Resolving Energy Consumption Issues and Spectrum Allocation for Future Broadband Networks

Abstract

With the fast and rapid pace of developments in wireless technology, energy consumption has become a problem of great significance for future networks. Over the past few years, several energy monitoring policies have been initiated to promote energy efficiency emphasizing the active and important role of consumers to realize this goal. This study resolves the energy consumption issues of broadband networks and determines the energy usage associated with high spectrum allocation in future broadband networks by leveraging clustering from the data mining domain. For analyzing the overall patterns of energy consumption in broadband networks, this study segments the broadband networks based on the similarities of their electrical load profiles and the proportion of energy usage per hour (%) as a common framework and divides the users into different groups. The prime objective for the segmentation is to provide personalized recommendations to each group to reduce the energy consumption and associated costs thereby fostering energy efficiency measures and improving consumer engagement. The segmentation is obtained by an iterative process based on computational clusters calculation which is finalized by a post clustering analysis. Post clustering analysis involves visualization and statistical data mining techniques to analyze the energy consumption patterns and reallocating to a more appropriate group. The K-Means clustering technique is utilized for this purpose which provides the best prediction accuracy of 98.46% for energy load profiles at the spectrum of 100GHz. The energy consumption segmentation of the consumers provides knowledge and a better understanding of the consumer for optimizing energy consumption for future broadband.