Please use this identifier to cite or link to this item: http://repository.uinjkt.ac.id/dspace/handle/123456789/46153
Title: Estimation system of occupant behavior against the use of electricity using bayes method and decision tree algorithm
Authors: Almas Shabrina
Feri Fahrianto
Nenny Anggraini
Keywords: Research Subject Categories::TECHNOLOGY::Information technology::Systems engineering
Issue Date: 1-Nov-2017
Publisher: IEEE
Abstract: Based on research by the year 2013, entitled Kleiminger Occupancy Monitoring Using Household Electricity Meters stated that digital power meter suitable for use as a sensor for occupancy detection average accuracy of your detection can reach 80%. Besides the estimated occupancy with future electric current sensors can identify electricity consumption usage patterns to estimate future electricity consumption (Silva, 2011) and a model of a daily routine for energy efficiency (Abreu, 2012). Huang in 2016 stated that the current occupancy detection system has several drawbacks, namely: (1) Cost of implementation is quite expensive (2) user privacy, (3) Accuracy of detection, and (4) Intrusiveness. As a result, it is feasible to study on other potential approaches to address the deficiencies in the system and the occupancy estimation can also estimate the model's daily routine to be one reason for the policy-making authorities for energy efficiency. In this study, the authors make the system estimates the consumption behavior of the household electric loads with maximum hypothesis Bayes methods appropri probability (HMAP) and decision tree algorithm, using arduino microcontroller hardware and ethernet shield, with the value of the Positive Predictive Value (PPV) reached 64.5%.
metadata.dc.description.uri: https://ieeexplore.ieee.org/document/8280579/metrics
URI: http://repository.uinjkt.ac.id/dspace/handle/123456789/46153
ISBN: 978-1-5386-2986-4
Appears in Collections:Prosiding Workshop/Lokakarya/Seminar



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