NILM- based system for energy monitoring of compound houses in Ghana

dc.contributor.authorSereboo, Nana Akua Agyemang
dc.date.accessioned2021-01-27T12:03:04Z
dc.date.available2021-01-27T12:03:04Z
dc.date.issued2020-05
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 2020en_US
dc.description.abstractCompound housing is a housing arrangement that stemmed from cultural family living settings. Over the years, it has evolved to denote a group of different households that share a number of utilities including electricity. Electricity bills and payments are a concern in such living units due to the inability to split bills fairly. Previous work done to solve this are either expensive, or do not take into consideration the complexity of electricity connections in such houses. This thesis aims at verifying if the Feedforward Artificial Neural Network (ANN) can be used to split bills by implementing a Non-Intrusive Load Monitoring System for Compound Houses in Ghana. The proposed algorithm’s performance is compared to Hidden Markov Model (HMM) and its variants. It was discovered that the Feedforward ANN had higher overall performance than the HMM and its variants, and is efficient for splitting electricity bills in compound houses.en_US
dc.description.sponsorshipAshesi Universityen_US
dc.identifier.urihttp://hdl.handle.net/20.500.11988/604
dc.language.isoen_USen_US
dc.subjectcompound housesen_US
dc.subjectNon-Intrusive Load Monitoring (NILM)en_US
dc.subjectelectricity consumptionen_US
dc.titleNILM- based system for energy monitoring of compound houses in Ghanaen_US
dc.typeSenior projecten_US

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