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dc.contributor.advisor Premarathne, SC
dc.contributor.author Abeysinghe, GAGK
dc.date.accessioned 2019-04-05T02:58:39Z
dc.date.available 2019-04-05T02:58:39Z
dc.identifier.uri http://dl.lib.mrt.ac.lk/handle/123/14154
dc.description.abstract This thesis presents a new decision support approach to energy control and monitoring system of domestic appliances. In the modem world, people are rapidly turning to technology as a fast and cost-effective way of improving quality of daily living. This primary goal is to address the needs of the end user by employing networked low-power sensors sensitive to the environment, so it can be altered to their liking. The proposed system consists of following steps: energy control and monitor, data analysis and data predictions. This research will present the design and implementation of a practical and simple smart home system, which can be further extended. The system is based on: group of sensors, Arduino UNO with unit and WIFI as a communication protocol. These devices can be easily controlled via user-friendly interfaces via web applications. The web applications are available for Consumers and Administrative Staff. Those web applications represent to the users are statistical data by using Google charts. Data analysis part has done using Data Mining techniques such as clustering and regression analysis. Sample data has been generated by using Test Data Generation Tool is DTM tool. Clustering and Regression Analysis has been done by using Rapid Miner Tool. Data prediction was done by using Regression Analysis technique. The main advantage of the proposed system is that it is a sensible, secure and easily configurable system that provides end users with a cost-effective energy consumption solution. en_US
dc.language.iso en en_US
dc.title Decision support approach to domestic energy monitoring system en_US
dc.type Thesis-Abstract en_US
dc.identifier.faculty IT en_US
dc.identifier.degree Master of Science in information Technology en_US
dc.identifier.department Department of Information Technology en_US
dc.date.accept 2017-05
dc.identifier.accno TH3383 en_US


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