homes Using Customer Preference and Dynamic Pricing

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
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)

Abstract

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. View Full-Text
Keywords: demand response; peak load avoidance; energy optimization; time of use pricing; binary knapsack; smart grid demand response; peak load avoidance; energy optimization; time of use pricing; binary knapsack; smart grid
Figures

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. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top