Z
Zafar A. Khan
Researcher at Mirpur University of Science and Technology
Publications - 82
Citations - 1040
Zafar A. Khan is an academic researcher from Mirpur University of Science and Technology. The author has contributed to research in topics: Computer science & Smart meter. The author has an hindex of 11, co-authored 48 publications receiving 629 citations. Previous affiliations of Zafar A. Khan include University of Birmingham & Dalhousie University.
Papers
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Journal ArticleDOI
Load forecasting, dynamic pricing and DSM in smart grid: A review
TL;DR: A comprehensive and comparative review of the LF and dynamic pricing schemes in smart grid environment, including Real Time Pricing (RTP), Time of Use (ToU) and Critical Peak Pricing (CPP) are presented.
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A Critical Review of Sustainable Energy Policies for the Promotion of Renewable Energy Sources
TL;DR: A review on sustainable energy policy for promotion of renewable energy by introducing the development history of energy policy in five countries, namely, United States, Germany, United Kingdom, Denmark and China is presented in this article.
Proceedings ArticleDOI
Minimizing Electricity Theft Using Smart Meters in AMI
TL;DR: Smart meter can be the best option to minimize electricity theft, because of its high security, best efficiency, and excellent resistance towards many of theft ideas in electromechanical meters.
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A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons
TL;DR: In this article, a review of different electricity demand forecasting methodologies is provided in the context of a group of low and middle income countries and a comparative analysis of the demand determinants in these countries indicates a frequent use of determinants like the population, GDP, weather, and load data over different time horizons.
Journal ArticleDOI
An Incentive-based Optimal Energy Consumption Scheduling Algorithm for Residential Users
TL;DR: This paper proposes an energy efficient optimization model based on Binary Particle Swarm Optimization (BPSO) for residential electricity consumers that efficiently shifts the appliances operation time from high peak to low peak hours and leads to significant electricity bill saving.