Electricity bill calculation for compound houses in Ghana using Artificial Neural Network (ANN)

dc.contributor.authorNkansah, Joel Anaafi Ampomah
dc.descriptionCapstone Project submitted to the Department of Engineering, Ashesi University in partial fulfillment of the requirements for the award of Bachelor of Science degree in Electrical and Electronic Engineering, May 2021
dc.description.abstractIn Ghana, people live in a housing arrangement where members (tenants) of the household share basic facilities such as washrooms, kitchens and electricity meter - this is called a compound house. Over the years, there have been several complaints regarding these shared facilities but one remains paramount and is the lectric meters. Tenants mostly have concerns about how electricity bills are shared amongst themselves. It is assumed that tenants may not consume electric power as much as others do and as a result conclude bills are not shared fairly. Previous research has been done to verify the ability to spolit bills by implementing the Non-Intrusive Load monitoring system with focus on identifying different appliances used by different tenants. However, this did not take into consideration similar appliances used by different tenants. This paper investigates the possibility of distinguisihing between similar appliances used by different teanants. It was discovered that the Time Series Classification Algorithm had a higher overall performance when compared to the Multilayer Perceptron (MLP) henece is efficient for distinguising between similar applicances used by tenants.
dc.description.sponsorshipAshesi University
dc.subjectnon-intrusive load monitoringen
dc.titleElectricity bill calculation for compound houses in Ghana using Artificial Neural Network (ANN)en
dc.typeCapstone projecten
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