Conserving Energy in Buildings by Detecting Hotspots Through Clustering Approaches
Abstract
Energy consumption efficiency is increasingly recognized as a pivotal concept in conservation of natural resources and mitigate environmental degradation by reducing the reliance on fossil fuels. This paper addresses the critical role that buildings play in global energy consumption, as buildings consume more than 40% of total usage. By examining historical trends and patterns in energy usage this paper highlights the urgent need for effective strategies to enhance energy efficiency. In this work we employed advanced data analytics techniques to analyze terabytes of meter reading data collected from various buildings. By using clustering algorithms we are able to pinpoint hotspots areas within buildings that exhibit unusually high energy consumption. By identifying these hotspots targeted interventions are proposed through integrating energy-efficient technologies or behavioral modifications to reduce energy waste. This analysis provides actionable insights that can inform energy management practices and policy makers. The findings of this work not only under-score the significance of energy consumption efficiency but also suggests more about sustainable buildings.