Authors:
Muhammad Arbab Arshad
;
Syed Hasnain
and
Naveed Arshad
Affiliation:
Department of Computer science, Lahore University of Management Sciences, Sector U, DHA, Lahore and Pakistan
Keyword(s):
Demand Side Management, Hierarchical Clustering, Quota Allocation, Machine Learning.
Related
Ontology
Subjects/Areas/Topics:
Energy and Economy
;
Energy-Aware Systems and Technologies
;
Greener Systems Planning and Design
;
Load Balancing in Smart Grids
;
Renewable Energy Resources
;
Smart Grids
Abstract:
Shortfall can occur at irregular times in an electric grid that has high a concentration of intermittent renewable energy sources. Many methods are being studied, proposed and used to change the demand in order to match the supply with the most common being Load Curtailment. New DSM techniques have evolved as a result of advancements in AMI technologies. The goal is to minimize the difference between supply and demand at the time of shortfall. Our proposed algorithm selects consumers and limits their energy consumption by profiling the commercial sites based on their historical consumption behaviour. Then, to save the required amount of energy, the sites with peak consumption levels with respect to their own daily usage are targeted. Thus, it harnesses the maximum potential of electricity deduction from a site while minimizing its effects on the residents.