Paper Type
Research-in-Progress Paper
Abstract
The world currently faces an energy problem which is rooted in the inefficient use of energy resources. As a result, economies around the world are grappling to devise ways and means of solving this problem. This study postulates that the solution should consider reducing and managing consumption and use in order to be successful. As a result, this study explores the impact the implementation of an Energy Management System has on energy consumption and how it contributes towards sustainable environmental practices. The study is built upon the Deign Science theory and is conducted within the boundaries of Energy Informatics. It will employ an experimental design, using Multiple Linear Regression to derive a model that predicts energy consumption. The study seeks also to derive optimized setting of the system through the use of Response Surface Methodology (RSM). The viability of the study will then be assessed by conducting a Cost-Benefit Analysis.
Recommended Citation
Hylton, Oliver D.; Tennant, Vanesa M.; and Golding, Paul A., "The Impact of a ZigBee enabled Energy Management System on Electricity Consumption" (2013). AMCIS 2013 Proceedings. 1.
https://aisel.aisnet.org/amcis2013/GreenIS/RoundTablePresentations/1
The Impact of a ZigBee enabled Energy Management System on Electricity Consumption
The world currently faces an energy problem which is rooted in the inefficient use of energy resources. As a result, economies around the world are grappling to devise ways and means of solving this problem. This study postulates that the solution should consider reducing and managing consumption and use in order to be successful. As a result, this study explores the impact the implementation of an Energy Management System has on energy consumption and how it contributes towards sustainable environmental practices. The study is built upon the Deign Science theory and is conducted within the boundaries of Energy Informatics. It will employ an experimental design, using Multiple Linear Regression to derive a model that predicts energy consumption. The study seeks also to derive optimized setting of the system through the use of Response Surface Methodology (RSM). The viability of the study will then be assessed by conducting a Cost-Benefit Analysis.